Method and device for carrying out intelligent travelled distance estimation due to vehicle configuration change

Abstract

A system includes a detection module, an estimation module, and a processor operable to detect a vehicle configuration change via the detection module; and estimating, via the estimation module, a new range for the vehicle based on a change in configuration of the vehicle, where the change in configuration includes adding an item to the vehicle.

Description

Technical Field

[0001] The present disclosure relates to vehicle range estimation. More specifically, the present disclosure relates to systems and methods for detecting vehicle configuration changes and estimating range based on the vehicle configuration changes. Background Art

[0002] Today, vehicle range is based on vehicle specifications and the total amount of fuel or charge available. The problem is that when owners add extra items to their vehicles, whether the vehicle is electric or fuel-powered, it becomes difficult to determine vehicle range. For example, when owners add a trailer and a boat, the vehicle system cannot determine the impact of these additional weights. This can lead to inaccurate range estimates, leaving drivers in trouble.

[0003] Therefore, when estimating driving range, in addition to considering the fuel for a gas vehicle or the amount of electricity for an electric vehicle, additional items need to be considered. Summary of the invention

[0004] The following summary of the invention provides a basic understanding of one or more embodiments described herein. The summary of the invention is not intended to identify key or important elements, nor is it intended to delimit any scope of different embodiments and / or any scope of the claims. The sole purpose of the summary of the invention is to present some concepts in a simplified form as a prelude to a more detailed description herein.

[0005] According to one embodiment, the system includes: a detection module; an estimation module; a communication module; and a processor; wherein the processor is operable to detect a change in vehicle configuration due to the addition of an item via the detection module; establish a connection between the item and the vehicle via the communication module; receive one or more characteristics of the item via the communication module; and estimate a new mileage of the vehicle based on the one or more characteristics of the item via the estimation module.

[0006] According to one embodiment, the method includes: detecting a change in vehicle configuration due to the addition of an item via a detection module; establishing a connection between the item and the vehicle via a communication module; receiving one or more characteristics of the item via the communication module; and estimating a new mileage of the vehicle based on the one or more characteristics of the item via an estimation module.

[0007] According to one embodiment, a non-transitory computer-readable medium is provided on which instructions are stored, and the instructions can be executed by a computer system to perform operations including the following: detecting a change in vehicle configuration due to the addition of an item via a detection module; establishing a connection between the item and the vehicle via a communication module; receiving one or more characteristics of the item via the communication module; and estimating a new mileage of the vehicle based on the one or more characteristics of the item via an estimation module. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] These and other aspects of the present invention will now be described in more detail with reference to the accompanying drawings showing exemplary embodiments of the invention, in which:

[0009] Figure 1 is an illustration of an example autonomous vehicle with various sensors, actuators, and systems according to one embodiment.

[0010] Figure 2 A block diagram of electronic components / assemblies of a vehicle is shown according to one embodiment.

[0011] Figure 3 A block diagram of a system and components thereof for detecting vehicle configuration changes and estimating driving range according to an embodiment is shown.

[0012] Figure 4A A system for vehicle configuration detection module and vehicle parameter estimation according to one embodiment is shown.

[0013] Figure 4B Retrieving details of an item or accessory from the item itself according to one embodiment is shown.

[0014] Figure 4C Retrieving details of an item or accessory from a cloud network according to one embodiment is shown.

[0015] Figure 4D Detection of an attachment or trailer using a communication module is shown according to one embodiment.

[0016] Figure 4E Detection of an accessory or trailer using wired communications is shown according to one embodiment.

[0017] Figure 4F Detecting an accessory or trailer using wireless communications is shown according to one embodiment.

[0018] Figure 5A Inputs and outputs to a range estimation module are shown according to one embodiment.

[0019] Figure 5B An example communication message format according to one embodiment is shown.

[0020] Figure 5C An example message exchange between a trailer and a vehicle is shown according to one embodiment.

[0021] Figure 5D An example message displayed on an infotainment system of a vehicle is shown according to one embodiment.

[0022] Fig. 6A The structure of a neural network / machine learning model with a feedback loop according to one embodiment is shown.

[0023] Figure 6B The structure of a neural network / machine learning model with reinforcement learning function according to one embodiment is shown.

[0024] Figure 6C An example block diagram for predicting mileage using a machine learning model according to one embodiment is shown.

[0025] Fig.6D An example flow chart for continuous monitoring and recommending actions using a machine learning model is shown according to one embodiment.

[0026] Fig. 7A A block diagram of a method for estimating driving range based on vehicle configuration changes according to one embodiment is shown.

[0027] Figure 7B A block diagram of a system for estimating driving range based on vehicle configuration changes is shown according to one embodiment.

[0028] Figure 7C A block diagram of a method stored on a non-transitory computer medium for estimating driving range based on vehicle configuration changes is shown according to one embodiment.

[0029] Fig. 8A A block diagram of a method for detecting an item and estimating mileage via a communication module according to one embodiment is shown.

[0030] Figure 8B A block diagram of a system for detecting items and estimating mileage via a communication module is shown according to one embodiment.

[0031] Fig. 9A A block diagram of a method for estimating driving range based on planned or future vehicle configuration changes according to one embodiment is shown.

[0032] Fig. 9B A block diagram of a system for estimating driving range based on planned or future vehicle configuration changes is shown according to one embodiment.

[0033] Fig. 9C A block diagram of a method stored on a non-transitory computer medium for estimating driving range based on planned or future vehicle configuration changes is shown according to one embodiment.

[0034] Fig. 10A A block diagram of a method for recommending adjustments for increasing vehicle mileage is shown according to one embodiment.

[0035] Fig. 10B A block diagram of a system for recommending adjustments for increasing vehicle mileage is shown according to one embodiment.

[0036] Fig. 10C A block diagram for recommending adjustments for increasing vehicle mileage is shown according to one embodiment.

[0037] Fig.11A A block diagram of a network security module according to one embodiment is shown.

[0038] Fig. 11B A flow chart showing data security protection through a network security module.

[0039] Fig. 11C A flow chart showing data security protection through a network security module. DETAILED DESCRIPTION

[0040] For simplicity and clarity, the general construction methods are illustrated in the drawings. To avoid unnecessarily obscuring the present disclosure, descriptions and details of well-known features and techniques may be omitted from the description and drawings. The dimensions of some elements relative to other elements are exaggerated in the drawings to help better understand the embodiments of the present disclosure. The same reference numerals in different drawings represent the same elements.

[0041] Although the detailed description herein contains many specific details for purposes of illustration, one of ordinary skill in the art will appreciate that many variations and alterations to those details are incorporated herein.

[0042] Therefore, the embodiments herein are not intended to be limiting and do not impose limitations on any claims. The terms used herein are only intended to describe specific embodiments and are not intended to be limiting. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those of ordinary skill in the art to which the present disclosure belongs.

[0043] As used herein, the articles "a" and "an" refer to one or more than one (i.e., at least one) of the grammatical object. For example, "an element" refers to one element or more than one element. In addition, unless otherwise specified or clearly understood from the context to be in the singular form, the use of the articles "a" and "an" in this specification and the drawings should be understood to mean "one or more".

[0044] As used herein, the terms "example" and / or "exemplary" refer to an example, instance, or illustration. For the avoidance of doubt, such examples do not limit the subject matter described herein. In addition, any aspect or design described herein as "example" and / or "exemplary" is not necessarily advantageous or superior to other aspects or designs, and equivalent exemplary structures and techniques known to those of ordinary skill in the art are not excluded.

[0045] As used herein, the terms "first", "second", "third", etc. in the specification and claims (if any) distinguish similar elements and do not necessarily describe a particular order or chronological order. Where appropriate, these terms are interchangeable, for example, the embodiments herein can operate in an order other than that described herein or otherwise. In addition, the terms "including", "having" and any variations thereof cover non-exclusive inclusions, for example, a process, method, system, article, device or apparatus comprising a series of elements is not necessarily limited to these elements, and may also include other elements that are not explicitly listed or inherent to the process, method, system, article, device or apparatus.

[0046] As used herein, the terms "left", "right", "front", "rear", "top", "bottom", "upper", "lower", etc. in the description and claims (if any) are used for descriptive purposes and are not necessarily used to describe permanent relative positioning. The terms used are interchangeable where appropriate, for example, embodiments of the apparatus, methods, and / or articles described herein are capable of operation in other orientations than those shown or described herein.

[0047] Unless explicitly stated, any element / component behavior or instruction used herein is not critical or essential. In addition, the term "a set" includes items (e.g., related items, unrelated items, combinations of related items and unrelated items, etc.) and is interchangeable with "one or more". If only one item is included, the term "one" or similar terms are used. In addition, the terms "have", "have", "contain" or similar terms are open terms. In addition, unless explicitly stated otherwise, the term "based on" means "based at least in part on".

[0048] The terms "system", "device", "unit" and / or "module" used herein refer to different components / assemblies, component parts or components at various levels in a sequence. However, these terms may be replaced by other expressions that achieve the same purpose.

[0049] As used herein, the terms "coupled", "connected", "coupled", "connected", etc. refer to connecting two or more elements together mechanically, electrically and / or otherwise. Two or more electrical elements can be electrically coupled together, but not mechanically or otherwise coupled together. The coupling can be for any length of time, such as permanent, semi-permanent, or only momentary. "Electrically coupled" includes various types of electrical couplings. The absence of the words "detachable", "removable", etc. near the word "coupled" does not mean that the coupling, etc. is or is not removable.

[0050] As used herein, "or" refers to an inclusive or rather than an exclusive or. That is, unless stated otherwise or the context makes clear, "X employs A or B" refers to any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then in any of the above cases, "X employs A or B" is true.

[0051] As used herein, two or more elements or modules are "integrated" or "unified" if they function together. Two or more elements are "non-integrated" if each element can function independently.

[0052] As used herein, the term "real time" refers to operations that are performed as quickly as practically possible after a triggering event occurs. A triggering event may include receiving data required to perform a task or process information. Due to the inherent delays in transmission and / or computing speeds, the term "real time" includes operations that occur in "close to" real time, or operations that are slightly delayed after a triggering event occurs. In many embodiments, "real time" may refer to real time minus the time delay for processing (e.g., determining) and / or transmitting data. The specific time delay may vary depending on the type and / or amount of data, the processing speed of the hardware, the transmission capacity of the communication hardware, the transmission distance, etc. However, in many embodiments, the time delay may be less than about one second, two seconds, five seconds, or ten seconds.

[0053] As used herein, the term "approximately" may mean within a specified or unspecified range of a specified or unspecified specified value. In some embodiments, "approximately" may mean within plus or minus ten percent of a specified value. In other embodiments, "approximately" may mean within plus or minus five percent of a specified value. In yet another embodiment, "approximately" may mean within plus or minus three percent of a specified value. In other embodiments, "approximately" may mean within plus or minus one percent of a specified value.

[0054] As used herein, the term "component" refers to a unique and identifiable part, element, or unit within a larger system, structure, or entity. It is a building block for a specific function or purpose within a more complex whole. Components are typically designed to be modular and interchangeable to allow components to be combined or replaced in various configurations to create or modify a system. A component can be a combination of mechanical, electrical, hardware, firmware, software, and / or other engineering elements.

[0055] Digital electronic circuits, or digital electronic circuits in computer software, firmware or hardware, including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of these structures, can implement the embodiments and all functional operations described in this specification. The embodiments may be one or more computer program products, i.e., one or more computer program instruction modules encoded on a computer-readable medium, for execution or control of its operation by a data processing device. The computer-readable medium may be a machine-readable storage device, a machine-readable storage substrate, a storage device, a material combination that affects a machine-readable propagation signal, or a combination of one or more of these. The term "computing system" includes all devices, devices and machines for processing data, such as a programmable processor, a computer or multiple processors or computers. In addition to hardware, the device may also include code that creates an execution environment for the relevant computer program, such as code constituting processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more thereof. A propagation signal is an artificially generated signal (e.g., a machine-generated electrical signal, an optical signal, or an electromagnetic signal) that encodes information for transmission to a suitable receiving device.

[0056] The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit the implementation scheme. Therefore, any software and hardware can implement the system and / or method based on the description of this article without referring to specific software code.

[0057] A computer program (also referred to as a program, software, software application, script, or code) is written in any suitable form of a programming language, including compiled or interpreted languages. A computer program may be deployed in any suitable form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program may be stored as part of a file containing other programs or data (e.g., one or more scripts stored in a markup language document), or in a single file dedicated to the program in question, or in multiple coordinated files (e.g., a file storing one or more modules, subroutines, or portions of code). A computer program may be executed on one computer or on multiple computers, which may be located at one site or distributed across multiple sites and interconnected by a communications network.

[0058] One or more programmable processors execute one or more computer programs to perform functions by operating input data and generating outputs, thereby executing the processes and logic flows described in this specification. These processes and logic flows can also be performed by dedicated logic circuits, and the device can also be implemented as a dedicated logic circuit, such as but not limited to a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), an application-specific standard product (ASSP), a system on a chip (SOC) system, a complex programmable logic device (CPLD), etc.

[0059] Processors suitable for executing computer programs include, for example, general and special purpose microprocessors, and one or more processors of any suitable type in a digital computer. The processor may receive instructions and data from a read-only memory or a random access memory or both. The elements of a computer may include a processor for executing instructions and one or more storage devices for storing instructions and data. The computer also includes or is operatively coupled to transfer data to / receive data from one or more mass storage devices for storing data, or both, the one or more mass storage devices being, for example, a magnetic disk, a magneto-optical disk, an optical disk, or a solid state disk. However, the computer does not necessarily require these devices. In addition, another device, for example, a mobile phone, a personal digital assistant (PDA), a mobile audio player, a global positioning system (GPS) receiver, etc., may also be embedded in the computer. Computer-readable media suitable for storing computer program instructions and data include various forms of nonvolatile memory, media and storage devices, including, for example, semiconductor memory devices (e.g., erasable programmable read-only memory (EPROM), electronically erasable programmable read-only memory (EEPROM), and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks (e.g., compact disk read-only memory (CD ROM) disks, digital versatile disk read-only memory (DVD-ROM) disks), and solid-state disks. Special purpose logic circuits may supplement or be combined with the processor and memory.

[0060] To enable interaction with a user, a computer may have: a display device, such as a cathode ray tube (CRT) or a liquid crystal display (LCD), for displaying information to the user; and a keyboard and pointing device, such as a mouse or trackball, through which the user can provide input to the computer. Other types of devices may also interact with the user. For example, feedback to the user may be any appropriate form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and the computer may receive any appropriate form of input from the user, including sound, voice, or tactile input.

[0061] The embodiments described herein may be implemented by a computing system that includes a back-end component, such as a data server; or includes a middleware component, such as an application server; or includes a front-end component, such as a client computer with a graphical user interface or a web browser through which a user can interact with the embodiments; or any suitable combination of one or more such back-end, middleware, or front-end components. Any suitable form or medium of digital data communication, such as a communication network, may interconnect the various components of the system. Examples of communication networks include local area networks (LANs) and wide area networks (WANs), such as intranets and the Internet.

[0062] A computing system may include clients and servers. Clients and servers are remote from each other and typically interact through a communication network. The relationship between client and server arises through computer programs running on their respective computers and having a client-server relationship with each other.

[0063] Embodiments of the present invention may include or utilize a special-purpose or general-purpose computer including computer hardware. Embodiments within the scope of the present invention may also include physical and other computer-readable media for carrying or storing computer-executable instructions and / or data structures. Such computer-readable media may be any medium accessible to a general-purpose or special-purpose computer system. Computer-readable media that store computer-executable instructions are physical storage media. Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example and not limitation, embodiments of the present invention may include at least two different computer-readable media: physical computer-readable storage media and transmission computer-readable media.

[0064] Although the embodiments of the present invention are described herein with reference to specific example embodiments, it will be apparent that various modifications and variations may be made to these embodiments without departing from the broader spirit and scope of the various embodiments. For example, hardware circuits (e.g., complementary metal oxide semiconductor (CMOS) based logic circuits), firmware, software (e.g., embodied in a non-transitory machine-readable medium), or any combination of hardware, firmware, and software may implement and operate the various devices, units, and modules described herein. For example, transistors, logic gates, and circuits (e.g., application specific integrated circuits (ASICs) and / or digital signal processor (DSP) circuits) may embody various electrical structures and methods.

[0065] Furthermore, non-transitory machine-readable media and / or systems may embody various operations, processes, and methods disclosed herein.Accordingly, the specification and drawings are illustrative rather than restrictive.

[0066] Physical computer-readable storage media include RAM, ROM, EEPROM, CD-ROM or other optical disk storage devices (such as CD, DVD, etc.), magnetic disk storage or other magnetic storage devices, solid-state disks or any other media. They store the required program code in the form of computer-executable instructions or data structures that can be accessed by general or special-purpose computers.

[0067] As used herein, the term "network" refers to one or more data links capable of conveying electronic data between a computer system and / or a module and / or other electronic devices. When a network or another communication connection (hardwired, wireless or a combination of hardwired or wireless) transmits or provides information to a computer, the computer correctly regards the connection as a transmission medium. General or special computer access can include a network and / or a data link that carries the desired program code in the form of a computer executable instruction or data structure. The scope of computer-readable media includes the above-mentioned combination, which enables electronic data to be transmitted between a computer system and / or a module and / or other electronic devices. The term network can include the Internet, a local area network, a wide area network or a combination thereof. The network can include one or more networks or communication systems, such as the Internet, a telephone system, a satellite network, a cable television network and various other private and public networks. In addition, the connection can include a wired connection (such as a wire, a cable, an optical fiber line, etc.), a wireless connection or a combination thereof. In addition, although not shown, other computers, systems, devices and networks can also be connected to the network. A network is any set of devices or subsystems that connect (directly or indirectly) through links to a set of terminal nodes that share resources located on or provided by network nodes. Computers communicate with each other over digital interconnections using common communication protocols. For example, a subsystem may include a cloud. The cloud refers to servers accessed through the Internet, and the software and databases running on those servers.

[0068] Furthermore, upon reaching various computer system components, program code in the form of computer-executable instructions or data structures may be automatically transferred from a transmission computer-readable medium to a physical computer-readable storage medium (or vice versa). For example, computer-executable instructions or data structures received over a network or data link may be buffered in RAM with a network interface module (NIC) and then ultimately transferred to the computer system's RAM and / or less volatile computer-readable physical storage medium. Thus, computer system components that also (or even primarily) utilize transmission media may include computer-readable physical storage media.

[0069] For example, computer executable instructions include instructions and data that cause a general purpose computer, a special purpose computer, or a special purpose processing device to perform a function or group of functions. For example, computer executable instructions can be binary instructions, intermediate format instructions (e.g., assembly language), or even source code. Although the subject matter described herein employs specific language of structural features and / or methodological acts, the described features or acts do not limit the subject matter defined in the claims. Rather, the features and acts described herein are example forms of implementing the claims.

[0070] Although this specification contains many specific details, these details should not be construed as limitations on the scope of the disclosure or claims, but only as descriptions of specific features of particular embodiments. A single embodiment may implement some of the functions described in this specification in the context of a separate embodiment. Conversely, multiple embodiments may implement the various functions described herein in the context of a single embodiment, either separately or in any suitable subcombination. In addition, although the features described herein function in some combinations, or are even initially claimed to be so, in some cases, one or more features in the claimed combination may be deleted from the combination, and the claimed combination may be directed to subcombinations or variations of subcombinations.

[0071] Similarly, although the operations described in the drawings are performed in a particular order to achieve the desired effect, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order, or requiring that all illustrated operations be performed to achieve the desired effect. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system components in the embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the program components and systems can be integrated into a single software product or packaged into multiple software products.

[0072] Even though particular combinations of features are recited in the claims and / or specification, these combinations are not intended to limit the disclosure of possible implementations. Other implementations are also within the scope of the claims. For example, the operations recited in the claims can be performed in a different order and still achieve the desired effect. In fact, many of these features can be combined in ways that are not explicitly stated in the claims and / or disclosed in the specification. Although each dependent claim may only directly depend on one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claims.

[0073] In addition, a computer system including one or more processors and a computer-readable medium (e.g., a computer memory) can also implement these methods. In particular, one or more processors can execute computer-executable instructions stored in the computer memory to perform various functions, such as the behaviors described in the embodiments.

[0074] Those skilled in the art will appreciate that the present invention can be implemented in a network computing environment with a variety of computer system configurations, including personal computers, desktop computers, notebook computers, information processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, microcomputers, mainframe computers, mobile phones, PDAs, pagers, routers, switches, etc. The present invention can also be implemented in a distributed system environment, in which local and remote computer systems are connected via a network (or via a hardwired data link, a wireless data link, or a combination of a hardwired and wireless data link) to perform tasks simultaneously. In a distributed system environment, program modules can be located in local and remote memory storage devices.

[0075] Unless otherwise indicated, the following terms and phrases shall be understood to have the following meanings.

[0076] As used herein, the term "cryptographic protocol" is also known as a security protocol or encryption protocol. It is an abstract or concrete protocol that performs security-related functions and typically uses a sequence of cryptographic primitives for encryption methods. The protocol describes the use of the algorithm. A sufficiently detailed protocol includes details about data structures and representations to enable multiple interoperable versions of the program.

[0077] Secure application-level data transport uses cryptographic protocols extensively. Cryptographic protocols typically include at least several of the following aspects: key negotiation or establishment, entity authentication, symmetric encryption, and message authentication material construction, secure application-level data transmission, non-repudiation methods, secret sharing methods, and secure multi-party computation.

[0078] Network switches use cryptographic protocols such as Secure Socket Layer (SSL) and Transport Layer Security (TLS), the successor to SSL, to secure data communications on wireless networks.

[0079] As used herein, the term "unauthorized access" means that someone uses another person's account or other methods to gain access to a website, program, server, service, or other system. For example, if someone keeps guessing passwords or usernames for accounts that do not belong to them until they gain access, this is considered unauthorized access.

[0080] As used herein, the term "IoT" stands for the Internet of Things, which describes a network of physical "things" or objects embedded with sensors, software and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet.

[0081] As used herein, "machine learning" refers to an algorithm that gives a computer the ability to learn without explicit programming, including algorithms that learn from data and predict the data. Machine learning techniques include, but are not limited to, support vector machines, artificial neural networks (ANN) (also referred to herein as "neural networks"), deep learning neural networks, logistic regression, discriminant analysis, random forests, linear regression, rule-based machine learning, Naive Bayes, nearest neighbors, decision trees, decision tree learning, and hidden Markov, etc. For clarity, part of the machine learning process can use algorithms such as linear regression or logistic regression. However, using linear regression or another algorithm as part of the machine learning process is different from using a spreadsheet program to perform statistical analysis, such as regression. The machine learning process can continuously learn and adjust the classifier as new data becomes available, and does not rely on explicit or rule-based programming. An artificial neural network can have a feedback loop to dynamically adjust the system output as it learns from new data (because new data becomes available). In machine learning, back propagation and feedback loops are used to train artificial intelligence / machine learning (AI / ML) models to improve the accuracy and performance of the model over time. Statistical modeling relies on discovering relationships between variables (like mathematical equations) to predict outcomes.

[0082] As used herein, the term "data mining" is the process used to transform raw data into useful information. It is the process of analyzing large data sets to discover hidden patterns, relationships, and insights that can be used for decision making and predictions.

[0083] As used herein, the term "data acquisition" is the process of sampling signals that measure real-world physical conditions and converting the resulting samples into digital values ​​that are processed by a computer. Data acquisition systems typically convert analog waveforms into digital values ​​for processing. Components of a data acquisition system include sensors that convert physical parameters into electrical signals, signal conditioning circuits that convert sensor signals into a form that can be converted into digital values, and analog-to-digital converters that convert the conditioned sensor signals into digital values. Standalone data acquisition systems are often referred to as dataloggers.

[0084] As used herein, the term "dashboard" is a type of interface that visualizes specific key performance indicators (KPIs) for a specific goal or process. It is based on data visualization and infographics.

[0085] As used herein, a "database" is a collection of information organized so that it can be easily accessed, managed, and updated. A computer database typically contains an aggregation of data records or files.

[0086] As used herein, the term "data set" or "Dataset" is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where each column of the table represents a specific variable and each row corresponds to a given record of the data set in question. A data set lists the value of each variable for each member of the data set, such as the height and weight of an object. Each value is called a datum. A data set can also consist of a collection of documents or files.

[0087] As used herein, a “sensor” is a device that detects and measures the physical properties of the surrounding environment and converts it into electrical or digital signals for further processing. Sensors play a vital role in collecting data for a variety of applications across various industries. Sensors can be made of electronic, mechanical, chemical or other engineered components. Examples include sensors for measuring temperature, pressure, humidity, proximity, light, acceleration, direction, etc.

[0088] The term "infotainment system" or "in-vehicle infotainment system" (IVI) as used herein refers to a combination of vehicle systems for delivering entertainment and information. In one example, the information can be delivered to the driver and passengers / occupants of the vehicle through audio / video interfaces, control elements such as touch screen displays, button panels, voice commands, etc. Some of the major components of an in-vehicle infotainment system are an integrated head-unit, heads-up display (also known as head-up display), high-end digital signal processors (DSPs) and graphics processing units (GPUs) to support multiple displays, operating systems, controller area networks (CAN), low voltage differential signaling (LVDS) and other network protocol support (as required), connectivity modules, vehicle sensor integration, digital instrument clusters, etc.

[0089] The term "environment" or "surroundings" as used herein refers to the surroundings and space that the vehicle is traveling in. It refers to the dynamic environment in which the vehicle is traveling, including other vehicles, obstacles, pedestrians, lane boundaries, traffic signs and signals, speed limits, potholes, snow, water, etc.

[0090] The term "autonomous driving mode" as used herein refers to an independent and unsupervised mode of operation.

[0091] As used herein, the term "vehicle" refers to an object used to transport people or goods. Cars, pickups, trucks, buses, etc. are examples of vehicles.

[0092] The term "autonomous driving vehicle" as used herein, also known as self-driving vehicle, unmanned vehicle, robotic vehicle, refers to a vehicle that incorporates vehicle automation, i.e., a ground vehicle that can sense its environment and move safely with little or no human input. Self-driving vehicles incorporate various sensors to sense the surrounding environment, such as thermal imaging cameras, radio detection and ranging (RADAR), light detection and ranging (LIDAR), sound navigation and ranging (SONAR), global positioning system (GPS), odometer, and inertial measurement unit. A control system designed for this purpose interprets the sensor information to identify the appropriate navigation path as well as obstacles and related landmarks.

[0093] The term "communication module" or "communication system" as used herein refers to a system that enables information exchange between two points. The process of transmitting and receiving information is called communication. The elements of communication include but are not limited to the transmitter of information, the channel or medium of communication, and the receiver of information.

[0094] As used herein, the term "autonomous communication" includes communication over a period of time with minimal supervision in different scenarios and is not based solely or entirely on pre-coded scenarios or pre-coded rules or pre-defined protocols. Generally speaking, autonomous communication is conducted in an independent and unsupervised manner. In one embodiment, the communication module enables autonomous communication.

[0095] The term "connection" as used herein refers to a communication link. It refers to a communication channel that connects two or more devices for the purpose of data transfer. It can refer to a physical transmission medium, such as a wire, or to a logical connection through a multiplexed medium, such as a radio channel in telecommunications and a computer network. A channel is used to transmit information, such as a digital bit stream, from one or several transmitters to one or several receivers. A channel has a certain information transmission capacity, which is usually measured by its bandwidth in Hertz (Hz) or data rate in bits per second. For example, vehicle-to-vehicle (V2V) communication can wirelessly exchange information about the speed, location, and direction of surrounding vehicles.

[0096] The term "communication" as used herein refers to the transmission of information and / or data from one point to another. Communication can be carried out with the help of electromagnetic waves. It is also the flow of information from one point called the source to another point called the receiver. Communication includes one of the following: transmitting data, instructions and information or a combination of data, instructions and information. Communication occurs between any two communication systems or communication units. The term communication here includes systems combined with other more specific types of communication, such as V2I (vehicle to infrastructure), V2N (vehicle to network), V2V (vehicle to vehicle), V2P (vehicle to pedestrian), V2D (vehicle to device) and V2G (vehicle to grid) as well as vehicle to everything (V2X) communication.

[0097] In addition, the communication device is configured on a computer with a communication function and is connected to the vehicle-mounted emergency reporting device for two-way communication via a communication line of a radio station and a communication network such as a public telephone network or via satellite communication of a communication satellite. The communication device is suitable for communicating with the communication terminal via the communication network.

[0098] The term "vehicle-to-vehicle (V2V) communications" refers to technology that allows vehicles to broadcast and receive information. The messages can be omnidirectional, creating a 360-degree "awareness" of other vehicles in the vicinity. Vehicles can be equipped with appropriate software (or safety applications) that can use information from surrounding vehicles to determine potential collision threats as they progress.

[0099] The term "Vehicle-to-Everything (V2X) communication" as used herein refers to the transmission of information from a vehicle to any entity that may affect the vehicle and vice versa. There are two types of V2X communication technologies, depending on the underlying technology used: cellular networks and other technologies that support direct device-to-device communication (e.g., Dedicated Short Range Communication (DSRC), Port Community System (PCS), wait).

[0100] The term "protocol" as used herein refers to the procedures required to initiate and maintain communications; a formal set of conventions governing the format and relative timing of message exchanges between two communicating terminals; a set of conventions governing the interaction of processes, devices and other components within a system; a set of signaling rules for passing information or commands between boards connected to a bus; a set of signaling rules for passing information between agents; a set of semantic and syntactic rules that determine the behavior of interacting entities; a set of rules and formats (semantics and syntax) that determine the communication behavior of simulated applications; a set of conventions or rules governing the interaction of processes or applications between communicating terminals; a formal set of conventions governing the format and relative timing of message exchanges between communicating terminals; a set of semantic and syntactic rules that determine the behavior of functional units in achieving meaningful communication; a set of semantic and syntactic rules for exchanging information.

[0101] As used herein, the term "communication protocol" refers to standardized communications between any two systems. An exemplary communication protocol is the DSRC protocol. The DSRC protocol uses a specific frequency band (e.g., 5.9 GHz (gigahertz)) and specific message formats (such as basic safety messages, signal phase and timing, and roadside alerts) to enable communications between vehicles and infrastructure components (such as traffic signals and roadside sensors). DSRC is a standardized protocol whose specifications are maintained by various organizations, including the Institute of Electrical and Electronics Engineers (IEEE) and the Society of Automotive Engineers (SAE) International.

[0102] As used herein, the term "two-way communication" refers to the exchange of data between two components. In one example, the first component may be a vehicle and the second component may be an infrastructure that is enabled by a system of hardware, software, and firmware. This communication is typically wireless.

[0103] The term "alert" or "alert signal" refers to a communication that draws attention. Alerts may include visual, tactile, audible alerts, and combinations of these alerts to warn the driver or passengers. These alerts enable the recipient (such as the driver or passenger) to react and respond quickly.

[0104] As used herein, the term "communicates with" refers to any coupling, connection, or interaction that uses signals to exchange information, messages, instructions, commands, and / or data, using any system, hardware, software, protocol, or format, whether the exchange occurs wirelessly or through a wired connection.

[0105] The term "electronic control unit" (ECU), also known as "electronic control module" (ECM), is generally a module that controls one or more subsystems. In this article, an ECU can be installed in a small vehicle or other motor vehicle. It can refer to many ECUs, and can include but is not limited to an engine control module (Engine Control Module, referred to as ECM), a powertrain control module (PCM), a transmission control module (TCM), a brake control module (BCM) or an electronic brake control module (EBCM), a central control module (CCM), a central timing module (CTM), a general electronic module (GEM), a body control module (Body Control Module, referred to as BCM) and a suspension control module (SCM). ECUs are sometimes collectively referred to as the vehicle's computer or the vehicle's central computer, and may include separate computers. In one example, an electronic control unit can be an embedded system in the vehicle's electronic equipment. In another example, the electronic control unit is wirelessly coupled to the vehicle's electronic equipment.

[0106] The terms "non-transitory computer-readable medium" and "computer-readable medium" include a single medium or multiple media (such as a centralized or distributed database), and / or associated caches and servers that store one or more sets of instructions. In addition, the terms "non-transitory computer-readable medium" and "computer-readable medium" include any tangible medium that can store, encode, or carry a set of instructions for execution by a processor, for example, which, when executed, causes the system to perform any one or more of the methods or operations disclosed herein. As used herein, the term "computer-readable medium" is expressly defined to include any type of computer-readable storage device and / or storage disk, and does not include propagating signals.

[0107] The term "vehicle data bus" as used herein represents an interface for a vehicle data bus (e.g., Controller Area Network (CAN), Local Interconnect Network (LIN), Ethernet / IP, FlexRay protocol, and Media Oriented Systems Transport (MOST)) that enables communication between onboard equipment (OBE) and other vehicle systems to support connected vehicle applications.

[0108] The term "handshake" refers to the exchange of preset signals between agents connected by a communication channel to ensure that each agent is connected to the other (and not to an imposter). This may also include the use of passwords and codes by the operator. Handshake signals are transmitted back and forth over the communication network to establish a valid connection between the two stations. Hardware handshaking uses dedicated lines, such as the request-to-send (RTS) and clear-to-send (CTS) lines in the recommended use of Standard 232 (RS-232) serial transmission. Software handshaking sends codes such as "synchronize" (SYN) and "acknowledge" (ACK) in the transmission control protocol / Internet protocol (TCP / IP) transmission.

[0109] The term "computer vision module" or "computer vision system" allows the vehicle to "see" and interpret the world around it. The system uses a combination of cameras, sensors, and other technologies such as Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR), Sound Navigation and Ranging (SONAR), Global Positioning System (GPS), and machine learning algorithms to collect visual data about the vehicle's surroundings and analyze that data in real time. The computer vision system is designed to perform a range of tasks, including object detection, lane detection, and pedestrian recognition. It uses deep learning algorithms and other machine learning techniques to analyze visual data and make decisions about how to control the vehicle. For example, a computer vision system can use an object detection algorithm to identify other vehicles, pedestrians, and obstacles in the vehicle's path. It can then use this information to calculate the vehicle's speed and direction, adjust the vehicle's trajectory to avoid collisions, and apply braking or acceleration as needed. It allows the vehicle to operate safely and efficiently in a variety of driving / driving situations.

[0110] The term "driver" as used herein refers to an occupant who, even if the occupant is not actually driving the vehicle, is located within the vehicle so as to be able to take over control and act as the driver of the vehicle when the vehicle control system transfers control to the occupant or driver, or when the vehicle control system is not operating in an autonomous or semi-autonomous mode.

[0111] As used herein, the term “host vehicle” refers to a vehicle that observes the environment and makes decisions based on the observations.

[0112] The term "target vehicle" as used herein refers to a vehicle that is of primary concern to the host vehicle. The target vehicle may or may not be an autonomous vehicle. It may or may not have vehicle-to-vehicle (V2V) communications enabled.

[0113] As used herein, the term "nearby vehicle" or "adjacent vehicle" or "surrounding vehicle" refers to a vehicle anywhere near the host vehicle within the communication range of the host vehicle. It may or may not be an autonomous vehicle. It may or may not have V2V communication enabled. In some embodiments, a neighboring vehicle may more specifically refer to a vehicle immediately in the next lane or behind the host vehicle.

[0114] As used herein, the term "road condition" refers to the physical state of a road, including its smoothness, texture, and friction. Road condition can be affected by a variety of factors such as weather, traffic volume, and maintenance practices. In one embodiment, road condition can be classified as dry, wet, snow-covered, or icy.

[0115] "Application server" refers to a server that hosts applications or software that provides business applications through communication protocols. The application server framework is a service layer model. It includes software components that can be used by software developers through application programming interfaces. It is a system software that sits between the operating system (OS) on one side, external resources (e.g., database management system (DBMS), communication and Internet services) on another side, and user applications on a third side.

[0116] The term "cybersecurity" as used herein refers to the application of technologies, processes, and controls to protect systems, networks, programs, devices, and data from cyber attacks.

[0117] The term "cybersecurity module" as used herein refers to a module that includes application technologies, processes, and controls to protect systems, networks, programs, devices, and data from cyber attacks and threats. It is designed to reduce the risk of cyber attacks and prevent unauthorized exploitation of systems, networks, and technologies. It includes, but is not limited to, critical infrastructure security, application security, network security, cloud security, Internet of Things (IoT) security.

[0118] The term "encryption" as used herein refers to the protection of digital data using one or more mathematical techniques and a password or "key" for decrypting the information. It refers to the conversion of information or data into code, especially to prevent unauthorized access. It can also refer to the hiding of information or data by converting it into code. It can also be called a cipher, code, encryption, encoding. A simple example is to represent letters with numbers - for example, "A" is "01", "B" is "02", and so on. For example, a message like "HELLO" will be encrypted as "0805121215", and this value will be transmitted to the recipient over the network.

[0119] The term "decryption" as used herein refers to the process of converting an encrypted message back to its original format. This is generally the reverse process of encryption. It decodes encrypted information so that only authorized users can decrypt the data, as a key or password is required for decryption. This term can be used to describe methods of decrypting data manually or using an appropriate code or key.

[0120] The term "cybersecurity threat" as used herein refers to any possible malicious attack that attempts to illegally access data, disrupt digital operations, or destroy information. Malicious behavior generally includes, but is not limited to, destroying data, stealing data, or disrupting digital life. Cyber ​​threats include, but are not limited to, malware, spyware, phishing attacks, ransomware, zero-day exploits, Trojans, advanced persistent threats, wiper attacks, data manipulation, data destruction, rogue software, malicious advertising, unpatched software, computer viruses, man-in-the-middle attacks, data breaches, denial of service (DoS) attacks, and other attack vectors.

[0121] The term "hash value" used herein can be thought of as a fingerprint of a file. The contents of a file are processed through an encryption algorithm and a unique value identifying the contents of the file, i.e., a hash value, is generated. If the content is modified in any way, the hash value will also change significantly. Exemplary algorithms for generating hash values: Message Digest-5 (MD5) algorithm and Secure Hash Algorithm-1 (SHA1).

[0122] The term "integrity check" as used herein refers to the verification of the accuracy and consistency of files, data, etc. associated with a system. It can be performed using a verification tool that can detect whether any critical system files have been changed, thereby enabling system administrators to look for unauthorized changes to the system. For example, data integrity corresponds to the quality of the data in a database and the level at which users check the quality, integrity, and reliability of the data. Data integrity verification verifies that the data in the database is accurate and operates as expected in a given application.

[0123] As used herein, the term "alarm" refers to a trigger when a system or a component in a system fails or fails to perform as expected. When a certain event occurs, the system may enter an alarm state. An alarm indication signal is a visual signal indicating an alarm state. For example, when a network security threat is detected, an alarm may be issued to a system administrator via an audible alarm, a message, an illuminated LED, a pop-up window, etc. The alarm indication signal may be reported downstream from the detection device to prevent an adverse situation or cascading effect.

[0124] As used herein, the term "cryptographic protocol" is also referred to as a security protocol or encryption protocol. It is an abstract or specific protocol that performs security-related functions and generally uses a sequence of cryptographic primitives for encryption methods. The protocol describes the use of an algorithm. A sufficiently detailed protocol includes details about data structures and representations to implement multiple interoperable versions of a program. Cryptographic protocols are widely used for secure application layer data transmission. Cryptographic protocols generally include at least several of these aspects: key negotiation or establishment, entity authentication, symmetric encryption, and message authentication material construction, secure application layer data transmission, non-repudiation methods, secret sharing methods, and secure multi-party computations. Hash algorithms can be used to verify the integrity of data. Network switches use cryptographic protocols such as Secure Socket Layer (SSL) and Transport Layer Security (TLS) (SSL's successor) to protect data communication security on wireless networks.

[0125] As used herein, the term "range of a vehicle" refers to the maximum distance a vehicle can travel before it needs to be refueled or recharged. The term is commonly used for electric vehicles (EVs) and hybrid vehicles, as well as traditional internal combustion vehicles that use gasoline, diesel or other fuels. Range is usually measured in miles or kilometers and represents the distance a vehicle can cover.

[0126] The term "article" as used herein is related to a vehicle and covers a range of articles or accessories that can be added to a vehicle. Articles can also include a separate unpowered trailer connected to a motor vehicle. In a broad sense, an article can refer to anything that changes or affects the mileage of a vehicle. Some examples can be cargo, trailers, bicycles, boats, etc. Some accessories for accessory assistance can be installed at the factory by the manufacturer or added in the aftermarket by the owner or a professional, which can also affect the mileage. Some common examples include roof racks for carrying additional cargo, towing hitches for towing trailers, bicycle racks for bicycle transportation, and cargo racks for carrying additional luggage and equipment, etc. A trailer is any wheeled structure towed by another vehicle. Some common trailer types include multi-purpose trailers, pop-up campers, travel trailers, livestock trailers, flatbed trailers, and boat trailers. A trailer can also include a vehicle towed by a recreational vehicle (RV) or another motor vehicle.

[0127] The term "vehicle configuration" refers to the specific setup or arrangement of items in a vehicle, taking into account the presence or absence of accessories or additional items and where they are located. This configuration may involve various accessories and improvements that are intended to enhance the functionality, appearance and / or performance of the vehicle. These modifications may include features installed by the manufacturer at the factory or features added by the owner or a professional after the sale. Examples of different vehicle configurations include connecting a trailer versus not connecting a trailer, installing a bicycle rack versus not installing a bicycle rack, and securing a bicycle in a bicycle rack versus not securing a bicycle in a bicycle rack. Optionally, the location of the bicycle is also considered in the vehicle configuration, such as on a luggage rack above the vehicle or on a bicycle rack at the rear of the vehicle. The concept of vehicle configuration becomes relevant when changes affect the vehicle's mileage, indicating how and to what extent these changes affect the vehicle's overall mileage. A vehicle configuration change is any change to the vehicle that affects mileage.

[0128] As used herein, the term "smart range" refers to the estimated distance a vehicle can travel on a single charge or tank of fuel, taking into account factors such as driving conditions, terrain, weather, driving habits, and accessories. For example, the system can employ sensors and advanced artificial intelligence / machine learning algorithms (AI / ML), as well as data processing to make intelligent estimates. The system can leverage real-time data and historical patterns to create more accurate and personalized range predictions. The intelligent aspects of the system enable it to learn from past behavior and optimize its calculations for different drivers and scenarios.

[0129] As used herein, the term “adaptive” refers to the ability to adjust or modify a system based on changing conditions, circumstances, or inputs. When adaptability is in effect, the range estimation system is able to adjust and update its calculations based on changing conditions during driving. For example, adaptive range can adjust or modify the system to increase the estimated range or available driving range based on current circumstances, environment, and requirements. As conditions change, the system continually recalculates the estimated range to more accurately predict how far the vehicle can travel before it needs to be recharged or refueled. This adaptive capability ensures that the range estimate remains relevant and reliable even as driving conditions change.

[0130] As used herein, the term “adaptive intelligent driving range estimation” refers to a dynamic and intelligent system or process that calculates and provides a real-time estimate of the distance a vehicle can travel on a single charge or tank of fuel. The estimate takes into account various changing factors, such as driving conditions, terrain, weather, driving habits, and the current state of the vehicle's systems (e.g., battery health for electric vehicles). For example, the system can utilize various technologies (e.g., vision systems and AI / ML technologies) to dynamically and intelligently calculate how far the vehicle can travel based on current conditions, historical data, and real-time input from the vehicle and users. This helps drivers plan their trips more efficiently and avoid unexpected problems related to running out of energy or fuel.

[0131] As used herein, the term "plan" refers to something that is well thought out, organized, and arranged in advance with a specific intent or purpose. To plan something means to take steps, actions, or decisions in advance to achieve a specific goal or result. Planning involves considering a variety of factors, making decisions, setting goals, and creating a structured approach to achieve those goals. For example, planning a vacation may include picking up and dropping off items and people at specific locations, the origin and destination of the trip, and the dates and times of the trip.

[0132] The embodiments described herein may be integrated at any possible level of technical detail for one or more of the systems, methods, devices and / or computer program products. The computer program product may include a computer-readable storage medium (or medium) having computer-readable program instructions thereon for causing a processor to perform various aspects of one or more embodiments described herein. The computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. For example, the computer-readable storage medium may be, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a superconducting storage device, and / or any suitable combination of the above devices. A non-exhaustive list of more specific examples of computer-readable storage media may also include the following: a portable computer floppy disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device (e.g., a convex structure in a punch card or a groove with instructions recorded thereon) and / or any suitable combination of the above devices. As used herein, computer-readable storage media itself does not represent transient signals, such as radio waves and / or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides and / or other transmission media (such as light pulses through optical fibers), and / or electrical signals transmitted through wires.

[0133] Computer-readable program instructions as described herein can be downloaded from computer-readable storage media to respective computing / processing devices, and / or downloaded to external computers or external storage devices via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network). The network may include copper transmission cables, optical transmission optical fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions to the computer-readable storage medium in the corresponding computing / processing device for storage. The computer-readable program instructions for performing the operation of one or more embodiments described herein may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, and / or source code and / or object code written in any combination of one or more programming languages. Programming languages ​​may be high-level programming languages, low-level programming languages, compiled languages, interpreted languages, scripting languages, functional programming languages, markup languages, etc. Programming languages ​​include: object-oriented programming languages, such as Smalltalk, C++ or similar languages; and / or procedural programming languages, such as "C" programming languages ​​and / or similar programming languages. Computer-readable program instructions can be executed entirely on a computer, or partially on a computer, as an independent software package, partially on a computer and / or partially on a remote computer, or completely on a remote computer and / or server. In the latter scenario, the remote computer can be connected to the computer via any type of network, including a local area network (LAN) and / or a wide area network (WAN), and / or can be connected to an external computer (e.g., using an Internet service provider via the Internet). In one or more embodiments, electronic circuits including, for example, programmable logic circuits, field programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs) can execute computer-readable program instructions by utilizing the state information of computer-readable program instructions to personalize the electronic circuits, thereby performing various aspects of one or more embodiments described herein.

[0134] Various aspects of one or more embodiments described herein are described with reference to flowchart illustrations and / or block diagrams of methods, devices (systems) and computer program products according to one or more embodiments described herein. Each block in the flowchart schematic diagram and / or block diagram and the block combination in the flowchart schematic diagram and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer and / or other programmable data processing device to produce a machine, so that instructions executed by a processor of a computer or other programmable data processing device can create a method for implementing the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can instruct a computer, a programmable data processing device and / or other device to operate in a specific manner, so that a computer-readable storage medium storing instructions can include a manufactured article, which includes instructions that can implement the functions / behaviors specified in the flowchart and / or block diagram blocks. Computer-readable program instructions may also be loaded onto computers, other programmable data processing devices and / or other equipment to cause a series of operational behaviors to be executed on the computers, other programmable devices and / or other equipment to produce a computer-implemented process, thereby causing the instructions executed on the computers, other programmable devices and / or other equipment to implement the functions / behaviors specified in the flowcharts and / or block diagrams.

[0135] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functions and / or operations of systems, computer-implemented methods and / or computer program products that may be implemented according to one or more embodiments described herein. In this regard, each block in the flowchart or block diagram may represent a module, segment and / or partial instruction, including one or more executable instructions for implementing a specified logical function (one or more). In one or more alternative embodiments, the functions marked in the blocks may not appear in the order marked in the accompanying drawings. For example, two blocks displayed in succession may be executed substantially simultaneously, and / or blocks may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and / or a combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that can perform specified functions and / or behaviors and / or execute one or more dedicated hardware and / or computer instruction combinations.

[0136] Although the subject matter described herein is the general case of computer executable instructions of a computer program product running on a computer and / or a computer, those skilled in the art will recognize that one or more embodiments herein may also be implemented in conjunction with one or more other program modules. Program modules include routines, programs, components, data structures, and / or similar programs that perform specific tasks and / or implement specific abstract data types. In addition, other computer system configurations, including single-processor and / or multi-processor computer systems, microcomputing devices, mainframe computers, and computers, handheld computing devices (e.g., PDAs, phones), microprocessor-based or programmable consumer and / or industrial electronic devices, and / or similar devices, can implement the computer-implemented methods described herein. A distributed computing environment may also implement the described aspects, in which remote processing devices connected via a communication network perform tasks. However, a stand-alone computer may also implement one or more aspects of one or more embodiments described herein, if not all. In a distributed computing environment, program modules may be located in local and remote memory storage devices.

[0137] The terms "component", "system", "platform", "interface" and / or similar terms used in this application may refer to and / or include entities related to computers or entities related to operating machines with one or more specific functions. The entities described herein may be hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process, a processor, an object, an executable file, an execution thread, a program, and / or a computer running on a processor. For example, an application and a server running on a server may be a component. One or more components may be located in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In another example, each component may be executed from various computer-readable media, on which various data structures are stored. Each component may communicate via local and / or remote processes, for example, according to a signal with one or more data packets (for example, data that a component interacts with another component in a local system, a distributed system, and / or data that interacts with other systems across a network, such as the Internet, via a signal). As another example, a component may be a device having a specific functionality whose mechanical components are operated by electrical or electronic circuits that are in turn operated by software and / or firmware applications executed by a processor. In this case, the processor may be internal and / or external to the device and may execute at least a portion of the software and / or firmware applications. As another example, a component may be a device that provides a specific functionality through electronic components without mechanical components, wherein the electronic components may include a processor and / or other devices that execute software and / or firmware that at least partially endow the electronic components with functionality. In one aspect, the component may simulate the electronic component via a virtual machine, such as within a cloud computing system.

[0138] As used in this specification, the term "processor" may refer to any computational processing unit and / or device, including but not limited to: a single-core processor; a single-core processor with software multi-threaded execution capability; a multi-core processor; a multi-core processor with software multi-threaded execution capability; a multi-core processor with hardware multi-threading technology; a parallel platform; and / or a parallel platform with distributed shared memory. In addition, a processor may also refer to an integrated circuit, an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), discrete gate or transistor logic, discrete hardware components, and / or any combination thereof, which is designed to perform the functions described herein. In addition, a processor may utilize nanoscale architectures, such as but not limited to molecular-based transistors, switches, and / or gates, to optimize space usage and / or improve the performance of associated devices. A combination of computational processing units may implement a processor.

[0139] As used herein, terms such as "storage," "storage," "data storage," "data storage," "database," and any other information storage component related to the operation and functionality of a component refer to a "memory component," an entity embodied as a "memory," or a component that includes a memory. The memory and / or memory components described herein may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. By way of example and not limitation, non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, and / or non-volatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). Volatile memory may include RAM, which may be used, for example, as an external cache memory. By way of illustration and not limitation, RAM can have a variety of forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct bus dynamic RAM (DRDRAM), and / or bus dynamic RAM (RDRAM). In addition, the memory components of the systems and / or computer-implemented methods described herein include, but are not limited to, these and / or any other suitable types of memory.

[0140] The embodiments described herein include only examples of systems and computer-implemented methods. Of course, it is not possible to describe every conceivable combination of components and / or computer-implemented methods for the purpose of describing one or more embodiments, but one of ordinary skill in the art will recognize that many further combinations and / or permutations of one or more embodiments are possible. In addition, when the terms "including," "having," "having," and the like are used in the detailed description, claims, appendices, and / or drawings, these terms are intended to be inclusive in a manner similar to the term "comprising," as interpreted when "including" is used as a transitional word in a claim.

[0141] Without departing from the spirit or features of the present invention, the present invention may also be embodied in other specific forms. The described embodiments are illustrative in all respects and not restrictive. Therefore, the appended claims rather than the description herein indicate the scope of the present invention. All changes within the meaning and equivalent range of the claims are within their scope.

[0142] The description of one or more embodiments is for illustrative purposes only and is not intended to be exhaustive or limited to the embodiments described herein. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein best explain the principles of the embodiments, practical applications, and / or technical improvements over technologies found in the marketplace, and / or enable others of ordinary skill in the art to understand the embodiments described herein.

[0143] In one embodiment, the system provides a range estimate. The range can be an electrical range estimate or a fuel range estimate depending on the type of vehicle. Prior art systems provide range estimates. Prior art systems provide an estimate of the number of miles a vehicle can travel based on the amount of electricity or fuel in the vehicle. However, prior art systems do not provide range estimates when the vehicle has accessories or additional items. Prior art systems do not provide vehicle range estimates when a trailer is attached to the vehicle.

[0144] Range is an important factor for consumers when considering whether a vehicle is suitable for their needs, especially for those consumers who frequently travel long distances or those consumers with limited charging or refueling infrastructure. Manufacturers typically provide range estimates based on standardized testing procedures, but actual range can vary depending on driving habits, weather conditions, and other factors. For traditional internal combustion engine vehicles, range is determined by fuel efficiency and fuel tank capacity, while for electric vehicles, range is based on battery efficiency and battery capacity. Range represents the distance a vehicle can travel before it needs to be refueled or recharged. In addition to design and performance factors that affect range, there are many other factors, such as additional load and driving conditions that affect range. In one embodiment, the present application addresses these factors that affect range, such as additional load, driving conditions, driving habits, etc.

[0145] Today, vehicle range is based on the vehicle's specifications and the total fuel / electricity available. The problem is that it is difficult to determine the range of a vehicle (e.g., a fuel-based vehicle or an electric vehicle) when the owner adds additional items to the vehicle. For example, when an owner adds a trailer and a boat, the vehicle system cannot determine the impact of the additional weight. This can lead to inaccurate range estimates, leaving the driver in trouble.

[0146] In one aspect, a system is provided on the vehicle that can automatically detect items added to the vehicle. For example, a connection (wired or wireless) is established when a trailer is connected. Once the connection is established, the system can modify the estimated mileage. In one aspect, the connection includes information about the attached item (e.g., item identification, length, weight, wind resistance, etc.). This information can be extracted from the item identification. For example, using the item ID, the system can identify additional information from the cloud using a preferred communication protocol. On the other hand, the system can extract some previously stored information and interact with the owner to complete the collection of the required information to accurately estimate the mileage. This can include using route information, weather information, the impact of wind, etc. to determine the impact of attaching additional items to the vehicle. Once the analysis is completed, the system can determine a new mileage estimate.

[0147] The problem is that it is difficult to accurately determine the vehicle's range when the owner plans to add additional items or people to the vehicle. For example, when the owner goes to a friend's house and adds a trailer and boat, the vehicle system cannot determine the impact of the additional weight. This can lead to inaccurate range estimates, leaving the driver in trouble. In addition, the current estimated range does not include the additional passengers (e.g., the impact caused by the additional weight).

[0148] In one aspect, the system receives information from the user. For example, the system receives the final destination and any additional stopovers. The system receives information about additional accessories planned to be attached to the vehicle at the origin or stopovers. The system also receives how many and which additional passengers will be added. Using this information, the system initially estimates the available driving range and makes recommendations for extending the driving range based on the additional accessories and passenger additions. Thereafter, the system can adaptively adjust the driving range using all available information (including tire pressure, weather, or unplanned stops / weight added or removed).

[0149] The problem is that it is difficult to determine the vehicle's mileage when the owner adds additional items to the vehicle. For example, when the owner adds a trailer and a boat, the vehicle system cannot determine the impact of the additional weight. This can lead to inaccurate mileage estimates, leaving the driver in trouble.

[0150] In one aspect, once the system has determined the mileage based on the additional weight or accessories, the system determines possible adjustments and makes recommendations for increasing the calculated mileage. For example, the system may recommend adjusting the tire pressure of the vehicle and attached trailer, the driving speed, an alternative route (e.g., a route with fewer hills), the departure time, or the time to rest due to weather. The system may suggest the placement of items (e.g., putting a bicycle on the top or in the back). The system may recommend adding additional items to reduce wind resistance. If the system detects a planned configuration change and estimates the mileage based on the planned configuration change, such adjustments can be determined before the trip. In one embodiment, some adjustments may include changing the trailer type, or adding additional contour-changing accessories to the trailer before the trailer is attached and the trip begins, or stopping the trip and manually providing such adjustments. In another embodiment, adjustments may be deployed electrically, such as automatically deploying a cover on the trailer. In one embodiment, some adjustments (such as changing the route, changing the tire pressure) may be made while the trip is in progress.

[0151] Figure 1 is a diagram of an example autonomous vehicle with various sensors, actuators, and systems according to one embodiment. The autonomous system includes: various sensors, such as ultrasonic sensors, lidar sensors, radar sensors, etc.; actuators, such as brake actuators, steering actuators, etc.; and various subsystems, such as propulsion systems, steering systems, brake sensor systems, communication systems, etc. Figure 1Depicted as an example system; it is neither limited to the depicted system nor an exhaustive list of sensors, actuators and systems / subsystems and / or features of an autonomous vehicle. In addition, the vehicle shown should not be interpreted as being limited in the arrangement of any sensors, actuators and systems / subsystems depicted. These sensors, actuators and systems / subsystems can be arranged as suitable for the purpose to be performed by the autonomous vehicle. Autonomous vehicles, also known as self-driving vehicles or unmanned vehicles, are vehicles that can navigate and operate without human intervention. Sensors (for example, including cameras, lidar, radar and ultrasonic sensors) enable autonomous vehicles to detect and identify objects, obstacles and pedestrians on the road. Autonomous vehicles use advanced control systems to make real-time decisions based on sensor data and pre-programmed rules or intelligent decision-making systems. For example, these systems control the acceleration, braking, steering and communication of the vehicle. Navigation systems (such as GPS, maps and other location-based technologies) can help autonomous vehicles navigate and plan the best route to a destination. The communication systems of autonomous vehicles can help them communicate with other vehicles and infrastructure (such as traffic lights and road signs) to exchange information and optimize traffic flow. Self-driving vehicles have a variety of safety features, including collision avoidance systems, emergency braking, and backup systems in case of system failure. Self-driving vehicles use artificial intelligence and machine learning algorithms to analyze data, identify patterns, and improve performance over time.

[0152] Figure 2 A block diagram of electronic components of a vehicle according to one embodiment is shown. In the illustrated example, the electronic components include an onboard computing platform 202 , a human machine interface (HMI) unit 204 , a communication module 220 , a sensor 206 , an electronic control unit (ECU) 208 , and a vehicle data bus 210 . Figure 2 Shows Figure 1 An example architecture of some electronic components is shown in .

[0153] The onboard computing platform 202 includes a processor 212 (also referred to as a microcontroller unit or controller) and a memory 214. In the example shown, the processor 212 of the onboard computing platform 202 is configured to include a controller 212-1. In other examples, the controller 212-1 is incorporated into another ECU having its own processor and memory. The processor 212 can be any suitable processing device or group of processing devices, such as but not limited to a microprocessor, a microcontroller-based platform, an integrated circuit, one or more field programmable gate arrays (FPGAs), and / or one or more application-specific integrated circuits (ASICs). The memory 214 can be a volatile memory (e.g., RAM, including non-volatile RAM, magnetic RAM, ferroelectric RAM, etc.), a non-volatile memory (e.g., disk memory, FLASH memory, EPROM, EEPROM, non-volatile solid-state memory based on memristors, etc.), an unchangeable memory (e.g., EPROM), a read-only memory, and / or a high-capacity storage device (e.g., a hard disk drive, a solid-state drive, etc.). In some examples, the memory 214 includes a variety of memories, particularly volatile memories and non-volatile memories. The memory 214 is a computer-readable medium on which one or more sets of instructions, such as software for operating the methods of the present disclosure, may be embedded. The instructions may embody one or more methods or logic described herein. For example, during execution of the instructions, the instructions may reside completely or at least partially in any one or more of the memory 214, the computer-readable medium, and / or the processor 212.

[0154] The HMI unit 204 provides an interface between the vehicle and the user. The HMI unit 204 includes digital and / or analog interfaces (e.g., input devices and output devices) for receiving input from the user and displaying information to one or more users. The input devices include, for example, control knobs, instrument panels, digital cameras for image capture and / or visual command recognition, touch screens, audio input devices (e.g., cabin microphones), buttons, or touch pads. The output devices may include instrument cluster outputs (e.g., dials, lighting devices), tactile devices, actuators, displays 216 (e.g., heads-up displays, center console displays, such as liquid crystal displays (LCDs), organic light-emitting diode (OLED) displays, flat panel displays, solid-state displays, etc.), and / or speakers 218. For example, the display 216, the speaker 218, and / or one or more other output devices of the HMI unit 204 are configured to issue an alert to the operator of the vehicle (e.g., the driver), such as an alert requesting manual takeover. Moreover, the HMI unit 204 of the illustrated example includes hardware (eg, a processor or controller, memory, storage, etc.) and software (eg, an operating system, etc.) of an infotainment system presented via a display 216 .

[0155] Sensors 206 are arranged in and / or around the vehicle to monitor the characteristics of the vehicle and / or the environment in which the vehicle is located. One or more sensors 206 can be installed to measure the characteristics of the external surroundings of the vehicle. Additionally or alternatively, one or more sensors 206 can be installed in the vehicle compartment or body (e.g., engine compartment, wheel well, etc.) to measure the characteristics of the vehicle and / or the internal sensing of the vehicle. For example, the sensor 206 includes an accelerometer, an odometer, a tachometer, a pitch and yaw sensor, a wheel speed sensor, a microphone, a tire pressure sensor, a biosensor, an ultrasonic sensor, an infrared sensor, a light detection and ranging (LIDAR / lidar), a radio detection and ranging system (radar), a global positioning system (GPS), a millimeter wave (mmWave) sensor, a camera, and / or any other suitable type of sensor. In the example shown, the sensor 206 includes a ranging sensor configured to monitor one or more objects located in the area around the vehicle. The sensor may include a distance detection sensor 206-1, such as a LIDAR, a radar, a camera, an ultrasonic sensor, a GPS sensor, etc., to detect the distance between the vehicle and an object or target near it.

[0156] ECU 208 monitors and controls subsystems of the vehicle. For example, ECU 208 is a discrete group of electronic devices that includes one or more circuits (e.g., integrated circuits, microprocessors, memory, storage devices, etc.) and firmware, sensors, actuators, and / or mounting hardware of its own. ECU 208 communicates and exchanges information via a vehicle data bus (e.g., vehicle data bus 210). Additionally, ECU 208 can communicate attributes (e.g., ECU status, sensor readings, control states, errors and diagnostic codes, etc.) and / or receive requests from each other. For example, a vehicle may have dozens of ECUs located at various locations around the vehicle and communicatively coupled via vehicle data bus 210.

[0157] In the example shown, the ECU 208 includes an autonomous / autonomous unit 208-1 and a body control module 208-2. For example, the autonomous unit 208-1 is configured to perform autonomous and / or semi-autonomous driving operations (e.g., defensive driving maneuvers) of the vehicle based at least in part on instructions received from the controller 212-1 and / or data collected by the sensor 206 (e.g., a distance detection sensor). In addition, the body control module 208-2 controls one or more subsystems of the entire vehicle, such as power windows, power locks, anti-theft systems, power mirrors, etc. For example, the body control module 208-2 includes circuits that drive one or more relays (e.g., control wiper fluid, etc.), brushed direct current (DC) motors (e.g., control power seats, power locks, power windows, wipers, etc.), stepper motors, LEDs, safety systems (e.g., seat belt pretensioners, airbags, etc.), etc.

[0158] The vehicle data bus 210 communicatively couples the communication module 220, the vehicle computing platform 202, the HMI unit 204, the sensor 206, and the ECU 208. In some examples, the vehicle data bus 210 includes one or more data buses. The vehicle data bus 210 may be configured in accordance with the controller area network (CAN) bus protocol defined by the International Organization for Standardization (ISO) 11898-1, the media oriented systems transport (MOST) bus protocol, the CAN flexible data (CAN-FD) bus protocol (ISO 11898-7), and / or the K-line bus protocol (ISO9141 and ISO 14230-1), and / or the Ethernet bus protocol. TM It is implemented by bus protocol IEEE 802.3 (after 2002) and so on.

[0159] The communication module 220-1 is configured to communicate with other nearby communication devices. In the example shown, the communication module 220 includes a dedicated short-range communication (DSRC) module. The DSRC module includes one or more antennas, one or more radios, and software for communicating with one or more nearby vehicles via vehicle-to-vehicle (V2V) communication, communicating with one or more infrastructure-based modules via vehicle-to-infrastructure (V2I) communication, and / or more generally communicating with one or more nearby communication devices (e.g., mobile device-based modules) via vehicle-to-everything (V2X) communication.

[0160] V2V communication allows vehicles to share information such as speed, location, direction, and other relevant data, enabling them to cooperate and coordinate their actions to improve safety, efficiency, and mobility on the road. V2V communication can be used to support a variety of applications such as collision avoidance, lane change assistance, platooning, and traffic management. It can rely on dedicated short-range communications (DSRC) and other wireless protocols to enable fast and reliable data transmission between vehicles. V2V communication is a form of wireless communication between vehicles that allows vehicles to exchange information and coordinate with other vehicles on the road. V2V communication enables vehicles to share data about their location, speed, direction, acceleration, and braking with other nearby vehicles, which can help improve safety, reduce congestion, and make transportation systems more efficient.

[0161] V2V communications are typically based on wireless communication protocols, such as Dedicated Short Range Communications (DSRC) or Cellular Vehicle-to-Everything (C-V2X) technology. With V2V communications, vehicles can receive information about potential hazards, such as accidents or road closures, and adjust their behavior accordingly. V2V communications can also be used to support Advanced Driver Assistance Systems (ADAS) and autonomous driving technologies, such as platooning, where a group of vehicles travel closely together using V2V communications to coordinate their movements.

[0162] For more information on DSRC networks and how they communicate with vehicle hardware and software, see the U.S. Department of Transportation's June 2011 Core System Requirements Specification (SyRS) report (available at http: / / wwwits.dot.gov / meetings / pdf / CoreSystemSESyRSRevA%20(2011-06-13).pdf). DSRC systems can be installed on vehicles or on roadside infrastructure. DSRC systems that incorporate infrastructure information are called "roadside" systems. DSRC can be combined with other technologies, such as the Global Positioning System (GPS), visible light communications (VLC), cellular communications, and short-range radar, so that a vehicle communicates its location, speed, heading, relative position with respect to other objects, and exchange information with other vehicles or external computer systems. DSRC systems can be integrated with other systems such as mobile phones.

[0163] Currently, DSRC networks are identified by the DSRC abbreviation or name. However, other names are sometimes used, usually in connection with the Connected Vehicle program, etc. Most of these systems are pure DSRC or variations of the IEEE 802.11 wireless standard. However, in addition to pure DSRC systems, it is also intended to cover dedicated wireless communication systems between vehicles and roadside infrastructure systems that are integrated with GPS and based on IEEE 802.11 protocols for wireless LANs (such as 802.11p, etc.).

[0164] Additionally or alternatively, the communication module 220-2 includes a cellular vehicle-to-everything (C-V2X) module. The C-V2X module includes hardware and software for communicating with one or more other vehicles via V2V communication, communicating with infrastructure-based modules via V2I communication, and / or more generally communicating with nearby communication devices (e.g., mobile device-based modules) via V2X communication. For example, the C-V2X module is configured to communicate with nearby devices (e.g., vehicles, roadside units, mobile devices, etc.) directly and / or via a cellular network. Currently, the Third Generation Partnership Project is developing standards related to C-V2X communications.

[0165] In addition, the communication module 220-2 is configured to communicate with an external network. For example, the communication module 220-2 includes hardware (e.g., a processor, a memory, a storage device, an antenna, etc.) and software to control a wired or wireless network interface. In the example shown, the communication module 220-2 includes one or more communication controllers for cellular networks (e.g., Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE), Code Division Multiple Access (CDMA)), near field communication (NFC), and / or other standard-based networks (e.g., WiMAX (IEEE 802.16m), local area wireless networks (including IEEE 802.11a / b / g / n / ac or others), wireless Gigabit (IEEE 802.11ad), etc.). In some examples, the communication module 220-2 includes a wired or wireless interface (e.g., an assist port, a universal serial bus (USB) port, The vehicle may be coupled to a mobile device (e.g., a smart phone, a wearable device, a smart watch, a tablet, etc.) to communicate with the external network via the coupled mobile device. The one or more external networks may be a public network, such as the Internet; a private network, such as an intranet; or a combination thereof, and may utilize various network protocols now available or later developed, including but not limited to TCP / IP-based network protocols.

[0166] In one embodiment of the system, the communication between the host vehicle and the trailer is via vehicle-to-vehicle (V2V) communication. In one embodiment of the system, the vehicle-to-vehicle (V2V) communication is based on a wireless communication protocol using at least one of dedicated short-range communication (DSRC) and cellular vehicle-to-everything (C-V2X) technology. In one embodiment of the system, the communication between the host vehicle and the trailer is via an Internet connection.

[0167] In one embodiment, the communication module enables autonomous communication, wherein the autonomous communication includes communication conducted over a period of time in different scenarios with minimal supervision. The communication module includes a hardware component, the hardware component includes a vehicle gateway system, the vehicle gateway system includes a microcontroller, a transceiver, a power management integrated circuit, and an Internet of Things device capable of transmitting one of an analog signal or a digital signal via telephone, wired or wireless communication.

[0168] The autonomous unit 208-1 in the illustrated example is configured to perform autonomous and / or semi-autonomous driving operations for the vehicle, such as defensive driving maneuvers. For example, the autonomous unit 208-1 performs autonomous and / or semi-autonomous driving operations based on data collected by the sensors 206. In some examples, the autonomous unit 208-1 is configured to operate a fully autonomous system, a parking assist system, an advanced driver assistance / assistance system (ADAS), and / or other autonomous system(s) for the vehicle.

[0169] ADAS is configured to assist the driver in safely operating the vehicle. For example, ADAS is configured to perform adaptive cruise control, collision avoidance, lane assistance (e.g., lane centering), blind spot detection, rear collision warning (one or more), lane departure warning, and / or any other function (one or more) that assists in operating the vehicle. To perform driver assistance features, ADAS monitors objects (e.g., vehicles, pedestrians, traffic signals, etc.) and develops situational awareness around the vehicle. For example, ADAS utilizes data collected by sensors 206, communication module 220-1 (e.g., from other vehicles, from roadside units, etc.), communication module 220-2 from a remote server, and / or other sources to monitor nearby objects and develop situational awareness.

[0170] In addition, in the example shown, the controller (or control module) 212-1 is configured to monitor the surrounding environment of the vehicle. For example, in order to enable the autonomous unit 208-1 to perform autonomous and / or semi-autonomous driving maneuvers, the controller 212-1 collects data collected by the vehicle's sensors 206. In some examples, the controller 212-1 collects location-based data via the communication module 220-1 and / or another module (e.g., a GPS receiver) to facilitate the autonomous unit 208-1 to perform autonomous and / or semi-autonomous driving maneuvers. In addition, the controller 212-1 (i) collects data from neighboring vehicles (one or more) via the communication module 220-1 and V2V communication and / or (ii) collects data from roadside units (one or more) via the communication module 220-1 and V2I communication to further facilitate the autonomous unit 208-1 to perform autonomous and / or semi-autonomous driving maneuvers.

[0171] In operation, according to one embodiment, the communication module 220-1 performs V2V communication with neighboring vehicles. For example, the communication module 220-1 collects data from neighboring vehicles to identify (i) whether the neighboring vehicle includes an autonomous and / or semi-autonomous system (e.g., ADAS), (ii) whether the autonomous and / or semi-autonomous system is active, (iii) whether a manual takeover request for the autonomous and / or semi-autonomous system has been issued, (iv) lane detection information of the neighboring vehicle, (v) the speed and / or acceleration of the neighboring vehicle, (vi) the (relative) positioning of the neighboring vehicle, (vii) the driving direction of the neighboring vehicle, (viii) the rate of change of the steering angle of the neighboring vehicle, (ix) the size / dimension of the neighboring vehicle, (x) whether the neighboring vehicle is using stability control system(s) (e.g., anti-lock brakes, traction control, electronic stability control, etc.), and / or any other information that helps the controller 212-1 monitor the neighboring vehicle.

[0172] Based at least in part on data collected from neighboring vehicles by the communication module 220-1 via V2V communication, the controller 212-1 can determine a collision probability for the neighboring vehicle. For example, the controller 212-1 determines the collision probability for the neighboring vehicle in response to identifying a manual takeover request in the data collected by the communication module 220-1 from the neighboring vehicle. Additionally or alternatively, the controller 212-1 determines the collision probability for the neighboring vehicle in response to identifying a difference between (i) a lane marking position determined by the controller 212-1 of the vehicle based on the sensor 206 and (ii) a lane marking position determined by the neighboring vehicle. In addition, in some examples, the controller 212-1 determines the collision probability for the neighboring vehicle based on data collected from other sources, such as: the sensor 206, for example, the distance detector sensor 206-1; and / or one or more other sensors of the vehicle; one or more roadside units communicating with the communication module 220-1 via V2I communication; and / or one or more remote servers communicating with the communication module 220-1.

[0173] In some examples, the controller 212-1 determines the collision probability based on a takeover time for the neighboring vehicle and / or a collision time of the neighboring vehicle. For example, the takeover time corresponds to the duration between (1) the neighboring vehicle issuing a request to perform a manual takeover and (2) the operator of the neighboring vehicle manually taking over control of the neighboring vehicle. The controller 212-1 is configured to determine the takeover time for the neighboring vehicle based on measured characteristics of the neighboring vehicle (e.g., speed, acceleration, size, etc.), the operator of the neighboring vehicle (e.g., measured reaction time, etc.), and / or the environment of the neighboring vehicle (e.g., road conditions, weather conditions, etc.). In addition, the collision time corresponds to the time required for the neighboring vehicle to collide with another vehicle (e.g., a third vehicle) and / or an object (e.g., a guardrail, a highway lane divider, etc.) if current conditions remain unchanged.

[0174] Additionally or alternatively, the controller 212-1 is configured to determine a collision time of the neighboring vehicle based on speed, acceleration, direction of travel, distance to an object, steering angle required to avoid an object, rate of change of steering angle, and / or other measured characteristics of the neighboring vehicle collected from the neighboring vehicle by the communication module 220-1 via V2V communication. In addition, the controller 212-1 is configured to determine a collision probability for the vehicle based on a collision probability for the neighboring vehicle.

[0175] When determining the collision probability of the adjacent vehicle and determining that the collision probability does not meet the threshold, the autonomous unit 208-1 autonomously performs (e.g., for ADAS) a defensive driving maneuver to prevent the vehicle from being involved in / involved in a collision caused by the adjacent vehicle. For example, the autonomous defensive driving maneuver includes deceleration, emergency braking, lane change, changing positioning within the current lane of travel, etc. In some examples, the autonomous unit 208-1 is configured to initiate the defensive driving maneuver before the takeover time of the adjacent vehicle is completed. That is, the controller 212-1 is configured to enable the autonomous unit 208-1 to perform a defensive driving maneuver before the operator of the adjacent vehicle manually takes over the control of the adjacent vehicle. In addition, in some examples, the controller 212-1 issues an audio, visual, tactile and / or other alarm (e.g., via the HMI unit 204) so ​​that the operator of the vehicle requests a manual takeover in response to determining that the collision probability is less than a first threshold and greater than a second threshold. By issuing such an alarm, the controller 212-1 enables the operator of the vehicle to safely control the vehicle before the adjacent vehicle may be involved in a collision. Additionally or alternatively, the controller 212 - 1 may be configured to perform other defensive measures (eg, pre-filling the brake fluid lines) in response to determining that the probability of a collision is greater than a threshold (eg, a second threshold, a third threshold).

[0176] The communication module uses vehicle-to-network (V2N), vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), vehicle-to-cloud (V2C), vehicle-to-pedestrian (V2P), vehicle-to-device (V2D), and vehicle-to-grid (V2G) communication systems to achieve in-vehicle communication, communication with other vehicles, infrastructure communication, grid communication, etc. The system then notifies nearby or surrounding vehicles or vehicles communicating with the vehicle's communication module. The vehicle uses, for example, a message protocol so that the message is sent to other vehicles via broadcast.

[0177] In one embodiment, a connection is established between a vehicle and an accessory such as a trailer. The vehicle control system detects the trailer. The trailer is detected by exchanging handshake signals. Handshaking is an automatic process of negotiating the establishment of a communication channel between entities. The processor sends a start signal over the communication channel to detect the trailer or accessory. If there is a trailer or accessory, the processor can receive a confirmation signal from the trailer or accessory. When the confirmation signal is received, the processor establishes a secure connection with the trailer or accessory. The processor can receive a signal from the trailer or accessory at a communication module. The processor can further automatically determine the source of the signal. The processor connects the communication module to the trailer or accessory in communication. The processor is then configured to send messages to the trailer or accessory and / or receive messages from the trailer or accessory. The signal received by the communication module can be analyzed to identify the source of the signal, thereby determining the location of the trailer or accessory.

[0178] In one embodiment, the system enables two-way communication. The system or vehicle sends a signal and then receives a signal / communication from the trailer or attachment.

[0179] As a first step of the method according to the present disclosure, a data link is established between the vehicle and an external device, which may be a trailer or an accessory, to allow data to be exchanged between the vehicle and the trailer or accessory in the form of two-way communication. This can be done, for example, via a radio link or a data line. Thus, the trailer or accessory can receive data from the vehicle, or the vehicle can request data from the trailer or accessory.

[0180] In one embodiment, the two-way communication comprises means for data collection and is designed to exchange data in both directions. Furthermore, at least the vehicle comprises logic means for collecting data and arranging the data into a specific protocol based on the protocol of the receiving entity. Initially, a data link for two-way communication is established. The vehicle and the trailer or attachment can communicate with each other via the data link and thus request or exchange data, wherein the data link can be implemented as a cable link or a radio link, for example.

[0181] Bidirectional communication has various advantages described herein. In various embodiments, data is communicated and transmitted at appropriate intervals, including, for example, 200 millisecond (ms) intervals, 100 ms intervals, 50 ms intervals, 20 ms intervals, 10 ms intervals, or even more frequently and / or in real time or near real time to allow the vehicle to respond to the data or otherwise react to the data. Bidirectional IR communication can be used to facilitate data exchange.

[0182] The apparatus for a vehicle that performs two-way communication according to an embodiment may be by means of a personal area network (PAN) modem. Therefore, a user can access external devices by using a vehicle information terminal, and can store, move, and delete data required by the user.

[0183] In one embodiment, the vehicles may transmit messages via a communication link. It may use any combination of vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), or vehicle-to-infrastructure (V2I) types of communications. In one embodiment, it uses vehicle-to-vehicle (V2V) communications to enable vehicles to wirelessly exchange information (communication), such as information about the vehicle's speed, location, and heading.

[0184] In one embodiment, the messaging protocol includes at least one of Advanced Message Queuing Protocol (AMQP), Message Queuing Telemetry Transport (MQTT), Simple (or Streaming) Text Oriented Messaging Protocol (STOMP), MQTT-S (an extension of open publish / subscribe MQTT), which are widely used in IoT-based technologies and edge networks.

[0185] In one embodiment of the system, the host vehicle is operable to establish communication with the trailer or accessory via the communication module to obtain information about the trailer or accessory, wherein the information includes the dimensions / size and weight of the trailer or accessory. In one embodiment of the system, the information about the trailer or accessory is obtained using a longitudinal accelerometer sensor, GPS, an inertial measurement unit (IMU), a lidar, a radar, and a camera.

[0186] In one embodiment of the system, communication between the host vehicle and the trailer or accessory occurs via vehicle-to-vehicle (V2V) communication. In one embodiment of the system, the V2V communication is based on a wireless communication protocol using at least one of dedicated short-range communication (DSRC) and cellular vehicle-to-everything (C-V2X) technology. In one embodiment of the system, communication between the host vehicle and the trailer or accessory occurs via an Internet connection.

[0187] Figure 3 A block diagram of a system 300 and its components for detecting vehicle configuration changes and estimating mileage according to one embodiment is shown. The system includes a processor 302, a vehicle configuration detection module 304, a mileage estimation module 306, a memory 308, a communication module 310, a user data and vehicle data collection module 312, an analysis and recommendation module 314, an alert signal generation module 316, and a display module 318.

[0188] The processor 302 may be a high-performance, multi-core CPU or a system-on-chip (SoC) solution for processing large amounts of data from various sensors that may be used or present in the vehicle. It processes large amounts of data from sensors such as cameras, LIDAR, radar, and other inputs to make real-time decisions, make recommendations, and perform control actions for the vehicle. Graphics processing units (GPUs) are also utilized for their ability to accelerate tasks such as image and sensor data processing. Some vehicles may employ field programmable gate arrays (FPGAs) to efficiently perform specialized calculations, while other vehicles may utilize application-specific integrated circuits (ASICs) to optimize functionality. The choice of processor is based on various factors, such as the level of autonomy of the vehicle, processing requirements, power consumption, and thermal considerations. The processor, also known as the central processing unit (CPU), is the heart and brain of any computer or electronic device capable of executing instructions. The function of the processor is to process data and perform calculations, among other things. The core of the processor's operation is data processing, and they handle arithmetic and logical operations on data stored in memory. The CPU executes instructions, which are specific sets of operations encoded in machine language, to perform various tasks. A control unit within or interacting with the processor manages and coordinates the execution of instructions, fetches instructions from memory, decodes the instructions, and instructs the appropriate components to execute the instructions. To ensure a controlled and orderly flow of tasks, the processor uses an internal clock to generate regular electrical pulses that synchronize its operations through clock cycles. The processor supports a multitasking environment and can quickly switch between different tasks executing various applications. Additionally, the processor can collaborate with the operating system to manage virtual memory, allowing programs to access more memory than is physically available and effectively manage memory usage. The processor can integrate security features, including hardware-level encryption, memory protection, and support for a secure execution environment, thereby enhancing the security of the system against potential threats. The processor can run complex algorithms and artificial intelligence (AI) software to analyze sensor data, detect obstacles, interpret the environment, and help make decisions to navigate the vehicle. The high-performance capabilities and parallel processing of the processor help ensure that the vehicle can quickly and accurately sense and respond to its surroundings. In one embodiment, the processor can be a neuromorphic processor inspired by the human brain, which provides a unique way to handle AI tasks. The processor interacts and exchanges data with one or more other components or modules of the system, such as Figure 3 Shown are a vehicle configuration detection module 304 , a range estimation module 306 , a memory 308 , a communication module 310 , a user data and vehicle data collection module 312 , an analysis and recommendation module 314 , an alert signal generation module 316 , and a display module 318 .

[0189] The vehicle configuration detection module 304 includes various sensors configured to detect any vehicle configuration changes. Vehicle configuration refers to the specific arrangement and combination of components / assemblies and features in a vehicle. Vehicle configuration includes adding or removing items that can significantly affect the overall performance of the vehicle (including its mileage). Vehicle configuration can include various elements such as engine type, fuel tank capacity, battery capacity (for electric vehicles), weight, aerodynamic (pneumatic) characteristics, tire type, tire pressure and other factors that can significantly affect the overall mileage of the vehicle. Adding weight to the vehicle, such as a trailer, bicycle, boat, additional passengers and / or cargo, will increase the overall mass of the vehicle. In addition, the attachment location of the item, i.e., whether the bicycle is added to the back of the vehicle or added to the top of the vehicle in the trunk, will change the weight, aerodynamic characteristics, friction on the tires or trailer, etc., thereby affecting the vehicle mileage. Similarly, the attachment modes of the trailer to the vehicle include ball hitch, fifth wheel hitch, pivot hitch, gooseneck hitch and receiver hitch; the choice of such attachments can affect the mileage of the towing vehicle, especially for electric vehicles. Factors such as the weight and drag of the trailer, towing capacity, hitch design, terrain, and driving conditions can affect the vehicle's energy consumption and reduce its mileage. Heavier vehicles require more energy to move, resulting in reduced fuel efficiency or increased energy consumption of electric vehicles, thereby reducing mileage. Similarly, modifications to the vehicle design, such as adding a roof rack, spoilers, or other accessories, can have a negative impact on aerodynamics. Poor aerodynamics can increase air drag and cause the vehicle to consume more fuel or energy, thereby reducing the vehicle's mileage. Therefore, the vehicle configuration detection module detects the vehicle configuration, whether there is a trailer attachment, whether a trailer is added, whether there is a luggage rack, whether more luggage is added to the luggage rack or elsewhere in the vehicle, whether additional items are added, whether there are more passengers, whether there are bicycle attachments, etc.

[0190] In one embodiment of the system, the vehicle configuration is determined via a computer vision module including an artificial intelligence engine, wherein the artificial intelligence engine includes a machine learning algorithm. In one embodiment of the system, when determining the vehicle configuration, the system can activate a camera of the computer vision module to record the surrounding environment of the vehicle.

[0191] In one embodiment, the vehicle configuration detection module first estimates the mileage based on its original or default configuration. In this process, the system may assume a default number of passengers based on historical data, destination, origin, etc. If the configuration is changed by adding or removing items, passengers, or any other equipment that affects the mileage, the vehicle will detect and re-estimate the new mileage. The new mileage is displayed on the infotainment system along with an alert signal. At the beginning of the trip, that is, at the origin, the detection of the configuration change is performed by using all the data available at that time. The input to the mileage estimation model may include parameters that affect the future mileage, such as picking up a trailer at the XYZ stopover. In one embodiment, the system may ask the user via an application (App) on the infotainment system or mobile phone to confirm its input to the mileage estimation model, for example, "Are you picking up a trailer at this location XYZ?" Once the user confirms, the estimated mileage will be displayed. Once the vehicle begins to move, the system will continue to monitor any changes in the configuration. If a new stopover, route change, vehicle load change, weather change, etc. are found, the system can recalibrate the mileage and provide new mileage information on the display screen of the infotainment system.

[0192] In one embodiment, a trailer identification system for a vehicle is disclosed. The system includes an imaging device and a user interface, the imaging device being configured to capture image data depicting a trailer, and the user interface being configured to communicate with a user. A processor processes the image data and applies a trailer detection model, the trailer detection model being configured to detect the trailer depicted in the image data. The processor initiates a detection module for the trailer and captures image data of the trailer from multiple perspectives relative to a trailer heading of the trailer so that the size and shape of the trailer can be captured. According to one embodiment, it is a trailer detection method for a vehicle trailer detection system, the method comprising the steps of: identifying a trailer in image data via a trailer detection model, initiating a detection routine for the trailer; capturing image data of the trailer from multiple perspectives relative to a trailer heading of the trailer, and generating a size and shape estimate of the trailer.

[0193] There are several ways to detect if a vehicle has a trailer attached or if there is a plan to attach one. The detection can be done using a user's schedule and / or using a vision system that includes an image sensor. The image sensor can be mounted on the rear of the towing vehicle or on a rearview mirror. The system can also determine the length, width, height, and shape of the trailer. In addition, the system can estimate the weight of the system based on material, shape, and volume.

[0194] For example, if someone is going camping and wants to know what the vehicle mileage estimate will be, the system can provide specific mileage numbers through an app or an in-vehicle infotainment system using a display. If the system knows based on the user's schedule that the user is going camping and is taking a trailer, the system will attempt to obtain information about the trailer. Information about the trailer can be the size of the trailer, including the dimensions of height, width, and length, the mode of attachment, the number of wheels in contact with the road, whether the trailer is covered or uncovered, the shape of the trailer, the weight of the trailer, whether the trailer is directly attached or carried on an additional trolley, etc. With the information about the trailer, the system will provide mileage estimates with and without the trailer. The system can also provide a stop schedule for charging or refueling.

[0195] In one embodiment, the size and shape of the trailer may be detected using various sensors strategically placed in and on the vehicle.

[0196] Figure 4A A system with a vehicle configuration detection module and a vehicle parameter estimation module according to one embodiment is shown. The system includes an imaging sensor 402, a proximity sensor 404, and an electrical connection sensor 406, which are positioned to detect a trailer or any item of interest added to the vehicle. The vehicle configuration detection module 408 may include a computer vision system and use sensor fusion technology to analyze inputs from one or more sensors and determine the current configuration. Based on the configuration, the imaging sensor 402, the proximity sensor 404, and the electrical connection sensor 406 are also used to collect more data. There may be one or more sensors of the same type placed in various locations to collect data on items or accessories / trailers. Once the data is collected, the accessory / item parameter estimation module 410 (including various artificial intelligence algorithms or data-based models) can estimate the parameters of the item. The accessory / item details 412 are provided by the estimation module. These details or parameters may include, but are not limited to, size (including length, height, width or breadth), shape, weight, color, and other details (as needed).

[0197] Radar and LiDAR can be proximity sensors capable of detecting nearby objects, including trailers, but they need to be configured and positioned correctly on the vehicle. Radar sensors use radio waves to detect objects, and when a trailer is attached to the vehicle, the radar can detect the trailer as a separate object within the detection range. Based on distance and speed, the trailer can be associated with the vehicle itself. Similarly, when a trailer is attached to the vehicle, LiDAR can detect the trailer as an additional object within the LiDAR's field of view. LiDAR can accurately capture the shape and position of the trailer, providing valuable data for the vehicle's perception and decision-making systems. In one embodiment, cameras, radar, and LiDAR sensors can work together to ensure that the vehicle is able to detect and identify objects, such as a trailer attached to the vehicle.

[0198] When a trailer is attached to a vehicle, it becomes an extension of the vehicle itself. Ultrasonic sensors, located on the rear bumper or side of the vehicle, can detect the presence of a trailer and the distance between the vehicle and the trailer. These sensors emit ultrasonic pulses and measure the time it takes for the pulse to bounce back after hitting an object. By analyzing the time delay, the sensor can calculate the distance to the trailer.

[0199] Vehicles are often equipped with trailer hitches to enable the vehicle to connect to and tow a trailer. In many trailer coupling arrangements, the trailer has a coupler that engages and connects to the vehicle's trailer coupler, typically via a coupling ball. The coupler often includes a coupler lock that is activated by a user operating a coupler lock lever between locked and unlocked positions. In one embodiment, a sensor is placed near the coupler such that the sensor is operable to detect whether the vehicle and trailer are in a connected state. According to one embodiment, a vehicle includes a trailer hitch, a vehicle electrical connector configured to electrically connect to a trailer electrical connector, and an imaging system adapted to capture images near the trailer hitch and images of the trailer.

[0200] In one embodiment, a capacitance-based sensor is used. A common implementation of a capacitance-based sensor for trailer detection is to use an electrical connector. Many trailers have electrical connectors that plug into corresponding sockets on the towing vehicle. These connectors are used to transmit electrical signals for lighting and other systems on the trailer.

[0201] Capacitive sensors can detect the presence of a connected trailer by monitoring changes in capacitance when an electrical connector is plugged in or disconnected. When a trailer is attached, the vehicle's electrical system and the trailer are electrically connected, causing a change in capacitance. The sensor can detect this change and determine that a trailer is connected.

[0202] In one embodiment, a millimeter wave radar sensor can be used to detect a trailer attached to a vehicle, especially when the trailer has sufficient reflective surfaces (e.g., metal parts). Such metal reflective surfaces can be set on the trailer for detection purposes. Millimeter wave radar sensors operate by transmitting electromagnetic waves in the millimeter wave frequency range and then measuring the time required for the waves to bounce back after hitting an object. The radar sensor can analyze the reflected signal to detect and track objects near the vehicle.

[0203] Once the trailer is detected, information such as the size, shape, weight, etc. of the trailer can be obtained through radio frequency identification chip (RFID) technology. In one embodiment, details of the trailer, such as size, shape, height, etc., can be estimated via one or more images captured using one or more cameras. To enhance the overall object detection and object geometry estimation capabilities, the system can use a combination of different sensor technologies, including cameras, radar, and lidar. This sensor fusion approach allows the system to leverage the strengths of each sensor type and provide a comprehensive perception of the detection of the attached trailer, as well as further details of the trailer related to shape, size, height, weight, color, model, brand, etc.

[0204] According to one embodiment of the method, the configuration change includes adding an item to the vehicle. According to one embodiment of the method, the configuration change includes removing an item from the vehicle. According to one embodiment of the method, the configuration change is detected using RFID technology, wherein an RFID tag including information about the size, shape, and weight of the item is associated with the item, and an RFID reader is associated with the vehicle.

[0205] In one embodiment, the trailer may be detected using a bounding box method. In one embodiment, components of the vehicle are detected using a bounding box method. For example, the results of the bounding box method are used to detect the frame of the trailer. In one embodiment, the computer vision system may use both 2D and 3D methods in a hierarchical manner, first quickly selecting a region of interest, and then attempting to use a 3D bounding box to gain more insights about the object.

[0206] Sensor fusion is the process of combining data from multiple sensors to improve the accuracy, reliability, and efficiency of the information collected. It involves integrating information from various sources (e.g., cameras, radar, lidar, and other sensors) to obtain a more complete and accurate picture of the environment. Sensor fusion can reduce the errors and uncertainties that can arise from using a single sensor and provide a more complete understanding of the world around us. By combining data from multiple sensors, vehicle systems can make more intelligent decisions and respond to changing conditions in real time. The sensor fusion process typically involves several steps, including data acquisition, signal processing, feature extraction, data association, and estimation. Depending on the application and the specific sensors used, different algorithms and techniques can be used to integrate information from multiple sensors.

[0207] According to one embodiment of the system, the detection module includes one or more sensors. According to one embodiment of the system, the one or more sensors include a magnetic sensor, a proximity sensor, a load sensor, an electrical sensor, and a visual sensor. According to one embodiment of the system, the one or more sensors include a camera coupled to a computer vision system. According to one embodiment of the system, the one or more sensors are mounted on a vehicle so that the one or more sensors detect changes in vehicle configuration by detecting changes in sensor data. According to one embodiment of the system, the one or more sensors are configured to determine one or more of the size, shape, and weight of an object. According to one embodiment of the system, the detection module is operable to: process sensor data; analyze pattern; determine the presence of an object; and determine one or more of the size, shape, and weight of an object.

[0208] According to one embodiment, AI-based trailer detection can be used in conjunction with sensor fusion techniques. Some algorithms suitable for trailer detection include, but are not limited to: (i) Hough Transform: The Hough Transform algorithm is a method for object detection that works by identifying straight lines in an image. The Hough Transform can be used to detect the edges of a trailer and estimate the shape and size of the trailer. (ii) Convolutional Neural Network (CNN): CNN is a deep learning algorithm widely used for trailer detection. The neural network is trained on trailer data obtained from various sensors to identify features of the trailer, such as the size, shape, and texture of the trailer. In one embodiment, the neural network is trained to estimate the weight of the trailer based on size and shape. Once the network training is complete, it can be used to detect and classify trailers in real time. (iii) Support Vector Machine (SVM): SVM is a machine learning algorithm that works by learning the boundaries that separate the trailer from the rest of the image. The SVM algorithm can be trained using labeled data to create a model. The model is then used to detect the size and shape of the trailer. (iv) Markov Random Field (MRF): MRF is a probabilistic graphical model that recognizes the size and shape of trailers by modeling the relationship between neighboring pixels in an image. MRF can be combined with other algorithms (such as CNN or SVM) to create a more powerful and accurate trailer detection system.

[0209] According to one embodiment of the system, the system further comprises one or more sensors. According to one embodiment of the system, the one or more sensors comprise a magnetic sensor, a proximity sensor, a load sensor, an electrical sensor, and a visual sensor. According to one embodiment of the system, the processor is further operable to sense vehicle configuration changes via the one or more sensors. According to one embodiment of the system, the one or more sensors comprise a camera coupled to a computer vision system. According to one embodiment of the system, the one or more sensors are mounted on the vehicle such that the one or more sensors detect vehicle configuration changes by detecting changes in sensor data. According to one embodiment of the system, the one or more sensors are operable to determine one or more of a size, shape, and weight of the one or more items.

[0210] According to an embodiment of the system, the system is further configured to detect additional factors. According to an embodiment of the system, the additional factors include one or more of driving conditions and driving patterns. According to an embodiment of the system, the system includes a non-volatile memory configured to store details of the one or more items, the vehicle and the new mileage.

[0211] Figure 4BDetailed description of obtaining an item or accessory from the item itself is shown in accordance with one embodiment. Figure 4B As shown, details of accessories and items can be obtained directly from the item or accessory itself. The item can be a trailer and can be equipped with a radio frequency identification chip (RFID). The RFID can contain identification and additional details related to the item or accessory, such as size, shape, weight, color, equipped technology, etc. The RFID tag or transponder attached to the item or trailer contains a unique identifier and sometimes other data. These tags are equipped with an antenna that uses radio frequency signals to communicate with an RFID reader. When the RFID tag enters the range of an RFID reader attached to the vehicle, the reader emits radio waves to power the tag so that the tag can transmit its unique identifier back to the reader. The RFID reader then captures the transmitted data and can retrieve additional details or information associated with the specific tag from a database or information system. The details can be password protected, and the password can be sent to the user by the RFID tag owner. In one embodiment, the RFID can point to a website from which additional details can be obtained.

[0212] Figure 4C The method of obtaining details of an item or accessory from a cloud network according to one embodiment is shown. According to this embodiment, an RFID tag can be used to obtain an identification number. Using the identification number, the system can use a cloud network that can store information to obtain details related to the item or accessory.

[0213] According to one embodiment of the method, the detection module is operable to establish a communication link between the vehicle and the item via the communication module; and retrieve the characteristics of the item via the communication link. According to one embodiment of the method, the detection module is operable to establish a communication link between the vehicle and the item via the communication module; detect a configuration change due to the addition of the item; retrieve an identification of the item via the communication link; and retrieve the characteristics of the item via the cloud network using the identification of the item via the communication link.

[0214] According to one embodiment of the system, the detection module is operable to detect the item via a communication module of the vehicle, wherein the communication module establishes a communication link between the vehicle and the item. According to one embodiment of the system, the communication link is a wireless connection. According to one embodiment of the system, the communication link is a wired connection. According to one embodiment of the system, the detection module retrieves the characteristics of the item directly from the communication link between the vehicle and the item. According to one embodiment of the system, the detection module retrieves an identification number of the item from the communication link between the vehicle and the item, and further retrieves the characteristics of the item from the cloud network using the identification number. According to one embodiment of the system, the characteristics of the item include one or more of the weight of the item, the brand and model of the item, the shape of the item, the size of the item, and the attachment mode of the item. According to one embodiment of the system, the communication module supports a communication protocol, wherein the communication protocol includes one or more of Hypertext Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), WebSocket, Constrained Application Protocol (CoAP), and Advanced Message Queuing Protocol (AMQP).

[0215] Figure 4D Detection of an attachment or trailer using a communication module according to one embodiment is shown. One of the purposes of attaching a trailer is to increase the overall cargo capacity of a vehicle to allow the vehicle to transport a larger or heavier load than it can carry on its own. A trailer typically has wheels and can be single-axle or multi-axle depending on the size and weight capacity of the trailer. A trailer can be of open or closed design, and the shape and size of its cargo area can vary based on the intended use. Trailers are typically used to transport items such as furniture, appliances, building materials, boats, livestock, motorcycles, or other vehicles. The attachment process includes connecting the trailer's hitch to a receiver of the vehicle's towing connector. The hitch is a mechanical coupling that enables the trailer to be securely linked to the tractor. The trailer may also have an electrical connector that synchronizes the lights and brakes on the trailer with the lights and brakes of the tractor.

[0216] According to one embodiment, a system for attaching a trailer to a vehicle may include a coupler 422 that connects a vehicle 422-1 and a trailer 422-2. The coupler 422 may also include a safety chain 424, a trailer wiring harness 426, and a communication connection 428. The safety chain 424 is configured to prevent the trailer from completely separating from the vehicle if the trailer becomes disconnected while towing. The trailer wiring harness 426 is an electrical wire at the rear of the vehicle that allows the vehicle's electrical system to connect to the trailer and is configured to power the trailer lights and synchronize them with the vehicle's tail lights. The trailer wiring harness 426 may also be configured to connect trailer brakes and an auxiliary power source. The communication connection 428 may be a communication connection that connects the vehicle 434 and the trailer 436 in a manner such as Figure 4E The wired connection 430 shown may be a wireless communication system that communicatively connects the vehicle 434 and the trailer 436, such as Figure 4F Wireless communication 432 is shown. Figure 4E and Figure 4F Further shown is a mechanical coupler 422 connecting the vehicle 434 and the trailer 436. Wireless communication includes various technologies that allow data to be transmitted without physical wires or cables. According to one embodiment, wireless communication includes Cellular communication, radio frequency identification (RFID), near field communication (NFC), satellite communication. These wireless technologies enable seamless connection and data sharing.

[0217] According to an embodiment of the system, the article comprises a physical connection, an electrical connection, and a communication connection. According to an embodiment of the system, the physical connection comprises a physical coupling between the article and the vehicle. According to an embodiment of the system, the electrical connection comprises an electrical coupling between the article and the vehicle. According to an embodiment of the system, the communication connection comprises at least one of a wired communication and a wireless communication between the article and the vehicle.

[0218] According to an embodiment of the system, the one or more items include a physical connection, an electrical connection, and a communication connection. According to an embodiment of the system, the physical connection includes a physical coupling between the one or more items and the vehicle. According to an embodiment of the system, the electrical connection includes an electrical coupling between the one or more items and the vehicle. According to an embodiment of the system, the communication connection includes at least one of a wired communication and a wireless communication between the one or more items and the vehicle.

[0219] According to one embodiment of the system, the estimation module includes a machine learning algorithm. According to one embodiment of the system, the estimation module includes historical data collected in mileage estimation based on similar item categories, wherein the similar item categories are based on comparison factors among size factors, shape factors, and weight factors.

[0220] Range estimation module 306: In one embodiment, range estimation is performed using a model-based engineering approach. Model-based range estimation involves using mathematical models to predict the range of a vehicle under various driving conditions. Figure 5A 306 may accept various inputs and feed them into a mileage estimation model, which may generally involve multiple variables representing vehicle characteristics, driving conditions, energy consumption, and other relevant parameters that affect mileage. For example, a general equation or model for model-based mileage estimation may be viewed as:

[0221] Mileage (expressed in distance units, such as miles or kilometers) = f(vehicle parameters 502, driving conditions 504, energy consumption 506, passenger load 508, trailer load 510, trailer type 512, trailer aerodynamics 514, trailer weight 516, additional load 518, auxiliary systems 520, attachment mode 522, hitch type 524, trailer cover 526, driving mode 528)

[0222] Where: f(…) represents a mathematical function that calculates mileage using various parameters;

[0223] Vehicle parameters 502 include variables such as vehicle weight, aerodynamic characteristics, tire characteristics, engine or motor specifications, transmission efficiency, and auxiliary system consumption (e.g., heating, ventilation, and air conditioning (HVAC));

[0224] Driving conditions 504 include parameters such as driving speed, road conditions, terrain (uphill / downhill), driving style (e.g., city, highway, stop-and-go traffic), traffic conditions (traffic jam, light traffic, no traffic), and environmental conditions (e.g., temperature, wind, humidity);

[0225] Energy consumption 506 represents the specific energy source used by the vehicle, such as gasoline, diesel, or electricity for electric vehicles. Energy consumption 506 includes factors such as fuel or battery efficiency, regenerative braking (for electric vehicles (EVs)), specific fuel consumption (for internal combustion engine vehicles) (i.e., fuel consumed per unit power produced by the engine) or energy consumption rate (for electric vehicles) (i.e., energy consumed per unit distance traveled (miles / kWh)).

[0226] Passenger load 508 includes the total number of passengers in the vehicle and the average weight of each passenger.

[0227] Trailer load 510 includes the weight of the trailer, bicycle, or boat being towed, including cargo or equipment.

[0228] Trailer types 512 include various types of trailers (eg, enclosed cargo trailers, flatbed trailers, boat trailers) having different aerodynamic characteristics, frontal areas, and shapes.

[0229] Trailer dynamics characteristics 514 include parameters that account for the effect of the aerodynamics of the trailer or towed attachment on the overall drag of the vehicle-trailer system.

[0230] Trailer weight 516 includes the weight of the trailer itself, excluding the load.

[0231] Additional load 518 includes any additional load or equipment added to the vehicle besides the trailer, such as a roof rack, cargo racks, or additional accessories.

[0232] Auxiliary systems 520 include parameters for energy consumed by various auxiliary systems in the vehicle, such as air conditioning, heating, entertainment systems, etc.

[0233] The attachment mode 522 includes parameters that take into account how the trailer is attached to the vehicle. For example, how the trailer is attached to the vehicle may be "bumper mounted," "frame mounted," or "gooseneck mounted."

[0234] Hitch type 524 includes the type of hitch used to connect the trailer. Common hitch types include "ball hitch", "fifth wheel hitch", "pivot hitch", etc. Hitch includes variables for defining all hitch categories, such as Class 1 to Class 5 commercial hitches.

[0235] Trailer cover 526 includes parameters for defining whether a trailer is covered or uncovered. Trailer cover 526 can be represented as a binary variable (1 for covered, 0 for uncovered). Trailer cover 526 can also include other identifiers related to the cover, such as material, hardness, shape, etc.

[0236] The driving mode 528 includes the selected driving mode, which may include options such as “economy mode”, “sport mode”, “normal mode”, etc. Different driving modes require different performance characteristics, such as throttle response, engine output, and transmission settings of the vehicle.

[0237] The range estimation module 530 (similar to the range estimation module 306 ) can obtain all of the above parameters from the vehicle and attached items, and then predict or estimate the range and provide an estimated range 532 .

[0238] The actual form of the equation and the specific variables used may depend on the complexity of the model and the level of detail required for accurate range estimation. According to one embodiment, a complex model may be used and may include other parameters such as the battery state of charge of the electric vehicle or the effects of different driving modes (e.g., economy mode, sport mode), etc.

[0239] According to one embodiment, an accurate model for mileage estimation can be developed, extensive data collection, preprocessing, analysis, model building, and validation against real travel / driving data. The data can then be regressed and presented in the form of a regression equation with all relevant parameters (input variables). Once the model is prepared, it can be validated and trained, and can be further evaluated in the real world. After achieving the desired accuracy and precision, the model can be deployed on a vehicle system.

[0240] In one embodiment, the system may include an aerodynamic model. In one embodiment, the system may include a regression model based on data from the aerodynamic model. In another embodiment, the system may include a regression model based on data collected from similar trailers, items, vehicles in similar driving conditions and weather conditions, etc.

[0241] In one embodiment, the model may be deployed on the system. In another embodiment, the model may be deployed on the cloud. In one embodiment, the model may be based on data collected in various scenarios and different vehicles. In another embodiment, the model may include physics-based equations.

[0242] According to one embodiment of the system, the estimation module includes energy consumption rate values ​​recorded during one or more previous operating cycles. According to one embodiment of the system, the estimation module derives the mileage estimation factor from a cloud network, wherein the cloud network includes historical data related to the mileage caused by the one or more items. According to one embodiment of the system, the new mileage is based on a regression of historical data on the impact of the mileage due to the one or more items. According to one embodiment of the system, the new mileage is based on the size, shape and weight of the one or more items. According to one embodiment of the system, the new mileage takes into account one or more of driving conditions, vehicle load, terrain and driving style. According to one embodiment of the system, the new mileage is estimated at the origin. According to one embodiment of the system, the new mileage is estimated when a deviation from a planned vehicle configuration change is detected. According to one embodiment of the system, the system also includes displaying the current mileage and the new mileage on the vehicle's infotainment system.

[0243] In one embodiment, the system monitors configuration changes and then determines a new range. When the configuration of the vehicle affects the range, the new range will be different from the previously estimated range. In one embodiment, the equation for range can be a composite equation formed from various models to provide range estimates for a single or a group of variables / factors. In one embodiment, the parameters or factors can be weighted based on their contribution and importance to the range. In determining the new range (which can also be referred to as driving range), weights can be provided for various items (such as trailers, passengers, other loads, driving conditions, etc.).

[0244] According to one embodiment, the estimation module is intelligent and adaptive, and is therefore an adaptive intelligent driving range estimation module. According to one embodiment of the system, the adaptive intelligent driving range estimation is configured to take into account one or more of driving conditions, terrain, weather, driving habits, historical data, real-time inputs to vehicle systems, and current status. According to one embodiment of the system, the adaptive intelligent driving range estimation is configured to modify the system to extend the new driving range based on one or more of driving conditions, terrain, weather, driving habits, historical data, real-time inputs to vehicle systems, and current status. According to one embodiment of the system, the system is configured to provide an upper limit and a lower limit for the new driving range.

[0245] The memory 308 may be non-volatile memory (NVM), which is critical to the reliable operation of the system to ensure that important data is preserved even during power outages or failures. Various NVM technologies may be used, such as flash memory for storing operating systems and software, EEPROM for retaining configuration data, calibration values, and sensor settings, ferroelectric RAM (FRAM) for critical real-time information, and emerging technologies such as ReRAM, which potentially improves performance due to its high-speed operation and low power consumption.

[0246] In one embodiment, the memory may be cloud memory. In another embodiment, the memory may be local memory. In another embodiment, the memory may be a combination of local memory and cloud memory. Local memory refers to traditional memory components present in physical devices, such as RAM, hard disk drive (HDD) or solid state drive (SSD) of a computer, etc. Local memory provides fast access to data and is directly connected to the device, making it suitable for instant processing tasks and offline use. On the other hand, cloud memory relies on remote servers and services provided by third-party cloud providers to store and manage data over the Internet. The system can access its data from anywhere with an Internet connection, thereby achieving seamless collaboration and extensibility. Cloud memory is often used to store large amounts of data, enable data sharing, and provide backup and disaster recovery solutions. The combination of local memory and cloud memory allows data to be managed flexibly and efficiently to meet the different needs of the system.

[0247] According to one embodiment of the system, the estimation module includes a machine learning algorithm. According to one embodiment of the system, the estimation module includes historical data collected in mileage estimation based on similar item categories, wherein the similar item categories are based on comparison factors among size factors, shape factors, and weight factors. According to one embodiment of the system, the estimation module includes energy consumption rate values ​​recorded during one or more previous operating cycles. According to one embodiment of the system, the estimation module derives the mileage estimation factor from a cloud network, wherein the cloud network includes historical data due to items affecting mileage. According to one embodiment of the system, the new mileage is based on a regression of the historical data due to items affecting mileage. According to one embodiment of the system, the new mileage is based on the size, shape, and weight of the item. According to one embodiment of the system, the estimation module also takes into account one or more of driving conditions, vehicle load, terrain, and driving style to estimate the new mileage.

[0248] The function of the communication module 310 is similar to that of the Figure 2 The communication module 220 described above. In addition, the communication module 310 also facilitates communication between different modules within the system, communication between objects such as trailers and vehicles, etc.

[0249] Communication between vehicle and accessory: In order for the system to be aware of the accessory, a communication connection is established between the vehicle and trailer to negotiate or transfer information. A new connection is established between the vehicle and trailer that does not exist at this moment and communication back and forth begins.

[0250] In one embodiment, the connection may be a physical connection or a wired connection. In another embodiment, the connection is a wireless connection. In one embodiment, the connection is established immediately once the trailer is attached. In another embodiment, the user may press a button or select an option on the infotainment system to establish the connection after the trailer is attached. The prior art does not disclose any communication between the trailer and the vehicle.

[0251] In one embodiment, the trailer has a wireless communication module that is activated when the trailer is connected to the vehicle. The communication module is configured to communicate information about the trailer, such as trailer weight, trailer payload, trailer dimensions, etc., with the vehicle, and the vehicle will then calculate the total mileage taking into account the trailer attachments.

[0252] In one embodiment, the system detects the attachment of a trailer or additional items. In addition, the system also retrieves information about the specific trailer or additional items using a communication module. In one embodiment, the communication is via a physical connection, such as a wired communication. In one embodiment, the communication is via a wireless communication. For example, if a vehicle is connected to a trailer of very large size, the route must be modified because there may be places where the vehicle cannot pass, which may be too narrow. If something is attached as a trailer and the trailer height is much higher than the height allowed on the path, the route must be changed. Initially, without a trailer, the system can suggest a route. The system will then take into account trailer attachments, trailer size and height, and re-evaluate if there are any restrictions on the route. If the route is found to be not suitable for the trailer height or size, the system can suggest a new route. In one scenario, the vehicle detects whether the cargo it is transporting is classified as hazardous materials (HazMat). The system also verifies whether the cargo and the selected transportation route meet regulatory requirements. If a route is found to be unsuitable for transporting hazardous materials, the system calculates an alternative route that meets hazardous materials regulations.

[0253] Figure 5B An example communication message format according to one embodiment is shown. In order to prepare a compact message for conveying trailer details, a binary encoding scheme may be used, wherein each item is assigned a specific number of bits. The number of bits assigned to each item will depend on the range and precision required for that particular attribute. Figure 5B An example field and bit allocation according to one embodiment is shown. In this example, each field in the message is allocated a specific number of bits.

[0254] The Vehicle Identification Number (VIN) field uses 32 bits (32 characters, each represented by 8-bit ASCII code) to represent the vehicle identification number, which is a unique identifier for the trailer. The Manufacturer field uses 48 bits (6 characters, each represented by 8-bit ASCII code) to specify the manufacturer's name. The Model field uses 48 bits (6 characters, each represented by 8-bit ASCII code) to specify the model name of the trailer.

[0255] The Trailer Dimensions field uses 8 bits (4 bits for length and 4 bits for width) to indicate the dimensions of the trailer in meters or feet. The Trailer Shape field uses 4 bits to indicate a shape identifier, where different shape codes can be assigned to various trailer shapes (e.g., rectangular, round, etc.). The Trailer Weight field uses 12 bits to indicate the weight of the trailer in kilograms to accommodate a range of weight values.

[0256] The time value field uses 32 bits to represent a timestamp using the Unix Epoch format to indicate when the message was generated. Reserved bits are bits that are reserved for possible future use or for additional attributes that may be added to the message format at a later time.

[0257] The message fields and bit allocations are presented as examples and hypotheticals only for demonstration purposes. In an embodiment, the fields, actual message format, and number of bits allocated to each item may vary based on the specific requirements and constraints of the application and communication protocol used.

[0258] Based on specific application requirements and scope considerations, various communication protocols can be used to transmit trailer information. and Suitable for short-range communications in close proximity, while cellular communications utilizing 3G, 4G or 5G networks allow data to be transmitted over longer distances. RFID is useful for contactless identification or tracking purposes, while CAN can be used for onboard communications between trailers and vehicles. V2X protocols include V2V and V2I communications, enabling vehicles to share data with each other and with infrastructure units. DSRC is designed for V2V and V2I scenarios and facilitates short-range communications of trailer details. The choice of communication protocol is based on factors such as range, data transfer rate, power consumption, security and existing infrastructure, as well as the specific use case for trailer details communication.

[0259] Figure 5C An example message exchange between a trailer and a vehicle according to one embodiment is shown. The message exchange may include elements related to the trailer and its parameters or data. The message exchange may include trailer identification number, trailer model, trailer brand, and trailer details, including unladen weight, maximum load that can be carried, size, shape, hinge type or coupling mode, wheelbase, tire size, coupler type, drag coefficient, and a list of additional accessories. In one embodiment, the message can be customized based on basic information including size, shape, and weight. In one embodiment, the message can be sent to the vehicle at the time of attachment. In one embodiment, the message can be sent to the application when booking a trailer. In one embodiment, the message can be in an RFID tag and can be read when a reader scans the RFID tag.

[0260] Once data from a trailer is received or a trailer is detected, the system can take this new configuration of the vehicle into account and estimate the range. Figure 5DExample messages displayed on an infotainment system of a vehicle are shown according to one embodiment. In one embodiment, an estimated driving range without and with a trailer is displayed on the infotainment system. In one embodiment, an upper and lower limit of the driving range is estimated and displayed. The upper and lower limits can be obtained by taking into account changes in real-time conditions and adaptive adjustments. In addition, the alert message can include a notification to the user whether the new driving range can still take the user to their destination or whether they need to charge. In one embodiment, the system can schedule a charging appointment before the driving range drops to a minimum level. In another embodiment, the system can re-plan the route to reach the destination within the available driving range as much as possible. In all embodiments, the infotainment system notifies the user via displayed messages and alert signals. In one embodiment, the infotainment system can obtain input from the user.

[0261] According to an embodiment of the method, the method further comprises displaying the previously estimated mileage and the new mileage on an infotainment system of the vehicle. According to an embodiment of the method, the new mileage is based on current driving data, historical driving data, and historical mileage related data. According to an embodiment of the method, the method further comprises detecting environmental information around the vehicle. According to an embodiment of the method, the method further comprises adjusting the new mileage based on the environmental information around the vehicle.

[0262] According to one embodiment of the system, the system further comprises displaying the previously estimated driving range and the new driving range on an infotainment system of the vehicle. According to one embodiment of the system, the configuration change comprises an initial configuration of the vehicle at the origin, which affects the previously estimated driving range. The vehicle configuration change involves a situation where the initially estimated driving range for the first vehicle configuration is different from the driving range estimated for the current configuration. Thus, the driving range itself undergoes a modification due to the configuration transition, thereby indicating that a configuration change has occurred in the vehicle.

[0263] According to one embodiment of the system, the system is further configured to detect additional factors. According to one embodiment of the system, the additional factors include: route conditions, road conditions, weather conditions, and traffic conditions. According to one embodiment of the system, the system includes a non-volatile memory configured to store the following details: item details, including item identification details, item model, item brand, item size, item shape, item weight, item details such as sensors, etc.; vehicle details, including vehicle model, vehicle brand, vehicle health parameters, tire pressure, etc.; previous cycle / period energy consumption details, including route, load, origin, target location, stopover, and energy consumed in the trip related to the previous trip, etc.; current trip details, including origin, stopover, destination, number of passengers in the trip, driver details, charging / refueling details, and new mileage. Data is stored during and after each trip. The data can be stored in local storage, cloud, or a combination of local storage and cloud. The system can initially store all details in local storage and can upload all details to the cloud at the end of the trip. In one embodiment, the stored details may be further separated and available for each journey or each segment in a journey. The system may retrieve data when it must determine the category of an item based on previously stored information. The system may retrieve data when it must determine the identification of an item and classify the size factor, form factor, or weight factor based on previously stored information for various items in the database.

[0264] User Data and Vehicle Data Collection Module 312: User data may be collected from any device owned or used by the user. Prior permission may be obtained to access such data or portions of the data. In one embodiment, the vehicle system may sync with the user's other devices to access the data. User data may include calendar appointments, pick-up and drop-off schedules, vacation plans, purchases or rentals of vacation accessories such as boats, bicycles, RVs, etc. User data may include data from other devices (e.g., Amazon ) for reminders or scheduling. User data may include speech processing assisted by natural language processing algorithms that process conversations that users have over their phones. This user data is used to understand the user’s intentions, plans, and any other data to understand the user’s travels, routes, and places of stay. For example, a user may talk to a friend and say that he will pick him up on the way to go boating. The user may also describe that he must pick up the boat before he picks up his friend. The system then processes this conversation using an AI-based language model to understand the user’s origin point, how many passengers are at the origin point, the vehicle route, and the pick-up points for items / accessories or people. The system may include data from online mapping services such as Google ) information, such as home location, destination, route and any other relevant data. If data or links are missing, the system can ask the user for input. For example, when the system asks how many passengers are departing from the origin point, how many friends will pick up at the pick-up point? The system can ask the user whether there will be a trailer attached at home and then a boat on the way, or whether the user will pick up the boat and trailer at the pick-up point. The system can try to find out these questions based on user data, conversations, messages, appointments, purchases or reservation orders, etc. If there are differences or ambiguities or missing data points, the system can ask questions to the user. In addition, even if the system has calculated all the data, the system can further ask the user for input to confirm whether all the data is correct. The system can interact with the user via an application installed on the mobile phone, which can be used at any time before the trip, or it can interact with the user via the vehicle's infotainment system, which can be used when starting the trip. When the user starts the vehicle, the infotainment system can display the data to the user as your final destination "X" and provide a yes / no option, or it can provide additional options to select or enter the correct data. The infotainment system can ask "Are you picking up two friends at Y?" Are you picking up the boat at "Z," etc. The infotainment system also includes user behavior, such as whether the user is usually on time for appointments.

[0265] According to one embodiment, the system collects vehicle data. Understanding driver behavior and driving conditions is critical to ensuring safety, optimizing vehicle performance, and improving the overall driving experience. In order to gain insight into these aspects, several types of vehicle data can be collected and analyzed. The vehicle's driving pattern is analyzed to determine its role in vehicle mileage. Data related to distance traveled, routes taken, and stop-start frequency provides insight into driver habits and their impact on energy consumption. Identifying inefficient driving patterns can allow targeted improvements to maximize vehicle mileage. Data related to driving patterns and habits includes acceleration, braking, and steering behavior to provide valuable information about driver style and aggressiveness. Driving behaviors such as aggressive driving, rapid acceleration, and excessive braking can all lead to increased energy consumption, thereby reducing mileage.

[0266] One category of vehicle data is energy consumption data, which tracks the vehicle's electricity or fuel usage over a period of time. This information enables the system to assess the impact of driving behavior on the vehicle's range. For example, aggressive acceleration and continued high-speed driving can significantly increase energy consumption to result in reduced range. Additionally, collecting data related to speed and acceleration profiles can provide further insight into the impact of driving at different speeds and sudden acceleration on energy consumption. High speeds and rapid acceleration typically lead to increased energy consumption, which negatively affects vehicle range. In addition to driver behavior, understanding driving conditions is also critical to assessing their impact on energy consumption. Terrain and topography data is crucial in this regard. Driving uphill or on hilly terrain typically requires more energy, which can reduce vehicle range.

[0267] Additionally, sensors associated with the vehicle can collect data on weather conditions, road surfaces, and visibility, revealing how external factors affect driver behavior, range, and safety. Weather conditions also play a role in the achievement of range; extreme temperatures (whether hot or cold) can affect battery performance, while headwinds can increase energy consumption during driving. Other external or additional factors (such as road conditions, traffic congestion, and stop-and-go driving) can affect energy use. Rough roads or traffic congestion can lead to increased energy consumption, further affecting vehicle range. Monitoring the use of auxiliary systems such as air conditioning or heating can provide an understanding of how much additional energy is consumed to improve passenger comfort, which is critical when considering overall range.

[0268] Determine driving conditions (e.g., road conditions and weather). According to one embodiment of the system, road conditions are determined by receiving real-time weather broadcasts. According to one embodiment of the system, road conditions are determined by displaying a map of weather conditions. According to one embodiment of the system, road conditions are determined by detecting road conditions in real time via a computer vision module. According to one embodiment of the system, road conditions are determined by analyzing the scattering of an emitted light beam on a road surface, using filtering techniques to analyze the scattering of an emitted light beam on a road surface through images captured by a computer vision module.

[0269] According to one embodiment of the system, the road condition is detected by utilizing vehicle dynamic sensor data and an image of the surrounding environment in front of the host vehicle acquired by a camera, wherein the camera is a monochrome camera. According to one embodiment of the system, a road condition observer classifies the road condition based on an evaluation of the image from the camera and comparing it with the vehicle dynamic sensor data.

[0270] According to one embodiment of the system, the road condition observer also considers local weather data and derives the road condition, wherein the local weather data includes temperature, humidity value, rainfall, sleet, snowfall, wiper activity, and cloud data. According to one embodiment of the system, the road condition is detected by recognizing road signs using a computer vision module. According to one embodiment of the system, the road condition is detected by vehicle-to-infrastructure (V2I) communication.

[0271] According to one embodiment of the system, the road condition is determined by the friction coefficient between the road on which the host vehicle is traveling and the tires of the host vehicle via an acceleration sensor. According to one embodiment of the system, the road condition is determined by a road condition sensor, wherein the road condition sensor is at least one of a temperature sensor, an infrared sensor, an accelerometer, a GPS sensor, and a humidity sensor.

[0272] In electric and hybrid vehicles, vehicle data collected from regenerative braking data can be used to estimate driving range. Regenerative braking systems recover energy during deceleration, which is critical to extending a vehicle's driving range. Analyzing regenerative braking data helps determine how effectively the driver is using this feature, ensuring that energy is recovered and reused rather than wasted as heat.

[0273] Some of the other factors include tire wear and tear, tire pressure, existing accessories, vehicle configuration purchased and updated, vehicle model, make, type, year of manufacture, current condition, accessories currently existing or to be attached in the future, etc.

[0274] Speed ​​and location data combined with GPS information can provide a comprehensive understanding of the vehicle's movement and speed profile to help understand driving conditions. Further vehicle performance data (such as engine performance and fuel efficiency) allows assessment of how the driver's behavior affects the health, mileage and overall performance of the vehicle. In some embodiments, the driver's biometric data (such as eye movement and heart rate) can be collected to assess attention and emotional state under different driving conditions.

[0275] In one embodiment, various vehicle data sets can provide valuable insights into driver behavior, the impact of external factors on driving conditions, and areas of improvement that can increase vehicle performance (e.g., mileage). Privacy and security measures are provided for data using a network security module.

[0276] By collecting and analyzing these different vehicle data and user data, the system can gain a comprehensive understanding of how driver behavior and driving conditions affect vehicle mileage.

[0277] According to one embodiment of the system, the detection module is operable to: receive data from one or more of a user device and a user; process the data; determine the user's plans; determine a planned vehicle configuration change; determine a planned configuration change due to one or more of the addition of one or more items or the addition of one or more passengers; receive an identification of the one or more items; determine a size, shape, and first weight of the one or more items; determine a second weight of the one or more passengers; estimate a new mileage based on the size, shape, and first weight of the one or more items and the second weight of the one or more passengers. According to one embodiment of the system, the device is a smartphone. According to one embodiment of the system, the data includes one or more of first data from a dispatcher, second data extracted from speech using a natural language processing method, third data from an informal chat, fourth data from a reservation, and fifth data from a social media website.

[0278] Analysis and recommendation module 314: After collecting and analyzing various vehicle data sets and user data sets via user data and vehicle data collection module 312, the analysis and recommendation module 314 is used to formulate strategies for predicting driving mileage and increasing driving mileage.

[0279] Using machine learning algorithms, artificial intelligence (AI) can predict the potential range of a vehicle under different driving conditions and scenarios. By taking into account factors such as terrain, weather, traffic, and user behavior, AI can more accurately estimate vehicle range. Using predictive analytics for range estimation is a complex approach that relies on advanced machine learning algorithms to analyze large amounts of vehicle data, user data, and various driving conditions.

[0280] The process starts with data collection and preprocessing, combining data about the vehicle’s historical energy consumption, speed, acceleration, and driving behavior with real-time information about weather conditions, traffic, and terrain. Relevant features are extracted from this comprehensive data set to form the basis for accurate predictions.

[0281] An appropriate machine learning algorithm is selected to provide a range estimate. Example modules include regression models, time series analysis, and neural networks. The selected model is then trained on the preprocessed dataset to understand the relationships between different variables and their impact on vehicle range. These variables can be discussed in the range estimation module 306. By learning from historical data, the model can reveal how multiple factors such as driving habits and external conditions affect energy consumption.

[0282] Predictive analytics for estimating mileage further incorporates real-time data. The model continuously incorporates the latest information about weather conditions, traffic patterns, and other relevant factors, ensuring that predictions are adaptive and responsive to dynamic driving conditions. In addition to estimating vehicle mileage, predictive analytics can also optimize routes to maximize energy efficiency. By taking into account traffic patterns, road conditions, and elevation changes, the model can recommend the most energy-efficient route for a given destination.

[0283] Predictive analytics can provide uncertainty assessments to provide insight into the confidence level of the range prediction. This helps users understand the reliability of the estimated range under different scenarios and enables them to make more educated decisions. The process also involves user analysis to create personalized range estimates based on individual driving behavior, charging habits, and climate control preferences. This level of personalization ensures that the range prediction fits each driver's specific driving style and usage.

[0284] Predictive analytics models continuously learn. As new data becomes available, the model can update and refine its predictions, maintaining accuracy over time and adapting to changes in vehicle performance or driving patterns. By combining historical data, real-time information, and advanced machine learning techniques, range estimation using predictive analytics can provide vehicle users with more reliable and dynamic range predictions.

[0285] This enables drivers to make educated decisions about route planning, charging, and driving behavior. This ultimately improves the overall driving experience and promotes the adoption of sustainable transportation solutions.

[0286] Fig. 6A The structure of a neural network / machine learning model with a feedback loop according to one embodiment is shown. The artificial neural network (ANN) model includes an input layer, one or more hidden layers, and an output layer. Each node or artificial neuron is connected to another node or artificial neuron and has an associated weight and threshold. If the output of any single node is higher than a specified threshold, the node is activated and sends data to the next layer of the network. Otherwise, no data is passed to the next layer of the network. The machine learning model or ANN model can be trained on a set of data to obtain a request in the form of input data, predict the input data, and then provide a response. The model can learn from the data. Learning can be supervised learning and / or unsupervised learning, and can be based on different scenarios and different data sets. Supervised learning includes the logic of using at least one of decision trees, logistic regression, and support vector machines. Unsupervised learning includes the logic of using at least one of k-means clustering, hierarchical clustering, hidden Markov model, and Apriori algorithm. The output layer can predict mileage based on the input data.

[0287] In one embodiment, the ANN may be a deep neural network (DNN), which is a multi-layer tandem neural network that includes an artificial neural network (ANN), a convolutional neural network (CNN), and a recurrent neural network (RNN), which can identify features from inputs, conduct expert reviews, and perform operations that require prediction, creative thinking, and analysis. In one embodiment, the ANN may be a recurrent neural network (RNN), which is a type of artificial neural network (ANN) that uses sequential data or time series data. Deep learning algorithms are often used to deal with sequential or temporal problems, such as language translation, natural language processing (NLP), speech recognition, image recognition, etc. Like feedforward and convolutional neural networks (CNNs), recurrent neural networks use training data for learning. Their notable feature is "memory" because they obtain information from previous inputs via feedback loops to affect current inputs and outputs. The output from the output layer in the neural network model is fed back to the model through feedback. When training the model, the weight changes of the hidden layer (one or more) will be adjusted to better match the expected output. This will allow the model to make fewer mistakes when providing results.

[0288] Neural networks feature feedback loops that dynamically adjust the system output as it learns new data. In machine learning, backpropagation and feedback loops are used to train artificial intelligence (AI) models and continuously improve them as they are used. As the model receives more incoming data, it has more opportunities to learn from the data. The feedback loop, or backpropagation algorithm, identifies inconsistencies and feeds the corrected information back to the model as input.

[0289] Even if an AI / ML model is well trained with a large set of labeled data and concepts, after a period of time, the performance of the model may degrade when new unlabeled inputs are added for many reasons, including but not limited to concept drift, loss of precision in recall due to drift away from true positives, and data drift over time. The model’s feedback loop maintains the accuracy of AI results and ensures that the model maintains its performance and continues to improve even as new unlabeled data is ingested. A feedback loop is the process by which the prediction output of an AI model is reused to train a new version of the model.

[0290] Initially, when training an AI / ML model, a number of labeled samples are used, including both positive and negative examples of concepts (e.g., vehicle type, trailer type, driving conditions, etc.) for the model to learn. Afterwards, unlabeled data can be used to test the model. Using methods such as deep learning and neural networks, the model can predict whether the desired output (e.g., mileage) is within the predicted mileage. However, if the model returns a lower probability score, this input can be sent to a controller (which can be a human arbitrator) who verifies and corrects the result if necessary. Human arbitrators are only used in special cases. The feedback loop dynamically feeds labeled data, either automatically labeled or verified by the controller, back to the model and is used as training data so that the system can improve its predictions in real time and dynamically. These models can be used at various levels, for example, in image processing for detecting trailers and their parameters based on images.

[0291] Figure 6B The structure of a neural network / machine learning model with reinforcement learning capabilities according to one embodiment is shown. The network receives feedback from an authorized network environment. Although the system is similar to supervised learning, the feedback obtained in this case is evaluative rather than instructive, which means that there is no teacher in supervised learning. After receiving the feedback, the network adjusts the weights to make better predictions in the future. Machine learning techniques (e.g., deep learning) allow the model to obtain labeled training data and learn to recognize these concepts in subsequent data and images. New data can be fed to the model for testing, so training can be strengthened by feeding the model with data that has already been predicted. If the machine learning model has a feedback loop, each true example of the system's output will be rewarded, thereby further strengthening the learning. Feedback loops ensure that AI results do not stagnate. By incorporating feedback loops, model outputs will continue to dynamically improve with use / time.

[0292] Figure 6CAn example block diagram of predicting mileage using a machine learning model according to one embodiment is shown. The machine learning model 602 can take as input any data associated with a vehicle, user, accessories, and learn to identify features in the data that predict mileage output. The training data samples can include, for example, vehicle, trailer, and user data 604, such as vehicle size, vehicle shape, vehicle type, vehicle model, weight, length, vehicle accessories, etc.; similarly, the training data samples can include trailer size, trailer shape, trailer type, trailer model, weight, trailer length, trailer accessories, etc. The training data samples also include user data, such as driving habits, speed profile, braking profile, etc. In one embodiment, the training data samples relate to a system and method for real-time identification of trailers and their features using an onboard camera and / or other sensors. The trailer identification and feature information as well as the vehicle and user data can be transmitted to the cloud, where the trailer identification will be coupled with the available mileage estimate prediction in the case where previously identified vehicles and trailers belong to similar categories and have similar features and similar user habits. Some of the data can be historical data of the vehicle in similar situations. This information is then used to calculate / estimate vehicle mileage using trailer data and user behavior. The disclosed systems and methods may also provide data analysis information, which may be used later to improve vehicle mileage.

[0293] In one embodiment, the training data samples may also include contextual data / information 606 related to the surrounding environment. This may include, for example, the location of the vehicle, current weather conditions, temperature, time of day, traffic conditions in the area, number of lanes, other obstacles, uphill sections of the road, etc. The system may also collect contextual information from devices associated with the vehicle. For example, through applications installed on the device (e.g., online map services such as Maps) and location services, the system can understand the details of the vehicle. Real-time sensor data can be collected, which can include, for example, video, image, audio, infrared, temperature, 3D modeling, and any other suitable type of data to capture the current state of the vehicle's surroundings. In one embodiment, one or more machine learning models 602 that have been trained and based on similar types of data can be used to process real-time sensor data to predict mileage. Current context information 606 includes real-time sensor data.

[0294] Other data 608 may include data derived from user data. For example, the user's future rides, future additions or reductions of passengers, future additions or removals of luggage, future additions of trailers, bicycles, etc., which may come from his appointments, vacation reservations, etc., as described elsewhere in this application. Future additions and removals of items or passengers may be planned or unplanned. In one embodiment, configuration changes to add or remove items or passengers are detected via millimeter wave radar sensors. In one embodiment, the millimeter wave radar sensor is operable to detect the presence and quantity of organisms through micro-movements of the organism's heartbeat and breathing.

[0295] Any of the above types of data (e.g., target vehicle, trailer, and user data 604, contextual data / information 606, other data 608) can be correlated to mileage calculations, and such correlations can be automatically learned by the machine learning model 602. In one embodiment, during training, the machine learning model 602 can process training data samples (e.g., target vehicle, trailer, and user data 604, contextual data / information 606, other data 608) and, based on the current parameters of the machine learning model 602, predict an output 610, which can be a mileage (estimated mileage) for a given scenario. The predicted output is the mileage of the vehicle, which can be based on training data with labels 612 associated with the training data samples 618. In one embodiment, during training, the predicted output and the training data with labels 612 can be compared at 614. For example, the comparison 614 can be based on a loss function that measures the difference between the predicted output and the training data with labels 612. Based on the corresponding output of the comparison or loss function at 614, the training algorithm can update the parameters of the machine learning model 602 with the goal of minimizing the difference or loss between the subsequent predicted output 610 and the corresponding label 612. By iteratively training in this manner, the machine learning model 602 can "learn" from different training data samples and become better at predicting the output 610 to predict driving ranges similar to the driving range represented by the training label at 612. In one embodiment, the machine learning model 602 is trained using data specific to the type of trailer for which the machine learning model is used to detect driving range. In one embodiment, the machine learning model 602 is trained using data that is common to the type of trailer and used to predict driving range, thereby determining recommended actions for extending driving range.

[0296] Using the training data, the machine learning model 602 can be trained to enable the machine learning model to recognize features of the input data, which represent or are related to the mileage. For example, the trained machine learning model 602 can recognize data features that represent the likelihood of a collision within the vehicle's mileage. Through training, the machine learning model 602 can learn to identify predictive and non-predictive features and apply appropriate weights to the features to optimize the prediction accuracy of the machine learning model 602. In an embodiment where supervised learning is used and each training data sample 618 has a label 612, the training algorithm can iteratively process each training data sample 618 (including target vehicle, trailer and user data 604, contextual data / information 606, other data 608) and generate a prediction output 610, which is the mileage or estimated mileage based on the current parameters of the machine learning model 602. Based on the results of the comparison 614, the training algorithm can adjust the parameters / configuration (e.g., weights) of the model 602 accordingly to minimize the difference between the generated prediction output 610 and the corresponding label 612. Any suitable machine learning model and training algorithm can be used, including, for example, neural networks, decision trees, clustering algorithms, and any other suitable machine learning techniques. Once trained, the machine learning model 602 can take input data associated with the vehicle, trailer, and user and output the mileage that takes into account the vehicle, trailer, and user data.

[0297] Fig.6D An example flow chart for continuous monitoring and recommended actions using a machine learning model according to one embodiment is shown. The system can receive real-time data related to the vehicle, trailer or attached items, and user, as shown in 632. Any type of sensor can be used to collect data related to the vehicle. The sensor output can be, for example, an image, video, audio, lidar measurement, infrared measurement, temperature measurement, GPS data, or any other information measured or detected by the sensor. In one embodiment, the sensor output can be the result of one or more sensors capturing environmental information related to the vehicle's surroundings, which can include traffic at a location, target vehicle details, number of lanes, traffic around the main vehicle, road conditions, etc. The system can receive any data associated with the sensor output from the sensor, including the original sensor output and / or any derived data. In one embodiment, the system can use a machine learning model trained with the aid of a set of training data to process the received data and identify any actionable parameters of interest. The system can receive other data 636 from other sensors of the vehicle, such as weather conditions, humidity, temperature, driver behavior, tire tread, tire condition, tire pressure, etc.

[0298] As shown in step 634, the system can extract features from the received data based on the machine learning model. The machine learning model can automatically do this based on what it has learned during the training process. In one embodiment, appropriate weights learned during the training process can be applied to the features.

[0299] As shown in step 638, the machine learning model can generate a score based on the features of the received data to represent the likelihood or confidence of the received data, which is related to the item or trailer being associated with the current mileage of the impacted vehicle and the extent to which the item or trailer is affected in the current mileage.

[0300] As shown in step 640, the system may determine whether the score is high enough relative to a threshold or standard to warrant taking a specific action. If the score is not high enough, indicating a false positive, the system may return to step 632 and continue to monitor subsequent incoming data. On the other hand, if the score is high enough, then at step 642, the system may generate a new mileage that is different from the current mileage due to the addition of new parameters (e.g., an unplanned new passenger boarding the vehicle), alert the user, and generate or determine an appropriate action / response. In one embodiment, the system may send an alert to an appropriate recipient based on the type of event detected. For example, an alert is generated in the vehicle regarding a drop in mileage, a reduction, and a message may be sent to the charging station to reserve a charging slot / charging position.

[0301] In one embodiment, the system may repeat the Fig.6D In one embodiment, steps 632 to 642 may be performed by the system, and any combination of these steps may be performed by any other computing system, such as a remote network or a cloud network. In one embodiment, when a machine learning model is used to make such determinations, the system may transmit the trained machine learning model to a computing system in the vehicle. This may be desirable because the sensor data may be too large to be transmitted to the vehicle system in a timely manner for training.

[0302] In one embodiment, the driving parameter identification module of the provided system utilizes a convolutional neural network (CNN). In one embodiment, the driving parameter identification module may use a recurrent neural network architecture because the recurrent neural network architecture is able to use past temporal information to infer current inputs.

[0303] The system may further perform steps to detect whether the user follows and performs the suggested or recommended action and modify the estimated mileage accordingly.

[0304] According to one embodiment, adjustments may be performed or recommended in anticipation of planned changes, prior to actual configuration changes.

[0305] According to one embodiment of the system, the recommendation module may recommend making a first adjustment to the vehicle.

[0306] According to one embodiment of the system, the first adjustment may include changing tire pressure on the vehicle. According to one embodiment of the system, the first adjustment may include changing one or more of the positions of the accessories. According to one embodiment of the system, the first adjustment includes one or more of the route changes so that hilly terrain and steep roads are avoided. According to one embodiment of the system, the first adjustment includes speed management of the vehicle. According to one embodiment of the system, the first adjustment includes a braking profile along the route of the vehicle. According to one embodiment of the system, the first adjustment includes utilizing regenerative braking. According to one embodiment of the system, the first adjustment includes air conditioning management / air condition management. According to one embodiment of the system, the first adjustment includes reducing idling. According to one embodiment of the system, the first adjustment includes deploying a first aerodynamic accessory, wherein the aerodynamic accessory includes one or more of a spoiler, a side skirt, an air deflector on the vehicle.

[0307] According to one embodiment of the system, the recommendation module may suggest a second adjustment to the accessory.

[0308] According to one embodiment of the system, the second adjustment includes changing the tire pressure of the attached trailer. According to one embodiment of the system, the second adjustment includes changing the model / type of the accessory. According to one embodiment of the system, the second adjustment includes changing the attachment pattern of the item. According to one embodiment of the system, the second adjustment includes covering the trailer to obtain a streamlined shape and reduce air flow resistance to the trailer. According to one embodiment of the system, the second adjustment includes deploying side skirts along the bottom of the trailer to cover the gap between the wheels, thereby reducing drag.

[0309] According to one embodiment of the system, the second adjustment includes covering the exposed area under the trailer to prevent air from being trapped and to create a smoother airflow, thereby reducing drag. According to one embodiment of the system, the second adjustment includes installing a tail cone at the rear of the trailer to taper the rear of the trailer into a teardrop shape to reduce turbulence and drag. According to one embodiment of the system, the second adjustment includes sealing any gaps and openings between the vehicle and the front of the trailer to minimize airflow disruption. According to one embodiment of the system, the second adjustment includes raising the height of the trailer to reduce wind resistance. According to one embodiment of the system, the second adjustment includes selecting a trailer made of lightweight materials, wherein the lightweight materials are one or more of composite materials. According to one embodiment of the system, the second adjustment includes deploying a second aerodynamic appendage, wherein the second aerodynamic appendage includes aerodynamic trailer fairings at the front, sides, and top to reduce drag. According to one embodiment of the system, the second adjustment includes selecting a trailer with brakes to reduce reliance on the vehicle's brake load. According to one embodiment of the system, the second adjustment includes changing the height, weight, and size of the trailer and payload. According to one embodiment of the system, the second adjustment comprises covering the trailer to form a smooth surface, thereby reducing turbulence and thus reducing drag. According to one embodiment of the system, the second adjustment comprises distributing the payload evenly on the trailer.

[0310] According to one embodiment of the system, the recommendation module may suggest a third adjustment to the driving condition.

[0311] According to one embodiment of the system, the third adjustment includes one or more of the following: changing the departure time, and changing the rest time based on the weather. According to one embodiment of the system, the third adjustment includes one or more of the following: changing the route to avoid hilly terrain and steep roads. According to one embodiment of the system, the third adjustment includes changing the stopover. According to one embodiment of the system, the third adjustment includes reducing the number of passengers carried.

[0312] According to one embodiment of the system, any of the parameters affecting mileage discussed in the functional form f() and the parameters affecting mileage discussed in this application may be used to formulate recommendations for adjustments.

[0313] According to one embodiment of the system, the driving range is estimated using one or more parameters using model-based engineering, wherein the one or more parameters include vehicle parameters, trailer parameters, driving conditions, and energy consumption.

[0314] Modifications for vehicles with trailers: When a trailer or items are added, the system can suggest a modified overall scenario. For example, a vehicle with only one driver will have a different mileage than a vehicle with 4 to 5 additional passengers due to the added extra weight. The system takes the added extra weight into account and calculates or estimates the new mileage.

[0315] The system can find out what is going to happen based on AI or machine learning. For example, the system will find out if there will be additional people on the bus, whether a trailer will be attached, etc. Based on all this information, the system will provide you with mileage and route information for the trip. The system will take into account this additional information and calculate the new mileage that does not exist in the system in the existing technology. The system in the prior art does not take into account the additional information when giving the vehicle mileage. When the user starts the vehicle, the vehicle gives the mileage based on the current vehicle configuration it has reached, such as the amount of fuel or power state, the basic configuration of the vehicle, and some basic assumptions about the average number of passengers; in addition, the system in the prior art does not consider what will happen in the future, such as other passengers who get on the bus along the route at a stopover or pick up a trailer on the way to the destination, etc., to provide any information about the mileage. Therefore, it is helpful and useful to know two or all situations for the current mileage (one or more), just like in the present invention, which helps to meet the user's prediction and trade-offs of mileage changes to avoid disappointment. Have you added any additional weight? Trailer? People? Is there anything else along the route or at the beginning of the route? What is the new mileage? In one embodiment, the system displays charging stations within the current driving range and the new driving range.

[0316] Additionally, running boards and side steps help passengers get in and out of the vehicle and can also affect the mileage due to changes in aerodynamics. Spoilers installed on the vehicle can increase the mileage by enhancing aerodynamics. Interior accessories such as air conditioning, audio and entertainment systems, lights, mobile charging facilities, coffee warmers, etc. do affect the mileage by consuming part of the battery power.

[0317] The alert signal generation module 316 is an aspect of communication that is used to immediately draw attention to a specific event, situation / condition or situation that requires immediate action or attention. For example, an alert signal reminds the user of the estimated driving range and further provides a signal to charging stations along the route.

[0318] These warning signals are designed to be eye-catching, unique and easily identifiable, ensuring that the urgency of the situation is effectively communicated to the intended recipients. The form in which warning signals are generated varies depending on the environment and the intended audience.

[0319] In one embodiment, the alarm signal may be an audible alarm. They may be a simple beep or chime, or a dramatic alarm.

[0320] In one embodiment, the warning signal can be a visual warning. Use bright and conspicuous visual signals, such as flashing screens, flashing LED displays, flashing text, to attract attention.

[0321] In addition to auditory and visual alerts, there can also be tactile feedback. Tactile feedback provides an alert signal through the sense of touch (such as vibration or pulse). This form of alert can be used in smartphones and wearable devices to notify users of messages without relying solely on sound or visual effects.

[0322] In one embodiment, the alert signal may be a text message, email, and application notifications are also used to generate an alert signal on the electronic device.

[0323] In various situations, an alert signal is used to notify the driver of potential range issues, such as when the range is lower than the original estimate. In one embodiment, the alert may include information about nearby charging stations. In another embodiment, the alert may notify that the range is longer than estimated and cancel the charging reservation at one station and make a reservation at another station based on the new range.

[0324] The effectiveness of alert signal generation depends on factors such as the clarity of the signal, the urgency of the situation, and the attention span of the intended recipient. Proper design and consideration of context are required to ensure that the alert signal achieves its intended purpose and effectively communicates important information to the user. In one embodiment, an audible alert, visual alert, tactile feedback, text message, vehicle alarm, alert signal generation is used to make the user aware of issues related to mileage and recommended actions.

[0325] Alert signals for range issues in electric and hybrid vehicles are operable to ensure that drivers are always aware of remaining range and potential range limitations. These alert signals are designed to gain the driver's immediate attention, helping the driver make informed decisions to efficiently manage vehicle energy and arrive at their destination safely.

[0326] A common way to generate warning signals is through a low-battery warning light on the vehicle dashboard. This dedicated light illuminates when the remaining charge approaches a critical level or a predefined limit, providing a clear and obvious visual indication that the driver needs to take action. It can prompt the driver to find a nearby charging station or adjust driving habits to effectively save energy.

[0327] In addition to the warning light, the remaining mileage is displayed as a percentage on the instrument panel, which can continuously and intuitively display the vehicle's mileage status. When the mileage percentage decreases, the driver will be reminded to take power-saving measures or charge. This percentage display helps the driver quickly assess the available mileage and make informed decisions about driving plans.

[0328] Real-time range estimates can be communicated to the driver via a pop-up alert on the infotainment or navigation screen. As the onboard system predicts that the current charge may not be enough to reach the destination, the pop-up alert provides timely updates to the driver. This helps the driver plan the route accordingly and consider nearby charging options to avoid running out of battery. In conjunction with the visual alert, an audible alert tone or chime can accompany the low range warning light or pop-up alert. Audible signals are an effective way to attract the driver's attention, especially in busy or noisy environments where visual alerts may not be as noticeable. This multi-sensory approach ensures that the driver is notified of any range issues in a timely manner, reducing the risk of unexpected range-related issues during the journey. In addition, the haptic feedback feature of some vehicles allows for additional warning signals. These systems can generate slight vibrations in the steering wheel or seat to alert the driver of range issues without distracting the driver. Haptic feedback is a subtle but effective way to keep the driver informed without relying solely on visual or audible alerts.

[0329] Additionally, connected mobile applications provided by vehicle manufacturers can be used to generate alert signals. These applications can send mileage-related notifications directly to the driver's smartphone, even when the driver is not in the vehicle. This ensures that the driver is always aware of the vehicle's mileage status and can plan their trips accordingly, providing increased convenience and peace of mind.

[0330] The warning signal generation module ensures that the driver is promptly notified of range issues by using a combination of visual, audible and haptic warning signals. These warning signals enable the driver to make informed decisions and take appropriate actions to optimize vehicle range.

[0331] The display module 318 may be connected to a range warning system via an onboard computer or electronic control unit (ECU). The range warning system continuously monitors various parameters related to vehicle energy consumption, battery charge, driving style, and other factors that affect remaining range.

[0332] When the range warning system detects that the remaining range is approaching a critical level or a predetermined level, a warning signal is triggered. The warning signal is then sent to the display module, which is responsible for displaying important information to the driver on the vehicle's instrument panel or instrument cluster.

[0333] The connection between the mileage warning system and the display module is usually established through a communication network within the vehicle. Modern vehicles use a controller area network (CAN) or other communication protocols to transmit data between different electronic components, including the mileage warning system and the display module.

[0334] Once the warning signal reaches the display module, it initiates appropriate visual and audible alerts to notify the driver about the range issue. A low range warning light can illuminate, indicating that the remaining range is approaching a critical level or a predefined level. At the same time, the display module can display the remaining range as a percentage, allowing the driver to see the exact amount of range remaining. In another embodiment, the display module can also generate a pop-up alert on the infotainment or navigation screen to provide more detailed information about the range issue and potential solutions, such as nearby charging stations. In addition, the vehicle can be equipped with a haptic feedback function, and the display module can trigger a tactile alert, such as a slight vibration on the steering wheel or seat, to provide additional tactile cues to the driver.

[0335] The integration of the mileage alert system with the display module ensures that the driver receives timely and accurate information about the vehicle's mileage status. It enables the driver to make informed decisions, optimize driving behavior, and plan routes accordingly to maximize energy efficiency and avoid mileage-related issues during the journey.

[0336] In one embodiment of the system, the message includes generating an alert in the vehicle, wherein the alert is at least one of a text message, a visual prompt, an audible alert, a tactile prompt, and a vibration.

[0337] Fig. 7A A block diagram of a method for estimating mileage based on vehicle configuration change according to one embodiment is shown. In one embodiment, the method includes: at step 702, detecting a vehicle configuration change via a detection module; at step 704, estimating a new mileage of the vehicle based on the vehicle configuration change via an estimation module. According to one embodiment of the method, the configuration change includes one of adding an item, removing an item, adding a passenger, and dropping off a passenger.

[0338] According to one embodiment of the method, the detection module is operable to detect the item via a computer vision system; and estimate the size, shape, and weight of the item via the computer vision system.

[0339] Figure 7BA block diagram of a system for estimating mileage based on vehicle configuration changes according to one embodiment is shown. According to one embodiment, system 740 includes: a detection module 744; an estimation module 746; and a processor 742; wherein processor 742 is operable to: detect a vehicle configuration change via detection module 744 at step 702; and estimate a new mileage of the vehicle based on the vehicle configuration change via estimation module 746 at step 704.

[0340] According to one embodiment of the system, the configuration change includes adding an item to the vehicle. According to one embodiment of the system, the item includes a trailer connected to the vehicle, the trailer configured as an extension for increasing the carrying capacity of the vehicle. According to one embodiment of the system, the item includes one or more of a trailer, a boat, a rack, a camper, a caravan, and a cargo carrier. According to one embodiment of the system, the rack is one or more of a roof rack, a bicycle rack, and a ski rack.

[0341] Figure 7C A block diagram of a method stored on a non-transitory computer medium for estimating mileage based on vehicle configuration changes according to one embodiment is shown. According to one embodiment, it is a non-transitory computer readable medium 774 on which instructions are stored, the instructions being executable by a computer system 771 to perform operations including: at step 702, detecting a vehicle configuration change via a detection module; at step 704, estimating a new mileage of the vehicle based on the vehicle configuration change via an estimation module. A software application 776 may be stored on the computer readable medium 774 and executed by a processor 772 of the computer system 771.

[0342] Fig. 8A A block diagram of a method for detecting an item and estimating mileage via a communication module according to one embodiment is shown.

[0343] According to one embodiment, the method includes: at step 802, detecting a change in vehicle configuration due to the addition of an item via a detection module; at step 804, establishing a connection between the item and the vehicle via a communication module; at step 806, receiving one or more characteristics of the item via the communication module; and at step 808, estimating a new mileage of the vehicle based on the one or more characteristics of the item via an estimation module.

[0344] Figure 8B A block diagram of a method for detecting an item and estimating mileage via a communication module according to one embodiment is shown.

[0345] In one embodiment, it is a system 840, comprising: a detection module 844; a communication module 846; and a processing device 842 including an estimation module 848; wherein the processing device after being powered on is operable to: detect a change in vehicle configuration due to the addition of an item via the detection module at step 802; establish a connection between the item and the vehicle via the communication module at step 804; retrieve one or more features of the attached item via the communication module at step 806; and estimate the vehicle mileage based on one or more features of the item via the estimation module at step 808.

[0346] The vehicle is equipped with a sensor device having a plurality of sensors located and facing the outside of the vehicle, and the plurality of sensors are used to sense objects in an area close to the front end, rear end and side of the vehicle. The plurality of sensors generate sensing signals indicating one or more sensed objects. The plurality of sensors may include a plurality of imaging devices, such as front, rear and side cameras. Each camera may generate images of the surrounding and nearby areas of the vehicle. The generated images may be processed by video processing to identify objects (such as trailers and trailer features) and the position of the objects relative to the vehicle. The plurality of sensors may also include radar sensors, ultrasonic sensors, which are located at each of the four corners of the vehicle, the front and the rear of the vehicle, for sensing objects located outside the vehicle and in the area surrounding the vehicle.

[0347] The sensor device includes a plurality of sensors for sensing an area close to the exterior of the vehicle to detect the trailer and geometric features of the trailer to determine a geometric profile of the trailer. The geometric profile may include the size, shape and distance of the trailer and features on the trailer.

[0348] The trailer added to the vehicle can have various aerodynamic properties. When the trailer is attached to the vehicle, these aerodynamic properties can be changed to enhance or change the performance of the trailer. These enhancements or additions can be made before the trailer is attached. For example, the trailer can be equipped with a molded trailer nose that can be adjusted to change the aerodynamic profile on the front side of the trailer. The trailer nose has a curved molded front side that can improve aerodynamic performance, that is, reduce wind resistance or drag during driving. The trailer nose can change shape or be replaced with another trailer nose with a different shape. In addition, the trailer can include aerodynamic side panels on the left and right sides of the trailer, and the aerodynamic side panels can change their position or shape to change the aerodynamic profile of the trailer. In addition, the trailer can include one or more trailer tails at the rear side of the trailer, and the position or shape of the one or more trailer tails can be adjusted to change the aerodynamic characteristics of the trailer during the towing operation. It should be appreciated that other features can be provided on the trailer to change the aerodynamic profile of the trailer. In one embodiment, the vehicle or system can inform the driver of the vehicle of the vehicle's expected vehicle range for a given trailer. Similarly, the vehicle controller can prompt the user to swap one trailer for another to reduce air drag and thereby increase range. In one embodiment, the geometric profile of the trailer is activated as soon as the trailer is detected.

[0349] In one embodiment, the geometrical profile of the trailer along with the complete profile scanned by the radar / camera sensor is stored in a cloud database or a local database. In one embodiment, the system determines if the scanned trailer profile matches any of the stored trailer profiles. In another embodiment, the system matches the trailer identification number received through the communication model or camera to download the trailer profile details along with weight, aerodynamic drag, etc. If no match is found, the system estimates the mileage using various physics-based models or regression-based models and stores it under the new profile. The system can utilize machine learning and utilize the scanned geography overlaid by the camera to estimate the mileage. Figure 3The system can then use a 3D model to match the contour with the cloud or local database. The system can then determine whether the geometric contour matches the cloud / local database within a certain confidence range, such as 90-95% matching in terms of shape, size and weight. The system then continues to use the preset inputs of the existing intelligent mileage algorithm. In one embodiment, the system uses various models to predict the available mileage for the trailer contour and weight. In one embodiment, the system recommends modifications to the trailer and provides a comparison of mileage with and without modifications. In one embodiment, the system can prompt the user for the recommended modifications to improve efficiency, such as adding an aerodynamic shield to redistribute the load to reduce drag, and thus improve aerodynamic efficiency and extend the vehicle's mileage. The system can automatically implement the recommendation by electronically deploying the shield to extend the mileage. In one embodiment, data on trips, trailers, trailer features, mileage, routes, weather conditions, and driving details (e.g., driving modes) are all stored by the system in a database. A trip refers to a journey, typically involving travel from one place to another. A trip involves moving from an origin or point of origin to a destination. In one embodiment, the driving mode can be determined via a sensor dedicated to setting the driving mode. In one embodiment, determining the driving mode of a vehicle relies on monitoring various factors and sensors within the vehicle. These include the vehicle's speed, throttle position, brake pedal state, steering angle, and wheel speed to help determine the driver's behavior and intent, thereby adjusting the engine output and transmission settings. In addition, the engine speed per minute (RPM) and the gear of the transmission can be used to understand whether the vehicle is accelerating, cruising, or encountering challenging terrain. Environmental sensors and external sensors such as cameras and radars help determine comfort and safety. Driver input, GPS data, and navigation systems provide valuable contextual information, while terrain and road conditions prompt adaptive responses. All of this data is processed by the vehicle's electronic control unit (ECU) to determine the driving mode.

[0350] According to one embodiment of the system, the detection module is operable to detect the item via a communication module of the vehicle, wherein the communication module establishes a wireless connection between the vehicle and the item. GPS data and navigation systems can help determine the vehicle's location, route, and upcoming road conditions, and can further allow for anticipated adjustments to driving modes. External sensors such as cameras and radars are used to detect road conditions, traffic, and obstacles. Environmental sensors measure ambient temperature, humidity, wind speed, and air quality. The gear in which the transmission is operating can be used to understand whether the vehicle is in a low gear for climbing a hill or a high gear for highway cruising. The engine's revolutions per minute (RPM) can indicate whether the vehicle is idling, accelerating, or cruising.

[0351] According to one embodiment of the system, the detection module is operable to detect the object via a communication module of the vehicle, wherein the communication module establishes a wired connection between the vehicle and the object.

[0352] In one embodiment, the mileage value for the current vehicle operating cycle is based on an energy consumption rate value recorded during a previous vehicle operating cycle with similar operating conditions and configuration settings. In one embodiment, the mileage model may include calculating a composite energy consumption rate value based on the current energy consumption rate value associated with the current vehicle operating cycle and the energy consumption rate value recorded during the previous vehicle operating cycle, and determining the mileage value based on the composite energy consumption rate value. The mileage model may also include the effects of various factors in the mileage calculation equation expressed in the form of weighted values ​​or weighted factors.

[0353] Fig. 9A A block diagram of a method for estimating mileage based on a planned or future vehicle configuration change according to one embodiment is shown. According to one embodiment, the method includes: receiving an origin, a destination, and a stopover at step 902; detecting a planned vehicle configuration change at one or more of the origin and the stopover via a detection module at step 904; estimating a new mileage of the vehicle based on the planned vehicle configuration change via an estimation module at step 906; wherein the system is configured for adaptive intelligent mileage estimation.

[0354] According to one embodiment of the method, the planned configuration change includes adding one or more items and one or more passengers to the vehicle. According to one embodiment of the method, the method includes receiving an origin, a destination, and a stopover via one of an infotainment system and a user interface of a user device.

[0355] According to one embodiment of the method, the new driving range is based on energy consumption rates and historical data recorded during a previous vehicle operation cycle. According to one embodiment of the method, the method further includes sensing a change in one or more parameters, the one or more parameters including a change in tire pressure, weather conditions, route, unplanned stops, additional weight, and removed weight; estimating a reduction in available driving range due to the change; and providing a recommendation for extending the available driving range.

[0356] Fig. 9BA block diagram of a system for estimating mileage based on planned or future vehicle configuration changes according to one embodiment is shown. According to one embodiment, the system 940 includes: a detection module 944; an estimation module 946; and a processor 942; wherein the processor is operable to: receive an origin, a destination, and a stopover at step 902; at step 904, detect a planned vehicle configuration change at one or more of the origin and the stopover via the detection module; and at step 906, estimate a new mileage of the vehicle based on the planned vehicle configuration change via the estimation module; wherein the system is configured for adaptive intelligent mileage estimation.

[0357] According to one embodiment of the system, the planned configuration change includes adding one or more of one or more items and one or more passengers to the vehicle. According to one embodiment of the system, the one or more items include a trailer, a boat, a rack, a camper, a caravan, and a cargo carrier. According to one embodiment of the system, the system also receives information about the number of one or more passengers planned to be added at one or more of the origin and the stopover. According to one embodiment of the system, the system also receives identification numbers of the one or more items planned to be added at one or more of the origin and the stopover.

[0358] According to one embodiment of the system, the system provides a recommendation for an action to extend the new driving range based on the addition of additional accessories and passengers, wherein the action includes one or more of adjusting tire pressure, reducing stops, increasing stops, reducing passengers, reducing weight. According to one embodiment of the system, the system estimates a first driving range based on the action. According to one embodiment of the system, the system is operable to adaptively adjust tire pressure to extend the first driving range.

[0359] According to one embodiment of the system, the system further comprises a communication module for connecting one or more items to the vehicle via a communication link. According to one embodiment of the system, the communication link is a wireless connection. According to one embodiment of the system, the communication link is a wired connection. According to one embodiment of the system, the detection module retrieves characteristics of the one or more items directly from the communication link between the vehicle and the one or more items. According to one embodiment of the system, the detection module retrieves an identification number of the one or more items from the communication link between the vehicle and the one or more items, and further retrieves characteristics of the one or more items from a cloud network using the identification number. According to one embodiment of the system, the characteristics of the one or more items include weight, brand and model, shape, size and attachment mode of the one or more items.

[0360] According to one embodiment of the system, the communication module supports communication protocols, wherein the communication protocols include one or more of Hypertext Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), WebSocket, Constrained Application Protocol (CoAP) and Advanced Message Queuing Protocol (AMQP).

[0361] Fig. 9C A block diagram of a method stored on a non-transitory computer medium for estimating driving range based on planned or future vehicle configuration changes according to one embodiment is shown. According to one embodiment, it is a non-transitory computer readable medium 974 on which instructions are stored, which can be executed by a computer system 971 to perform operations including the following: receiving an origin, a destination, and a stopover at step 902; detecting a planned vehicle configuration change at one or more of the origin and the stopover via a detection module at step 904; estimating a new driving range of the vehicle based on the planned vehicle configuration change via an estimation module at step 906. A software application 976 can be stored on the computer readable medium 974 and executed by a processor 972 of the computer system 971; wherein the system is configured for adaptive intelligent driving range estimation.

[0362] According to one embodiment of the non-transitory computer readable medium, the method also includes instructions capable of being executed by the computer system to perform additional operations, the additional operations including: sensing a change in one or more parameters including tire pressure, weather conditions, route, unexpected stops, additional weight, and removed weight; estimating a reduction in available driving range due to the change; and providing recommendations for extending the available driving range.

[0363] Fig. 10A A block diagram of a method for recommending an adjustment for increasing a vehicle's driving range according to one embodiment is shown. According to one embodiment, the method includes: receiving an origin, a destination, and a stopover at step 1002; detecting a vehicle configuration change at one or more of the origin and the stopover via a detection module at step 1004; estimating a new driving range of the vehicle based on the vehicle configuration change via an estimation module at step 1006; and recommending an adjustment for extending the new driving range via a recommendation module at step 1008.

[0364] According to one embodiment of the method, the origin, destination, and stopovers are received via a user device. According to one embodiment of the method, the system processes data from the user device to determine the origin, destination, and stopovers. According to one embodiment of the method, the origin, destination, and stopovers are received via user input on an infotainment system.

[0365] Fig. 10BA block diagram of a system for recommending adjustments for increasing vehicle mileage according to one embodiment is shown. According to one embodiment, it is a system 1040, including: a detection module 1044; an estimation module 1046; and a processor 1042; wherein the processor 1042 is operable to: receive an origin, a destination, and a stopover at step 1002; detect a vehicle configuration change at one or more of the origin and the stopover via the detection module at step 1004; estimate a new mileage of the vehicle based on the vehicle configuration change via the estimation module at step 1006; and recommend an adjustment for extending the new mileage via the recommendation module at step 1008.

[0366] The system may further perform steps to detect whether the user followed and performed the suggested or recommended action and modify the estimated mileage accordingly.

[0367] According to one embodiment of the system, the recommendation module may recommend making a first adjustment to the vehicle.

[0368] According to one embodiment of the system, the first adjustment may include changing tire pressure on the vehicle. According to one embodiment of the system, the first adjustment may include changing one or more of the positions of the accessories. According to one embodiment of the system, the first adjustment includes changing one or more of the routes to avoid hilly terrain and steep roads. According to one embodiment of the system, the first adjustment includes speed management of the vehicle. According to one embodiment of the system, the first adjustment includes braking curves along the vehicle's route. According to one embodiment of the system, the first adjustment includes utilizing regenerative braking. According to one embodiment of the system, the first adjustment includes air conditioning management. According to one embodiment of the system, the first adjustment includes reducing idling. According to one embodiment of the system, the first adjustment includes deploying a first aerodynamic accessory, wherein the aerodynamic accessory includes one or more of a spoiler, a side skirt, an air deflector on the vehicle.

[0369] According to one embodiment of the system, the recommendation module may suggest a second adjustment to the accessory.

[0370] According to one embodiment of the system, the second adjustment includes changing the tire pressure of the attached trailer. According to one embodiment of the system, the second adjustment includes changing the accessory model. According to one embodiment of the system, the second adjustment includes changing the attachment mode of the item. According to one embodiment of the system, the second adjustment includes covering the trailer to obtain a streamlined shape and reduce air flow resistance to the trailer. According to one embodiment of the system, the second adjustment includes deploying side skirts along the bottom of the trailer to cover the gap between the wheels, thereby reducing drag.

[0371] According to one embodiment of the system, the second adjustment includes covering the exposed area under the trailer to prevent air from being trapped and to create a smoother airflow to reduce drag. According to one embodiment of the system, the second adjustment includes attaching a tail cone at the rear of the trailer to taper the rear of the trailer into a teardrop shape to reduce turbulence and drag. According to one embodiment of the system, the second adjustment includes sealing any gaps and openings between the vehicle and the front of the trailer to minimize airflow disruption. According to one embodiment of the system, the second adjustment includes raising the height of the trailer to reduce wind resistance. According to one embodiment of the system, the second adjustment includes selecting a trailer made of lightweight materials, wherein the lightweight materials are one or more of composite materials. According to one embodiment of the system, the second adjustment includes deploying a second pneumatic attachment, wherein the second pneumatic attachment includes pneumatic trailer fairings at the front, sides, and top to reduce drag. According to one embodiment of the system, the second adjustment includes selecting a trailer with brakes to reduce reliance on the vehicle's brake load. According to one embodiment of the system, the second adjustment includes changing the height, weight, and size of the trailer and the payload. According to one embodiment of the system, the second adjustment comprises covering the trailer to form a smooth surface, thereby reducing turbulence and thus reducing drag. According to one embodiment of the system, the second adjustment comprises distributing the payload evenly on the trailer.

[0372] According to one embodiment of the system, the recommendation module may suggest a third adjustment to the driving condition.

[0373] According to one embodiment of the system, the third adjustment includes one or more of the following: changing the departure time and changing the rest time based on the weather. According to one embodiment of the system, the third adjustment includes one or more of the following: changing the route to avoid hilly terrain and steep roads. According to one embodiment of the system, the third adjustment includes changing the stopover. According to one embodiment of the system, the third adjustment includes reducing the number of passengers carried.

[0374] According to one embodiment of the system, any of the parameters affecting mileage discussed in the functional form f() and the parameters affecting mileage discussed in this application may be used to formulate recommendations for adjustments.

[0375] According to one embodiment of the system, the driving range is estimated using one or more parameters using model-based engineering, wherein the one or more parameters include vehicle parameters, trailer parameters, driving conditions, and energy consumption.

[0376] Fig. 10CA block diagram for recommending adjustments for increasing the driving range of a vehicle according to one embodiment is shown. According to one embodiment, it is a non-transitory computer readable medium 1074 on which instructions are stored, which can be executed by a computer system 1071 to perform operations including the following: receiving an origin, a destination, and a stopover at step 1002; detecting a vehicle configuration change at one or more of the origin and the stopover via a detection module at step 1004; estimating a new driving range of the vehicle based on the vehicle configuration change via an estimation module at step 1006; and recommending adjustments for increasing the new driving range via a recommendation module at step 1008. Software application 1076 can be stored on computer readable medium 1074 and executed by processor 1072 of computer system 1071.

[0377] According to one embodiment of the transitory computer readable medium, the estimation module is operable to continuously monitor vehicle configuration changes in real time. According to one embodiment of the non-transitory computer readable medium, the recommendation module is operable to generate a recommendation for reserving a charging slot at a charging station to add new driving range.

[0378] According to one embodiment of the system, the configuration change includes adding one or more items and one or more passengers to the vehicle. According to one embodiment of the system, the system also receives information about the number of one or more passengers planned to be added at one or more of the origin and the stopover. According to one embodiment of the system, the items include one or more of a trailer, a boat, a rack, a camper, a caravan, and a truck. According to one embodiment of the system, the system also receives identification of the items planned to be added at one or more of the origin and the stopover.

[0379] In one embodiment, the system may include a network security module. In one aspect, a secure communication management (SCM) computer device for providing a secure data connection is provided. The SCM computer device includes a processor in communication with a memory. The processor is programmed to receive a first data message from a first device. The first data message is in a standardized data format. The processor is also programmed to analyze whether the first data message contains a potential network security threat. If it is determined that the first data message does not contain a network security threat, the processor is also programmed to convert the first data message into a first data format associated with a vehicle environment, transmit the converted first data message into a first data format associated with the vehicle environment, and transmit the converted first data message to the communication module using a first communication protocol associated with the negotiation protocol.

[0380] According to one embodiment, security authentication for data transmission includes: providing a hardware-based security engine (HSE) located in a network security module, the hardware-based security engine has been manufactured in a secure environment and authenticated as part of an approved network in the secure environment; using the hardware-based security engine to asynchronously authenticate, verify and encrypt data, storing user permission data and connection status data in an access control list for defining an allowed data communication path of the approved network, so that the network security module can communicate with other computing systems (e.g., communication modules) subject to the access control list, and using the security engine to asynchronously authenticate and encrypt data includes using a hardware-based module provided with one or more security aspects for protecting the system to identify a user device (UD) incorporating credentials contained in the hardware, wherein the security aspects include the hardware-based module communicating with a user of the user device and the hardware-based security engine.

[0381] Fig.11A A block diagram of a network security module according to one embodiment is shown. In one embodiment, Fig.11A A block diagram of a network security module is shown. Data communications between the system 1100 and the server 1170 via the communication module 1112 are first verified by the information security management module 1132 before being sent from the system to the server or from the server to the system. The information security management module is operable to analyze data for potential network security threats, encrypt data when no network security threats are detected, and send the encrypted data to the system or server. The system 1100 includes a processor 1108.

[0382] In one embodiment, the network security module further includes an information security management module that provides isolation between the system and the server. Fig. 11B A flow chart of protecting data by the network security module 1130 is shown. At step 1140, the information security management module is operable to receive data from the communication module. At step 1141, the information security management module exchanges security keys when communication begins between the communication module and the server. At step 1142, the information security management module receives the security key from the server. At step 1143, the information security management module authenticates the identity of the server by verifying the security key. At step 1144, the information security management module analyzes the security key for potential network security threats. At step 1145, the information security management module negotiates an encryption key between the communication module and the server. At step 1146, the information security management module receives encrypted data. At step 1147, when no network security threat is detected, the information security management module sends the encrypted data to the server.

[0383] In one embodiment, Fig. 11C A flow chart of protecting data by the network security module 1130 is shown. At step 1151, the information security management module is operable to: exchange security keys when communication begins between the communication module and the server. At step 1152, the information security management module receives the security key from the server. At step 1153, the information security management module authenticates the identity of the server by verifying the security key. At step 1154, the information security management module analyzes the security key for potential network security threats. At step 1155, the information security management module negotiates the encryption key between the communication module and the server. At step 1156, the information security management module receives encrypted data. At step 1157, the information security management module decrypts the encrypted data and performs an integrity check on the decrypted data. At step 1158, when no network security threat is detected, the information security management module sends the decrypted data to the communication module.

[0384] In one embodiment, the integrity check is a hash signature verification using Secure Hash Algorithm 256 (SHA256) or a similar method. In one embodiment, the information security management module is configured to perform asynchronous authentication and verification of the communication between the communication module and the server.

[0385] In one embodiment, the information security management module is configured to issue an alarm when a network security threat is detected. In one embodiment, the information security management module is configured to discard the received encrypted data if the integrity check of the encrypted data fails.

[0386] In one embodiment, the information security management module is configured to verify the integrity of the decrypted data by means of verifying accuracy, consistency and any possible data loss during communication via the communication module.

[0387] In one embodiment, the server is physically isolated from the system through the information security management module. When the system communicates with the server, such as Fig.11AAs shown, first, identity authentication is performed on the system and the server. The system is responsible for communicating / exchanging the system's public key and the signature of the public key with the server. The system's public key and the signature of the public key are sent to the information security management module. The information security management module decrypts the signature and verifies whether the decrypted public key is consistent with the received original public key. If the decrypted public key is verified, the identity authentication is passed. Similarly, the system and the server perform identity authentication on the information security management module. After the identity authentication is passed to the information security management module, the communicating parties, the system and the server negotiate the encryption key and integrity verification key for the data communication between the communicating parties through the authenticated asymmetric key. The session ID number is sent during the identity authentication process, so that the key needs to be bound to the session ID number; when the system sends data to the outside, the information security gateway receives the data via the communication module, performs integrity authentication on the data, and then encrypts the data through the negotiated key, and finally sends the data to the server via the communication module. When the information security management module receives data from the server, it first decrypts the data and then verifies the integrity of the data. If the verification passes, the data is sent via the communication module; otherwise, the data will be discarded.

[0388] In one embodiment, identity authentication is achieved by using an asymmetric key with a signature. In one embodiment, the signature is achieved by a pair of asymmetric keys trusted by the information security management module and the system, wherein the private key is used to sign the identities of the two communicating parties, and the public key is used to verify whether the identities of the two communicating parties have been signed. The signature identity includes a public key and a private key pair. In other words, the signature identity refers to the public name of the certificates installed on the user's machine.

[0389] In one embodiment, both communicating parties need to authenticate their identities through a pair of asymmetric keys, and use a unique pair of asymmetric keys to identify the task responsible for communicating with the information security management module of the system.

[0390] In one embodiment, the dynamic negotiation key is encrypted by using the Rivest-Shamir-Adleman (RSA) encryption algorithm. RSA is a public key cryptographic system widely used for secure data transmission. The negotiated key includes a data encryption key and a data integrity check key.

[0391] In one embodiment, the data encryption method is a triple data encryption algorithm (3DES) encryption algorithm. The integrity check algorithm is a hash-based message identity authentication code (HMAC-MD5-128) algorithm. When the data is output, the data is integrity checked and calculated, and the calculated message authentication code (MAC) value is added to the header of the data message, and then the data (including the MAC of the header) is encrypted by using the 3DES algorithm. After the data is encrypted, the header information of the security layer is added, and then the data is sent to the next layer for processing. In one embodiment, the next layer refers to the transport layer in the Transmission Control Protocol / Internet Protocol (TCP / IP) model.

[0392] When data encryption and data integrity authentication are initiated between the communicating parties, the information security management module ensures the security, reliability and confidentiality of the communication between the system and the server through identity authentication. This method is particularly suitable for embedded platforms with fewer resources and not connected to the public key infrastructure (PKI) system, and by ensuring the security and reliability of the communication between the system and the server, it can ensure that the security of the data on the server under Internet conditions can be protected from hacker attacks.

[0393] The description of one or more embodiments is for illustrative purposes only and is not intended to be exhaustive or limited to the embodiments described herein. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein best explain the principles of the embodiments, practical applications, and / or technical improvements over technologies found in the marketplace, and / or enable others of ordinary skill in the art to understand the embodiments described herein.

[0394] List of Terms

[0395] 1. A system comprising:

[0396] a detection module; an estimation module; and a processor; and

[0397] Among other things, the processor is operable to:

[0398] detecting, via a detection module, a vehicle configuration change; and

[0399] A new range for the vehicle is estimated based on the vehicle configuration change via an estimation module.

[0400] 2. The system of clause 1, wherein the configuration change comprises adding an item to the vehicle.

[0401] 3. The system of clause 2, wherein the item comprises a trailer connected to the vehicle, the trailer being configured as an extension for increasing the carrying capacity of the vehicle.

[0402] 4. The system of clause 2, wherein the item comprises one or more of a trailer, a boat, a rack, a camper, a caravan, and a cargo transporter.

[0403] 5. The system of clause 4, wherein the bracket is one or more of a roof rack, a bicycle rack, and a ski rack.

[0404] 6. The system of clause 2, wherein the detection module comprises one or more sensors.

[0405] 7. The system of clause 6, wherein the one or more sensors include a magnetic sensor, a proximity sensor, a load sensor, an electrical sensor, and a visual sensor.

[0406] 8. The system of clause 6, wherein the one or more sensors comprises a camera coupled to a computer vision system.

[0407] 9. The system of clause 6, wherein the one or more sensors are mounted on the vehicle such that the one or more sensors detect vehicle configuration changes by detecting changes in sensor data.

[0408] 10. The system of clause 6, wherein the one or more sensors are configured to determine one or more of a size, shape, and weight of an item.

[0409] 11. The system of clause 2, wherein the detection module is operable to:

[0410] Processing sensor data;

[0411] Analytical patterns; and

[0412] confirm the existence of the item; and

[0413] One or more of the size, shape, and weight of the item is determined.

[0414] 12. The system of clause 2, wherein the detection module is operable to detect the object via a communication module of the vehicle, wherein the communication module establishes a communication link between the vehicle and the object.

[0415] 13. The system of clause 12, wherein the communication link is a wireless connection.

[0416] 14. The system of clause 12, wherein the communication link is a wired connection.

[0417] 15. The system of clause 12, wherein the detection module retrieves the characteristics of the object directly from a communication link between the vehicle and the object.

[0418] 16. The system of clause 15, wherein the detection module retrieves an identification number of the item from a communication link between the vehicle and the item, and further uses the identification number to retrieve characteristics of the item from a cloud network.

[0419] 17. The system of clause 16, wherein the characteristics of the item include one or more of a weight of the item, a brand and model of the item, a shape of the item, a size of the item, and a mode of attachment of the item.

[0420] 18. A system according to clause 12, wherein the communication module supports communication protocols, wherein the communication protocols include one or more of Hypertext Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), WebSocket, Constrained Application Protocol (CoAP), and Advanced Message Queuing Protocol (AMQP).

[0421] 19. The system of clause 1, wherein the system is further configured to detect environmental information around the vehicle.

[0422] 20. The system of clause 19, wherein the environmental information includes route conditions, road conditions, weather conditions, and traffic conditions.

[0423] 21. A system as described in clause 2, wherein the system includes a non-volatile memory configured to store details of the item, the vehicle and the new mileage.

[0424] 22. The system of clause 2, wherein the item comprises a physical connection, an electrical connection, and a communication connection.

[0425] 23. The system of clause 22, wherein the physical connection comprises a physical coupling between the article and the vehicle.

[0426] 24. The system of clause 22, wherein the electrical connection comprises an electrical coupling between the article and the vehicle.

[0427] 25. The system of clause 22, wherein the communication connection comprises at least one of wired communication and wireless communication between the item and the vehicle.

[0428] 26. The system of clause 1, wherein the estimation module comprises a machine learning algorithm.

[0429] 27. The system of clause 1, wherein the estimation module includes historical data collected in performing mileage estimation based on similar item categories, wherein the similar item categories are based on comparison factors among size factors, shape factors, and weight factors.

[0430] 28. The system of clause 1, wherein the estimation module includes energy consumption rate values ​​recorded during one or more previous operating cycles.

[0431] 29. The system of clause 2, wherein the estimation module derives the mileage estimation factor from a cloud network, wherein the cloud network includes historical data related to the impact of items on mileage.

[0432] 30. The system of clause 2, wherein the new mileage is based on a regression of historical data regarding the effect of the item on mileage.

[0433] 31. The system of clause 2, wherein the new mileage is based on the size, shape, and weight of the item.

[0434] 32. The system of clause 1, wherein the estimation module further considers one or more of driving conditions and driving patterns to estimate the new driving range.

[0435] 33. The system of clause 1, wherein the system further comprises displaying the previously estimated mileage and the new mileage on an infotainment system of the vehicle.

[0436] 34. The system of clause 33, wherein the configuration change comprises an initial configuration of the vehicle at the origin that affects the previously estimated range.

[0437] 35. A method comprising:

[0438] detecting, via a detection module, a vehicle configuration change; and

[0439] A new range for the vehicle is estimated via an estimation module based on the vehicle configuration change.

[0440] 36. The method of clause 35, wherein the configuration change comprises one of adding an item, removing an item, adding a passenger, and dropping off a passenger.

[0441] 37. A method according to clause 36, wherein the detection module is operable to:

[0442] Detecting items via computer vision systems; and

[0443] Estimate the size, shape and weight of items via computer vision systems.

[0444] 38. The method of clause 36, wherein the detection module is operable to:

[0445] establishing a communication link between the vehicle and the object via the communication module; and

[0446] detecting a configuration change due to adding one of the items; and

[0447] Characteristics of the item are retrieved via the communication link.

[0448] 39. The method of clause 36, wherein the detection module is operable to:

[0449] establishing a communication link between the vehicle and the object via the communication module;

[0450] retrieving an identification of the item via the communication link; and

[0451] Characteristics of the item are retrieved via the cloud network using an identification of the item via the communication link.

[0452] 40. The method of clause 35, wherein the method further comprises displaying the previously estimated mileage and the new mileage on an infotainment system of the vehicle.

[0453] 41. The method according to clause 35, wherein the new mileage is based on current driving data, historical driving data and historical mileage related data.

[0454] 42. The method according to clause 35, wherein the method further comprises detecting environmental information around the vehicle.

[0455] 43. The method according to clause 42, wherein the method further comprises adjusting the new driving range based on environmental information surrounding the vehicle.

[0456] 44. A non-transitory computer readable medium having instructions stored thereon, the instructions being executable by a computer system to perform operations comprising:

[0457] detecting, via a detection module, a vehicle configuration change; and

[0458] A new range for the vehicle is estimated via an estimation module based on the vehicle configuration change.

[0459] 45. The non-transitory computer-readable medium of clause 44, wherein the configuration change comprises adding an item to the vehicle.

[0460] 46. ​​The non-transitory computer-readable medium of clause 44, wherein the configuration change comprises removing an item from the vehicle.

[0461] 47. The non-transitory computer-readable medium of clause 45, wherein the configuration change is detected using RFID technology, wherein an RFID tag including information related to the size, shape, and weight of the item is associated with the item and an RFID reader is associated with the vehicle.

[0462] 48. A system comprising:

[0463] a detection module; an estimation module; a communication module; and a processor; and

[0464] Among other things, the processor is operable to:

[0465] Detecting vehicle configuration changes caused by adding items via a detection module;

[0466] establishing a connection between the object and the vehicle via the communication module;

[0467] receiving, via the communication module, one or more characteristics of the item; and

[0468] A new mileage of the vehicle is estimated based on one or more characteristics of the item via the estimation module.

[0469] 49. The system of clause 48, wherein the object comprises at least one of a trailer, a boat, a rack, a camper, a caravan, and a cargo transporter.

[0470] 50. The system of clause 48, wherein the detection module further comprises one or more sensors.

[0471] 51. The system of clause 50, wherein the one or more sensors include a magnetic sensor, a proximity sensor, a load sensor, an electrical sensor, and a visual sensor; and wherein the visual sensor includes a camera coupled to a computer vision system.

[0472] 52. The system of clause 50, wherein the one or more sensors are mounted on the vehicle such that the one or more sensors detect vehicle configuration changes by detecting changes in sensor data.

[0473] 53. The system of clause 48, wherein the connection is a wireless connection.

[0474] 54. The system of clause 48, wherein the connection is a wired connection.

[0475] 55. The system of clause 48, wherein the detection module retrieves one or more characteristics of the object directly from the connection between the vehicle and the object.

[0476] 56. A system according to clause 48, wherein the detection module retrieves an identification number of the item from the connection between the vehicle and the item, and further uses the identification number to retrieve one or more characteristics of the item from the cloud network.

[0477] 57. A system according to clause 48, wherein the one or more characteristics of the item include one or more of the weight of the item, the brand and model of the item, the shape of the item, the size of the item, and the attachment mode of the item.

[0478] 58. A system according to clause 48, wherein the communication module supports communication protocols, wherein the communication protocols include one or more of Hypertext Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), WebSocket, Constrained Application Protocol (CoAP), and Advanced Message Queuing Protocol (AMQP).

[0479] 59. The system of clause 48, wherein the system is further configured to detect environmental information surrounding the vehicle.

[0480] 60. A system according to clause 59, wherein the environmental information includes route conditions, road conditions, weather conditions and traffic conditions.

[0481] 61. A system as described in clause 59, wherein the system includes a non-volatile memory configured to store details of the item, the vehicle, and the new mileage.

[0482] 62. The system of clause 48, wherein the article comprises a physical connection, an electrical connection, and a communication connection.

[0483] 63. The system of clause 62, wherein the physical connection comprises a physical coupling between the article and the vehicle.

[0484] 64. The system of clause 62, wherein the electrical connection comprises an electrical coupling between the article and the vehicle.

[0485] 65. A system according to clause 62, wherein the communication connection includes at least one of wired communication and wireless communication between the item and the vehicle.

[0486] 66. A method comprising:

[0487] Detecting vehicle configuration changes caused by adding items via a detection module;

[0488] establishing a connection between the object and the vehicle via the communication module;

[0489] receiving, via the communication module, one or more characteristics of the item; and

[0490] A new range of the vehicle is estimated based on one or more characteristics of the item via the estimation module.

[0491] 67. A method according to clause 66, wherein the estimation module includes a machine learning algorithm.

[0492] 68. The method of clause 66, wherein the estimation module includes historical data collected in performing mileage estimation based on similar item categories, wherein the similar item categories are based on comparison factors among size factors, shape factors, and weight factors.

[0493] 69. The method of clause 66, wherein the estimation module includes energy consumption rate values ​​recorded during one or more previous operating cycles.

[0494] 70. The method of clause 66, wherein the estimation module derives the mileage estimation factor from a cloud network, wherein the cloud network includes historical data related to the impact of items on mileage.

[0495] 71. The method of clause 66, wherein the new mileage is based on a regression of historical data regarding the effect of the item on mileage.

[0496] 72. The method of clause 66, wherein the new mileage is based on the size, shape, and weight of the item.

[0497] 73. The method according to clause 66, wherein the estimation module also considers one or more of driving conditions and driving patterns to estimate the new mileage.

[0498] 74. The method of clause 66, wherein the system further comprises displaying the previously estimated mileage and the new mileage on an infotainment system of the vehicle.

[0499] 75. The method of clause 74, wherein the configuration change includes an initial configuration of the vehicle at the origin that affects the previously estimated range.

Claims

1. A system comprising: Detection module; Estimation module; Communication module; and processors; as well as Among other things, the processor is operable to: Detecting vehicle configuration changes caused by adding items via a detection module; establishing a connection between the object and the vehicle via the communication module; receiving, via the communication module, one or more characteristics of the item; and A new mileage of the vehicle is estimated based on the change in the one or more characteristics of the item via the estimation module.

2. The system according to claim 1, wherein: Configuration changes also include adding passengers to the vehicle.

3. The system according to claim 1, wherein: The article includes one or more of a trailer, a boat, a rack, a camper, a caravan, and a cargo carrier, wherein the rack is one or more of a roof rack, a bicycle rack, and a ski rack.

4. The system according to claim 1, wherein: The detection module includes one or more sensors coupled to a computer vision system, wherein the one or more sensors include a magnetic sensor, a proximity sensor, a load sensor, an electrical sensor, and a visual sensor.

5. The system according to claim 4, wherein: The one or more sensors are mounted on the vehicle such that data from the one or more sensors are operable to detect vehicle configuration changes.

6. The system according to claim 4, wherein: The one or more sensors are operable to determine one or more of a size, shape, and weight of the item, and wherein the estimate of the new range is based on one or more of the size, shape, and weight of the item.

7. The system according to claim 4, wherein: The detection module is operable to: processing data from the one or more sensors; Analytical patterns; confirming the existence of an object; as well as One or more of the size, shape, and weight of the item is determined.

8. The system according to claim 1, wherein: The detection module is operable to detect the item via a communication module of the vehicle, wherein the communication module establishes a wired connection between the vehicle and the item.

9. The system according to claim 1, wherein: The detection module is operable to detect the item via a communication module of the vehicle, wherein the communication module establishes a wireless connection between the vehicle and the item.

10. The system according to claim 1, wherein: The detection module retrieves the one or more characteristics of the item directly from the connection between the vehicle and the item, wherein the one or more characteristics of the item include one or more of a weight of the item, a brand and model of the item, a shape of the item, a size of the item, and an attachment mode of the item.

11. The system according to claim 1, wherein: The detection module retrieves an identification number of the item from the connection between the vehicle and the item, and further uses the identification number to retrieve the one or more characteristics of the item from the cloud network, wherein the one or more characteristics of the item include one or more of the weight of the item, the brand and model of the item, the shape of the item, the size of the item, and the attachment mode of the item.

12. The system of claim 1, wherein: The vehicle and the item are connected via a physical connection, an electrical connection, and a communication connection, wherein the physical connection includes a physical coupling between the item and the vehicle, wherein the electrical connection includes an electrical coupling between the item and the vehicle, and wherein the communication connection includes at least one of wired communication and wireless communication between the item and the vehicle.

13. The system of claim 1, wherein: The estimation module includes a machine learning algorithm operable to perform mileage estimation based on similar item categories, wherein the similar item categories are based on comparative factors among size, shape, and weight.

14. The system of claim 1, wherein: The system also includes displaying the current mileage and new mileage on the vehicle's infotainment system.

15. The system of claim 4, wherein: The system is further operable to detect additional factors via the one or more sensors, wherein the additional factors include one or more of driving conditions, driving patterns; and wherein the estimation module is further operable to consider external factors in performing the mileage estimation.

16. A method comprising: Detecting vehicle configuration changes caused by adding items via a detection module; establishing a connection between the object and the vehicle via the communication module; receiving, via the communication module, one or more characteristics of the item; as well as A new mileage of the vehicle is estimated based on the one or more characteristics of the item via the estimation module.

17. The method according to claim 16, wherein: The detection module is operable to: Detect configuration changes due to adding one of the items; Detecting items via computer vision systems; and Estimate the size, shape and weight of items via computer vision systems.

18. The method according to claim 16, wherein: The method can be operated to: receiving an identification number of an item via the connection; and The one or more characteristics of the item are retrieved via a cloud network using an identification number of the item, wherein the one or more characteristics of the item include one or more of the weight of the item, the brand and model of the item, the shape of the item, the size of the item, and the attachment mode of the item.

19. A non-transitory computer readable medium having instructions stored thereon, the instructions being executable by a computer system to perform operations comprising: Detecting vehicle configuration changes caused by adding items via a detection module; establishing a connection between the object and the vehicle via the communication module; receiving, via the communication module, one or more characteristics of the item; and A new range of the vehicle is estimated based on the one or more features via an estimation module.

20. The non-transitory computer readable medium of claim 19, wherein: Configuration changes are detected using RFID technology, where an RFID tag including information about the size, shape, and weight of the item is associated with the item and an RFID reader is associated with the vehicle.

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