Method of detecting and preventing accidents in lane reduction using autonomous vehicles

Abstract

One embodiment relates to a system comprising a processor, a computer vision module, and a control module; wherein the processor is operable to detect a lane decrease in the most recently disappeared lane; detecting an adjacent vehicle in the recently disappearing lane and a driving action of the adjacent vehicle; detecting a license plate of an adjacent vehicle; receiving a first signal for the driving mode from the adjacent vehicle; detecting a lane vanishing point in the latest vanishing lane; determining a time interval for an adjacent vehicle to arrive at the lane vanishing point; a hazard assessment of the collision is performed based on the driving action, and then control commands are transmitted to an actuator system of the host vehicle based on the hazard assessment.

Description

Technical Field

[0001] The present disclosure relates generally to the field of vehicle safety and more particularly to systems and methods for lane reduction monitoring methods and lane reduction systems for autonomous vehicles. Background Art

[0002] In a vehicle, the driver remains a critical component of the vehicle's control systems as the driver makes many decisions for safe operation of the vehicle, including speed, steering, obstacle and hazard identification and avoidance. However, the driver's ability to perform all of these functions may be impaired due to physical factors such as driver fatigue, driver impairment, driver inattention, or other factors such as visibility that reduce the driver's reaction time required to successfully avoid a hazard.

[0003] When a lane ends or is reduced, this can create a dangerous situation for a human driver because the human driver may not be able to react quickly enough to avoid a collision. This is especially true at high speeds or in high traffic areas. Therefore, there is a need for a method and system for an autonomous vehicle to safely and efficiently navigate a roadway, thereby reducing the risk of accidents and improving the overall experience for passengers and other drivers on the road. Summary of the invention

[0004] The following presents a summary of the invention to provide a basic understanding of one or more embodiments described herein. The summary of the invention is not intended to identify key or important elements or to delineate 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 the more detailed description presented herein.

[0005] The present specification provides systems and methods for identifying when a lane ends or is decreasing, and then making appropriate adjustments to the speed and trajectory of the vehicle to avoid a collision.

[0006] One embodiment relates to a system for lane reduction detection. The system includes a processor, a computer vision module, a communication module, and a control system, wherein the system is configured to: detect lane reduction in a lane that has recently disappeared by the processor, detect a neighboring vehicle and a driving action of the neighboring vehicle in the lane that has recently disappeared by the computer vision module, detect a license plate number of the neighboring vehicle by the computer vision module; receive a signal for a driving mode from the neighboring vehicle by the communication module; detect a lane vanishing point in the lane that has recently disappeared by the computer vision module; determine a time interval for the neighboring vehicle to reach the lane vanishing point by the processor; detect the driving action of the neighboring vehicle via the processor; perform a risk assessment of collision based on the driving action by the processor; and transmit a control command to an actuator system of a host vehicle via the communication module based on the risk assessment.

[0007] One embodiment relates to a vehicle. The vehicle includes a sensor, an actuator, and a system, the system further including a processor, a computer vision module, a communication module, and a control system, wherein the system is configured to: detect lane reduction in a lane that has recently disappeared by the processor; detect a neighboring vehicle and a driving action of the neighboring vehicle in the lane that has recently disappeared by the computer vision module; detect a license plate of the neighboring vehicle by the computer vision module; receive a first signal for a driving mode from the neighboring vehicle by the communication module; detect a lane vanishing point in the lane that has recently disappeared by the computer vision module; determine a time interval for the neighboring vehicle to reach the lane vanishing point by the processor; detect the driving action of the neighboring vehicle via the processor; perform a risk assessment of collision based on the driving action by the processor; and transmit a control command to an actuator system of a host vehicle via the communication module based on the risk assessment.

[0008] One embodiment is directed to a method for detecting and preventing a possible accident of a host vehicle in a lane reduction. The method includes: detecting a lane reduction in a lane that has recently disappeared; detecting a neighboring vehicle in the lane that has recently disappeared; detecting a license plate of the neighboring vehicle; receiving a first signal for a driving mode from the neighboring vehicle; detecting a lane vanishing point in the lane that has recently disappeared; determining a time interval for the neighboring vehicle to reach the lane vanishing point; detecting a driving action of the neighboring vehicle; performing a risk assessment of a collision based on the driving action and the time interval; and transmitting a control command to an actuator system of the host vehicle based on the risk assessment.

[0009] One embodiment relates to a non-transitory computer-readable medium having instructions stored thereon, the instructions executable by a computer system to perform operations including: detecting a lane reduction in a lane that has recently disappeared; detecting a neighboring vehicle in a lane that has recently disappeared; detecting a license plate of a neighboring vehicle; receiving a first signal for a driving mode from a neighboring vehicle; detecting a lane vanishing point in a lane that has recently disappeared; determining a time interval for a neighboring vehicle to reach the lane vanishing point; detecting a driving action of the neighboring vehicle; performing a risk assessment of a collision based on the driving action and the time interval; and transmitting a control command to an actuator system of a host vehicle based on the risk assessment.

[0010] One embodiment relates to a system for lane reduction detection. The system includes a processor, a computer vision system, and a control system, wherein the system is configured as a component of a host vehicle; wherein the processor is configured to: detect lane reduction in a lane that has recently disappeared; detect a neighboring vehicle in the lane that has recently disappeared; detect a license plate of a neighboring vehicle; receive a first signal for a driving mode from a neighboring vehicle; detect a lane vanishing point in the lane that has recently disappeared; determine a time interval for a neighboring vehicle to reach the lane vanishing point; predict a driving action of the neighboring vehicle; perform a hazard assessment based on the driving action and the time interval; and transmit a hazard signal to the neighboring vehicle based on the hazard assessment.

[0011] One embodiment is directed to a method for detecting and preventing a possible accident of a host vehicle in a lane reduction. The method includes: detecting a lane reduction in a recently disappeared lane; detecting a neighboring vehicle in a recently disappeared lane; detecting a license plate of the neighboring vehicle; receiving a first signal for a driving mode from the neighboring vehicle; detecting a lane vanishing point in the recently disappeared lane; determining a time interval for the neighboring vehicle to reach the lane vanishing point; predicting a driving action of the neighboring vehicle; performing a hazard assessment based on the driving action and the time interval; and transmitting a hazard signal to the neighboring vehicle based on the hazard assessment.

[0012] One embodiment relates to a non-transitory computer-readable medium having instructions stored thereon, the instructions executable by a computer system to perform operations including: detecting lane reduction; detecting a neighboring vehicle in a lane that has recently disappeared; detecting a license plate of a neighboring vehicle and checking in a license plate database whether the license plate number is assigned to a non-autonomous lane; performing lane width detection to detect a lane vanishing point in a lane that has recently disappeared; determining a time interval for a neighboring vehicle to reach the lane vanishing point; predicting a driving action of a neighboring vehicle; performing a hazard assessment based on the driving action and the time interval; and transmitting a hazard signal to the neighboring vehicle based on the hazard assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] 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 present invention, in which:

[0014] The present disclosure is directed to various embodiments shown and described herein with reference to the various drawings, in which like reference numerals are used to represent like systems or assembly components, methods or algorithm steps. The illustrated components of the various systems are not necessarily drawn to scale.

[0015] Figure 1A and Figure 1B A problematic accident scenario due to lane reduction is illustrated in accordance with one or more embodiments.

[0016] Figure 2A , Figure 2B and Figure 2C A real-life scenario that occurs due to lane reduction is illustrated in accordance with one or more embodiments.

[0017] Figure 3A A schematic configuration of a lane reduction determination system according to one or more embodiments is shown.

[0018] Figure 3B Various types of sensors are shown attached to a host vehicle in accordance with one or more embodiments.

[0019] Figure 3CVarious types of actuators are shown attached to a host vehicle in accordance with one or more embodiments.

[0020] Figure 4A is the response of a vehicle including the system in a lane reduction situation in accordance with one or more embodiments.

[0021] Figure 4B The response of a vehicle including the system in a lane reduction situation is shown in accordance with one or more embodiments.

[0022] Figure 5A A flow chart showing a method for detecting a lane reducing situation and making appropriate adjustments to avoid a collision.

[0023] Figure 5B Traffic signs for lane reduction situations are shown in accordance with one or more embodiments.

[0024] Fig. 6A A vehicle including the system is shown approaching a lane reduction.

[0025] Figure 6B Lane width detection by a vehicle is illustrated in accordance with one or more embodiments.

[0026] Figure 6C The time period during which a vehicle including a system for detecting / tracking neighboring vehicles may reach a lane vanishing point is shown in accordance with one or more embodiments.

[0027] Fig.6D , Fig. 6E , Fig. 6F , Figure 6G , Figure 6H , Fig.6I and Figure 6J Various situations that may occur during channel reduction are shown.

[0028] Fig. 7A and Figure 7B Shown is the system's approach response when two different types of neighboring vehicles are approaching a lane reduction.

[0029] Fig. 8A , Figure 8B and Figure 8C An image showing a temporary lane closure with signs and barriers in place, and a graphical representation of the system's response to the temporary lane closure.

[0030] Fig. 9A A block diagram of a system illustrating a lane reduction determination system in accordance with one or more embodiments.

[0031] Fig. 9B A block diagram illustrating a non-transitory computer readable medium that performs operations to detect lane reduction and make appropriate adjustments according to one embodiment.

[0032] Fig. 10A A block diagram of a system illustrating a lane reduction determination system in accordance with one or more embodiments.

[0033] Fig. 10B A flow chart showing a method for detecting a lane reducing situation and transmitting a message to a neighboring vehicle to avoid a collision.

[0034] Fig. 10C A block diagram illustrating a non-transitory computer readable medium that performs operations to detect lane reduction and transmit messages to neighboring vehicles to avoid collisions according to one embodiment.

[0035] Fig.11A An example message illustrating a dedicated short range communications (DSRC) message format.

[0036] Fig. 11B An example message from a host vehicle to a neighboring vehicle is shown.

[0037] Fig. 11C An example message from a host vehicle to a neighboring vehicle is shown.

[0038] Fig.11D An example message from a neighboring vehicle to the host vehicle is shown.

[0039] Fig.11E An example alert message displayed in a host vehicle is shown.

[0040] Fig. 12A The structure of a neural network / machine learning model with a feedback loop according to one embodiment is shown.

[0041] Fig. 12B The structure of a neural network / machine learning model with reinforcement learning according to one embodiment is shown.

[0042] Fig. 12C An example block diagram for detecting a collision event using a machine learning model according to an embodiment is shown.

[0043] Fig.13 An example flow chart for detecting a collision event using a machine learning model is shown according to one embodiment.

[0044] Fig.14A A block diagram of a network security module considering systems and servers is shown.

[0045] Fig. 14B One embodiment of a network security module is shown.

[0046] Fig. 14C Another embodiment of a network security module is shown.

[0047] Other features of the presented embodiments will be apparent from the accompanying drawings and from the detailed description that follows. DETAILED DESCRIPTION

[0048] For simplicity and clarity of explanation, each figure shows the general mode of construction. The description and details of known features and technologies may be omitted in the specification and drawings to avoid unnecessarily obscuring the present disclosure. Each figure amplifies the size of some elements relative to other elements to help improve the understanding of the embodiments of the present disclosure. The same reference numerals in different figures represent the same elements.

[0049] Although the detailed description herein contains many details for the purposes of illustration, one of ordinary skill in the art will appreciate that many variations and alterations to the details are contemplated herein.

[0050] Therefore, the embodiments herein are not limited to any claims set forth herein without loss of generality. The terms used herein are only for the purpose of describing specific embodiments and are not restrictive. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present disclosure belongs.

[0051] As used herein, the articles "a" and "an" used herein refer to one or more than one (i.e., at least one) of the grammatical object of the article. For example, "an element" refers to one element or more than one element. In addition, unless otherwise specified or clearly intended from the context to be singular, the use of the articles "a" and "an" in this specification and the drawings is interpreted to mean "one or more".

[0052] As used herein, the terms "example" and / or "exemplary" mean serving as 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 preferred or advantageous over other aspects or designs, nor does it exclude equivalent exemplary structures and techniques known to those of ordinary skill in the art.

[0053] As used herein, the terms "first", "second", "third", etc. in the specification and in the claims, if any, distinguish similar elements and do not necessarily describe a particular order or chronological sequence. These terms are interchangeable where appropriate, such that the embodiments herein can, for example, operate in an order different from that shown or otherwise described herein. In addition, the terms "including", "having", and any variations thereof cover non-exclusive inclusions, such that a process, method, system, article, equipment, or device comprising a series of elements is not necessarily limited to those elements, but may include other elements that are not explicitly listed or inherent to such a process, method, system, article, equipment, or device.

[0054] As used herein, the terms "left", "right", "front", "back", "top", "bottom", "upper", "lower" and the like in the specification and claims, if any, are used for descriptive purposes and are not necessarily used to describe permanent relative positions. The terms so used are interchangeable where appropriate, such that the embodiments of the apparatus, methods, and / or articles described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein.

[0055] Unless explicitly described as such, the element actions or instructions used in this article are not critical or necessary. In addition, the term "set" includes items (e.g., related items, unrelated items, combinations of related items and unrelated items, etc.) and can be interchanged with "one or more". In the case of only one item, the term "one" or similar language is used. In addition, the term "having", "having" or similar terms are open terms. In addition, unless explicitly stated otherwise, the phrase "based on" means "based at least in part on".

[0056] As used herein, the terms "system", "device", "unit" and / or "module" refer to different components, component parts or components at various levels of magnitude. However, other expressions that achieve the same purpose can be replaced with these terms.

[0057] As used herein, the terms "coupled," "coupled," "coupled," and similar terms refer to connecting two or more elements mechanically, electrically, and / or otherwise. Two or more electrical elements may be electrically coupled together, rather than mechanically or otherwise coupled together. The coupling may be for any length of time, e.g., permanent or semi-permanent or only momentary. "Electrical coupling" includes all types of electrical couplings. The absence of the words "removably," "removable," and similar terms adjacent to the word "coupled" and similar terms does not mean that the coupling, etc. in question is removable or not removable.

[0058] As used herein, the term "or" is intended to be an inclusive or rather than an exclusive or. That is, unless specified otherwise, or clear from the context, "X employs A or B" means any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then "X employs A or B" is satisfied under any of the foregoing instances.

[0059] As used herein, two or more elements are "unitary" if they comprise the same piece of material. Two or more elements are "non-unitary" if they each comprise different pieces of material.

[0060] As used herein, the term "real time" refers to operations that are actually performed as quickly as possible when a triggering event occurs. The triggering event may include receiving data necessary to perform a task or otherwise process information. Due to the inherent delays in transmission and / or computing speeds, the term "real time" encompasses operations that occur in "near" real time or are slightly delayed from the triggering event. In several embodiments, "real time" may mean that the real time is less than 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.

[0061] The present invention may be implemented in other specific forms without departing from its spirit or features. The described embodiments should be considered in all respects to be illustrative only and not restrictive. Therefore, the scope of the present invention is indicated by the appended claims rather than by the preceding description. All variations within the equivalent meaning and scope of the claims are to be included within their scope.

[0062] As used herein, the term "approximately" may mean within a specified or unspecified range of a specified or unspecified value. In some embodiments, "approximately" may mean within plus or minus ten percent of the value. In other embodiments, "approximately" may mean within plus or minus five percent of the value. In further embodiments, "approximately" may mean within plus or minus three percent of the value. In still other embodiments, "approximately" may mean within plus or minus one percent of the value.

[0063] Without departing from the spirit or features of the present invention, other specific forms may implement the present invention. The described embodiments are illustrative and non-restrictive in all respects. Therefore, the appended claims rather than the description herein indicate the scope of the present invention. All variations within the equivalent meanings and scopes of the claims are within their scope.

[0064] As used herein, the term "component" broadly interprets hardware, firmware, and / or a combination of hardware, firmware, and software.

[0065] Digital electronic circuits, or in the form of computer software, firmware or hardware, including the structures disclosed in this specification and their structural equivalents, or one or more combinations thereof, can implement the implementation and all functional operations described in this specification. The implementation can be one or more computer program products, that is, one or more modules of computer program instructions encoded on a computer-readable medium, for execution by a data processing device or controlling the operation of the data processing device. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a combination of substances that affect machine-readable propagation signals, or one or more combinations thereof. The term "computing system" encompasses all devices, equipment and machines for processing data, including, for example, 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 computer program in question, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or one or more combinations thereof. A propagation signal is an artificially generated signal (e.g., a machine-generated electrical, optical or electromagnetic signal) that encodes information for transmission to a suitable receiver device.

[0066] The actual dedicated control hardware or software code used to implement these systems and / or methods is not limited to these implementations. Thus, any software and any hardware can implement the systems and / or methods based on the description in this article without reference to specific software codes.

[0067] A computer program (also referred to as a program, software, software application, script or code) is written in any suitable form of programming language, including compiled or interpreted languages. Any suitable form, including independent programs or modules, components, subroutines or other units suitable for use in a computing environment, can deploy the computer program. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a part of a file holding other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple collaborative files (e.g., files storing one or more modules, subroutines or code portions). A computer program can be executed on a computer, or on multiple computers located in one location or distributed in multiple locations and interconnected by a communication network.

[0068] One or more programmable processors execute one or more computer programs to perform functions by operating on input data and generating output, performing the processes and logic flows described in this specification. These processes and logic flows can also be performed by special-purpose logic circuits, and the device can also be implemented as a special-purpose 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.

[0069] Processors suitable for executing computer programs include, for example, general and special purpose microprocessors, and any one or more processors of digital computers of any appropriate type. The processor will 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 memory devices for storing instructions and data. The computer will also include or be operably coupled to one or more large-capacity storage devices for storing data, such as a magnetic disk, a magneto-optical disk, an optical disk or a solid-state disk, so as to receive data, transmit data or both therefrom. However, the computer does not need to have such a device. In addition, another device such as a mobile phone, a personal digital assistant (PDA), a mobile audio player, a global positioning system (GPS) receiver, etc. can be embedded in the computer. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices (e.g., erasable programmable read-only memory (EPROM), electrically 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 incorporate the processor and memory.

[0070] To provide interaction with a user, a computer may have a display device, such as a cathode ray tube (CRT) or liquid crystal display (LCD) monitor, 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 kinds of devices also provide interaction with the user. For example, feedback to the user may be any suitable form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and a computer may receive input from a user in any suitable form, including sound, voice, or tactile input.

[0071] A computing system comprising a back-end component such as a data server, or comprising a middleware component such as an application server, or comprising a front-end component such as a client computer with a graphical user interface or a web browser (wherein a user can interact with the implementation through a graphical user interface or a web browser), or any suitable combination of one or more such back-ends, middleware or front-end components can implement the implementation described herein. Digital data communication of any suitable form or medium, such as a communication network, can interconnect the components of the system. Examples of communication networks include local area networks (LANs) and wide area networks (WANs), such as intranets and the Internet.

[0072] A computing system may include clients and servers. The client and server are remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0073] Embodiments of the present invention may include or utilize a special 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 by a general or special purpose computer system. A computer readable medium storing computer executable instructions is a physical storage medium. A computer readable medium carrying computer executable instructions is a transmission medium. Thus, by way of example and not limitation, embodiments of the present invention may include at least two different types of computer readable media: a physical computer readable storage medium and a transmission computer readable medium.

[0074] Although the embodiments of the present invention are described herein with reference to specific example embodiments, it will be apparent that various modifications and changes may be made to the 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 enable 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.

[0075] Additionally, 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.

[0076] Physical computer-readable storage media include RAM, ROM, EEPROM, CD-ROM or other optical disk storage (such as CD, DVD, etc.), magnetic disk storage or other magnetic storage devices, solid-state disks or any other media. They store the desired program code in the form of computer-executable instructions or data structures that can be accessed by general or special-purpose computers.

[0077] As used in this article, the term "network" refers to one or more data links that enable electronic data to be transmitted between computer systems and / or modules and / or other electronic devices. When a network or another communication connection (any one of hard-wired, wireless, or a combination of hard-wired or wireless) transmits or provides information to a computer, the computer appropriately regards the connection as a transmission medium. General or special-purpose computers access transmission media, which may include networks and / or data links that carry the desired program code of computer executable instructions or data structure forms. The scope of computer-readable media includes the combination of the above, which enables electronic data to be transmitted between computer systems and / or modules and / or other electronic devices.

[0078] 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 within a network interface module (NIC) and then ultimately transferred to computer system RAM and / or a less volatile computer readable physical storage medium at the computer system. Thus, computer system components that also (or even primarily) utilize transmission media may include computer readable physical storage media.

[0079] Computer executable instructions include, for example, instructions and data that cause a general-purpose computer, a special-purpose computer, or a special-purpose processing device to perform a specific function or group of functions. Computer executable instructions can be, for example, binary, intermediate format instructions, such as assembly language, or even source code. Although the subject matter described herein is a language dedicated to structural features and / or method actions, the described features or actions do not limit the subject matter defined in the claims. On the contrary, the features and actions described herein are example forms of implementing the claims.

[0080] Although this specification contains many details, these are not to be construed as limitations on the scope of the present disclosure or claims, but rather as descriptions of features specific to a particular embodiment. A single embodiment may implement certain features described in this specification in the context of a separate embodiment. Conversely, multiple embodiments may implement the various features described herein in the context of a single embodiment individually or in any suitable sub-combination. In addition, although the features described herein function in certain combinations and are even initially claimed as such, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may be directed to a sub-combination or a variation of the sub-combination.

[0081] Similarly, although the operations depicted in the accompanying drawings in a particular order achieve the desired results, this should not be understood as requiring such operations to be performed in the particular order shown or in a continuous order or requiring all of the operations shown to be performed to achieve the desired results. 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 described program components and systems can be integrated together in a single software product or packaged into multiple software products.

[0082] Even though particular combinations of features are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. Other implementations are within the scope of the claims. For example, the actions recited in the claims can be performed in a different order and still achieve the desired results. In fact, many of these features can be combined in ways not specifically recited in the claims and / or disclosed in the specification. Although each dependent claim may be directly dependent on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set.

[0083] In addition, a computer system including one or more processors and a computer-readable medium such as a computer memory can practice these methods. In particular, one or more processors execute computer-executable instructions stored in the computer memory to perform various functions such as the actions recorded in the embodiments.

[0084] Those skilled in the art will appreciate that the present invention can be practiced in a network computing environment with many kinds of computer system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile phones, PDAs, pagers, routers, switches, etc. The present invention can also be practiced in a distributed system environment, in which local and remote computer systems linked by a network (by a hardwired data link, a wireless data link, or by a combination of hardwired and wireless data links) all perform tasks. In a distributed system environment, program modules can be located in both local and remote memory storage devices.

[0085] Unless otherwise indicated, the following terms and phrases shall be understood to have the following meanings.

[0086] As used herein, the term "lane reduction" or "road diet" or "road re-channelization" or "road conversion" is a technique in transportation planning whereby the number of driving lanes and / or the effective width of a road is reduced in order to achieve system improvements.

[0087] As used herein, the term "recently disappearing lane" is an adjacent lane that is completely blocked or merged into another lane by reducing lane width.

[0088] As used herein, the term "vehicle" refers to an object used to transport people or goods. Cars, sedans, trucks, buses, etc. are examples of vehicles.

[0089] As used herein, the term “host vehicle” refers to a vehicle that includes an embodiment of the system described herein.

[0090] As used herein, the term “nearby vehicle” or “neighboring vehicle” refers to vehicles surrounding the user's vehicle and within the reach of at least the communication range of the user's vehicle, where the communication range is defined as the maximum distance over which communication can exist between two antennas, one of which is the user's vehicle antenna in the wireless network.

[0091] As used herein, the term "lane vanishing point" refers to the point on a lane where lane reduction begins. It refers to the point where the lane width begins to gradually decrease.

[0092] 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 make predictions about 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 neighbor, decision tree, decision tree learning, and hidden Markov, etc. For clarity, a part of the machine learning process may 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 performing statistical analysis, such as regression using a spreadsheet program. The machine learning process can continuously learn and adjust the classifier when new data becomes available, and does not rely on explicit or rule-based programming. ANN may have the characteristics of a feedback loop to dynamically adjust the system output when it becomes available to learn from new data. In machine learning, back propagation and feedback loops are used to train artificial intelligence / machine learning (AI / ML) models, thereby improving the accuracy and performance of the model over time.

[0093] Statistical modeling relies on finding relationships between variables (e.g., mathematical equations) to predict outcomes.

[0094] As used herein, the term "data mining" is the process used to transform raw data into useful information.

[0095] 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 manipulated by a computer. Data acquisition systems typically convert analog waveforms into digital values ​​for processing. The 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 conditioned sensor signals into digital values. Stand-alone data acquisition systems are often referred to as data loggers.

[0096] As used herein, the term "dashboard" is an interface that visualizes specific key performance indicators (KPIs) for a specific goal or process. It is based on data visualization and information graphics.

[0097] 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.

[0098] As used herein, the term "set of data" (or "dataset") is a collection of data. In the case of tabular data, a dataset corresponds to one or more database tables, where each column of the table represents a particular variable, and each row corresponds to a given record of the dataset in question. A dataset lists the value of each variable for each member of the dataset, such as the height and weight of an object. Each value is called a datum. A dataset may also consist of a collection of documents or files.

[0099] As used herein, a "sensor" is a device that measures physical input from its environment and converts it into data that can be interpreted by either a person or a machine. Most sensors are electronic, presenting electronic data, but some sensors are simpler, such as a glass thermometer that presents visual data.

[0100] The term "electronic control unit" (ECU), also known as "electronic control module" (ECM), is a module that controls one or more subsystems. Herein, an ECU may be installed in a car or other motor vehicle. It may refer to many ECUs, and may include, but is not limited to, an engine control module (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 (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 may be an embedded system in an automotive electronics system. In another example, an electronic control unit is wirelessly coupled to automotive electronics.

[0101] As used herein, the term "infotainment system" or "in-vehicle infotainment system" (IVI) refers to a combination of vehicle systems for delivering entertainment and information. In one example, information can be delivered to the driver and passengers of the vehicle / occupants through an audio / video interface, control elements similar to a touch screen display, a button panel, voice commands, etc. Some of the main components of an in-vehicle infotainment system are an integrated head unit, a head-up display, a high-end digital signal processor (DSP) and a graphics processing unit (GPU) to support multiple displays, an operating system, a controller area network (CAN), low voltage differential signaling (LVDS) and other network protocol support (as needed), a connectivity module, automotive sensor integration, a digital instrument cluster, etc.

[0102] As used herein, the term "environment" or "surroundings" refers to the surroundings and space that the vehicle is cruising in. It refers to the dynamic surroundings that the vehicle is cruising in, including other vehicles, obstacles, pedestrians, lane boundaries, traffic signs and signals, speed limits, potholes, snow, standing water, etc.

[0103] As used herein, the term "autonomous mode" refers to an independent and unsupervised mode of operation.

[0104] As used herein, the term "manual mode" refers to an operating mode that hands driving control of a vehicle over to a human driver to allow them to manually drive the vehicle.

[0105] As used herein, the term "autonomous communication" includes communication over time periods with minimal supervision in different scenarios and is not solely or entirely based on pre-coded scenarios or pre-coded rules or pre-defined protocols. In general, autonomous communication occurs in an independent and unsupervised manner. In one embodiment, the communication module is enabled for autonomous communication.

[0106] As used herein, the term "autonomous vehicle", also known as self-driving vehicle, unmanned vehicle, robotic vehicle, refers to a vehicle that incorporates vehicle automation, that is, a ground vehicle that can sense its environment and move safely with little or no human input. Self-driving vehicles combine various sensors to sense their surroundings, such as temperature recording cameras, radio detection and ranging (radar), light detection and ranging (lidar), sound navigation and ranging (sonar), global positioning system (GPS), odometry, and inertial measurement units. A control system designed for this purpose interprets the sensor information to identify appropriate navigation paths as well as obstacles and related landmarks.

[0107] As used herein, the term "communication system" or "communication module" refers to a system that enables the exchange of information between two points. The process of transmission and reception of information is called communication. The main elements of communication include, but are not limited to, a sender of information, a channel or medium for communication, and a receiver of information.

[0108] As used herein, the term "connection" refers to a communication link. It refers to a communication channel that connects two or more devices for the purpose of data transmission. It can refer to a physical transmission medium, such as a wire, or to a logical connection through a multiplexed medium, such as a wireless channel in telecommunications and computer networks. A channel is used for information transmission, such as a digital bit stream from one or several transmitters to one or several receivers. A channel has a specific capacity for transmitting information, usually measured by its bandwidth in Hertz (Hz) or its data rate in bits per second. For example, vehicle-to-vehicle (V2V) communication can wirelessly exchange information about the speed, location, and direction of travel of surrounding vehicles.

[0109] As used herein, the term "communication" refers to the transmission of information and / or data from one point to another. Communication can be by means of electromagnetic waves. It is also a flow of information from one point called a source to another point called a receiver. Communication includes one of the following: transmission of 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 with..." may refer to any coupling, connection or interaction using electrical signals to exchange information or data, using any system, hardware, software, protocol or format, regardless of whether the exchange occurs wirelessly or through a wired connection. The term communication includes systems that combine other more specific types of communication such as V2I (vehicle to infrastructure), 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) and vehicle to everything (V2X) communication. V2X communication is the transmission of information from a vehicle to any entity that may affect the vehicle, and vice versa. The main motivations for developing V2X are passenger safety, road safety, traffic efficiency, and energy efficiency. Depending on the underlying technology adopted, there are two types of V2X communication technologies: cellular networks and other technologies that support direct device-to-device communication (such as dedicated short-range communication (DSRC), port communication system (PCS), In addition, the emergency communication device is configured on a computer with a communication function and is connected to perform two-way communication with the on-board emergency reporting device via a radio station and a communication line such as a public telephone network or via satellite communication via a communication satellite. The emergency communication device is suitable for communicating with communication terminals including road management offices, police stations, fire departments and hospitals through a communication network. The emergency communication device can also be connected online with a communication terminal of a relevant person associated with the occupant of the emergency reporting vehicle (the driver receiving the service).

[0110] The term "Vehicle-to-Vehicle (V2V) communication" refers to technology that allows vehicles to broadcast and receive messages. Messages can be omnidirectional, creating 360-degree "awareness" of other vehicles in the vicinity. Vehicles can be equipped with appropriate software (or safety applications) that can use messages from surrounding vehicles to determine potential collision threats as they develop.

[0111] The term "alert" or "alert signal" refers to a communication that attracts attention. Alerts may include visual, tactile, audible alerts, and combinations of these alerts to warn the driver or occupants. These alerts allow the driver or occupants to have the ability to react and respond quickly.

[0112] As used herein, the term "occupant" refers to a passenger in a vehicle, and it includes the driver. Passenger and occupant are used interchangeably and refer to a person in a vehicle during a ride.

[0113] The term "computer vision system" allows a vehicle to "see" and interpret the world around it. The system uses a combination of cameras, sensors, and other technologies such as lidar, radar, GPS, machine learning algorithms, etc. to collect visual data about the vehicle's surroundings and analyze the data in real time. Computer vision systems are 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 determine the vehicle's speed and direction, adjust its trajectory to avoid collisions, and apply braking or acceleration as needed. It allows the vehicle to cruise safely and efficiently in a variety of driving conditions.

[0114] The term "deterministic motion model" is a mathematical description of the motion of an object or system that is completely determined by its initial state and inputs. This means that given the starting position, velocity, and other relevant parameters of the system, as well as the inputs it receives (such as forces or torques), the model can accurately predict its future position, velocity, and other states at any point in time. Deterministic motion models can be used to simulate the behavior of mechanical systems (such as vehicles) to understand, plan, and optimize their motion, or to control their motion in real time. Such models are typically based on physical principles, such as Newton's laws of motion, and may also take into account environmental factors, such as friction, gravity, surface conditions, or wind resistance. In summary, the goal of a deterministic motion model is to provide a reliable and accurate way to predict the behavior of a system.

[0115] As used herein, the term "subcompact vehicle" refers to a vehicle that typically weighs less than 2,500 pounds and has a length of approximately 10 feet or less.

[0116] As used herein, the term "compact vehicle" refers to a vehicle that is slightly larger than a sub-compact vehicle, having a weight between 2,500 pounds and 3,500 pounds and a length of approximately 14 feet or less.

[0117] As used herein, the term "mid-size vehicle" refers to a larger than compact vehicle, having a weight between 3,500 pounds and 4,500 pounds and a length of approximately 15 feet to 18 feet.

[0118] As used herein, the term "full-size vehicle" refers to a vehicle that is larger than a mid-size vehicle, having a weight between 4,500 pounds and 5,500 pounds and a length of approximately 18 feet to 20 feet.

[0119] As used herein, the term "sport utility vehicle" refers to a vehicle that is typically larger than a compact vehicle, weighing between 4,500 and 7,500 pounds and about 15 to 25 feet in length. They are designed to be used on and off-road and have high ground clearance.

[0120] As used herein, the term "truck" refers to a vehicle designed to carry cargo, and their size can vary widely depending on their intended use. Light-duty trucks weigh between 6,000 and 10,000 pounds, medium-duty trucks weigh between 10,000 and 26,000 pounds, and heavy-duty trucks weigh over 26,000 pounds.

[0121] As used herein, the term "bus" refers to a vehicle designed to carry multiple passengers, and their size can vary widely depending on the type of bus. School buses are typically between 23 feet and 40 feet long and weigh between 10,000 pounds and 36,000 pounds. Transit buses can be up to 45 feet long and weigh 44,000 pounds or more.

[0122] As used herein, the term "cones" refers to conical traffic diversion markers. They come in many different colors, with orange, yellow, pink, and red being the most common colors due to their brightness. Some traffic cones are green and blue. Some traffic cones may also have reflective strips (often called "flash strips") to increase their visibility. They are used to demarcate areas where work is ongoing or where traffic needs to be redirected.

[0123] As used herein, the term "barricade" refers to a traffic diversion sign, which is a traffic sign often used to block an entire section of road or sidewalk. They are typically made of plastic or metal and can be arranged in a variety of configurations to form a barrier.

[0124] As used herein, the term "Jersey barriers" refers to traffic diverters, which are large concrete or plastic barriers used to separate traffic or block off certain areas. They are often used in construction zones or to protect buildings and other structures.

[0125] As used herein, the term "traffic barrel" refers to a traffic diversion marker that is similar to a traffic cone but is larger and more visible. They are often used to create temporary lane deviations or to block off an area where work is in progress.

[0126] As used herein, the term "portable traffic signal" refers to a traffic diversion sign used to control intersections or other areas where traffic needs to be redirected. They are typically battery powered and can be quickly installed and removed.

[0127] As used herein, the term "temporary pavement markings" refers to traffic diversion markings, such as tape, paint, or other materials, to mark areas where traffic needs to be redirected. They are often used in construction zones or to demarcate areas for special events.

[0128] As used herein, the term "flaggers" refers to people who are trained to direct traffic using hand signals, flags, or other means. They are often used in construction zones or other areas where traffic needs to be redirected.

[0129] As used herein, the term "traffic control processor" is a computer system used to manage and control traffic flow. It provides real-time traffic information, such as traffic conditions, road closures, and accidents, to vehicle systems and / or occupants. This information may be displayed on variable message signs, mobile applications, or other communication channels.

[0130] The term "application server" refers to a server that hosts applications or software that delivers business applications through communication protocols. The application server framework is a service layer model. It includes software components that are available to software developers through application programming interfaces. It is system software that resides between the operating system (OS) on one side, external resources such as database management systems (DBMS), communications and Internet services on another side, and user applications on a third side.

[0131] As used herein, the term “cybersecurity” refers to the application of technologies, processes, and controls to protect systems, networks, programs, devices, and data from cyber attacks.

[0132] As used herein, the term "cybersecurity module" refers to a module that includes the application of technologies, processes and controls to protect systems, networks, programs, devices and data from cyber attacks and threats. Its purpose is to reduce the risk of cyber attacks and prevent unauthorized use 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.

[0133] The term "encryption" as used herein refers to the use of one or more mathematical techniques and a password or "key" for decrypting information to protect digital data. It refers to converting information or data into code, especially to prevent unauthorized access. It can also refer to hiding information or data by converting information or data into code. It can also be called a password, code, cipher, 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 the value will be transmitted to the recipient (multiple recipients) over the network.

[0134] The term "decryption" as used herein refers to the process of converting an encrypted message back to its original format. It is generally the reverse process of encryption. It decodes the encrypted information so that only authorized users can decrypt the data, as decryption requires a secret key or password. The term can be used to describe methods of decrypting data manually or using an appropriate code or key.

[0135] The term "cybersecurity threat" as used herein refers to any possible malicious attack that seeks to illegally access data, disrupt digital operations, or damage information. Malicious behavior generally includes, but is not limited to, damaging data, stealing data, or destroying digital life. Cyber ​​threats include, but are not limited to, malware, spyware, phishing attacks, pass-through software, zero-day malicious exploits, Trojans, advanced persistent threats, wiper attacks, data manipulation, data destruction, rogue software, malware, unattack software, computer viruses, man-in-the-middle attacks, data breaches, denial of service (DoS) attacks, and other attack vectors.

[0136] The term "hash value" as 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 numerical value, i.e., a hash value, is generated that identifies the contents of the file. If the contents are modified in any way, the hash value will also change significantly. Example algorithms for generating hash values: Message Digest-5 (MD5) algorithm and Secure Hash Algorithm-1 (SHA1).

[0137] The term "integrity check" as used herein refers to checking the accuracy and consistency of system-related files, data, etc. It can be performed using a checking tool that detects 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 data in a database and the level at which a user checks the quality, completeness, and reliability of the data. Data integrity checking verifies that the data in the database is accurate and functions as expected within a given application.

[0138] The term "alarm" as used herein refers to a trigger when a component or system in a system fails or does not 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 adverse situations or cascading effects.

[0139] As used herein, the term "communicating with" refers to any coupling, connection, or interaction using electrical signals to exchange information or data, using any system, hardware, software, protocol, or format, regardless of whether the exchange occurs wirelessly or through a wired connection.

[0140] 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 applies encryption methods, usually as a sequence of encryption primitives. The protocol describes how the algorithm should be used. A sufficiently detailed protocol includes details about data structures and representations, at which point it can be used to implement multiple interoperable versions of the program. Cryptographic protocols are widely used for secure application-level data transmission. Cryptographic protocols typically include at least some of these 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. Hash algorithms can be used to verify the integrity of data. Secure Sockets Layer (SSL) and Transport Layer Security (TLS), the successor protocols to SSL, are encryption protocols that can be used by network switches to protect data communications on the network.

[0141] As used herein, the term "network" may include the Internet, a local area network, a wide area network, or a combination thereof. A network may 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, a connection may include a wired connection (such as a wire, a cable, a fiber optic line, etc.), a wireless connection, or a combination thereof. In addition, although not shown, other computers, systems, devices, and networks may also be connected to the network. A network refers to any group of devices or subsystems connected by a link (directly or indirectly) connecting a group of terminal nodes that share resources located on or provided by a network node. Computers communicate with each other using a public communication protocol on a digital interconnect. For example, a subsystem may include a cloud. A cloud refers to a server accessed via the Internet, and the software and databases running on those servers.

[0142] The embodiments described herein may be directed to one or more of the systems, methods, devices, and / or computer program products of any possible technical detail integration level. A computer program product may include a computer-readable storage medium (or multiple media) having computer-readable program instructions thereon, which are used to cause a processor to perform various aspects of one or more embodiments described herein. A computer-readable storage medium may be a tangible device that can retain and store instructions used by an instruction execution device. For example, a 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 foregoing. A non-exhaustive list of more specific examples of computer-readable storage media may also include the following: a portable computer 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 such as a punch card or a raised structure in a groove on which instructions are recorded, and / or any suitable combination of the foregoing. As used herein, computer-readable storage media does not interpret transient signals themselves, such as radio waves and / or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides and / or other transmission media (e.g., light pulses passing through fiber optic cables), and / or electrical signals transmitted through wires.

[0143] The computer-readable program instructions described herein can be downloaded to the corresponding computing / processing equipment and / or downloaded to an external computer or external storage device via a network from a computer-readable storage medium, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can 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 the computer-readable program instructions from the network, and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in the corresponding computing / processing device. The computer-readable program instructions for performing the operation of one or more embodiments described herein can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, instructions that depend on the machine, microcode, firmware instructions, state setting data, configuration data of integrated circuits, and / or source code and / or object code written in any combination of one or more programming languages, programming languages ​​including object-oriented programming languages ​​(such as Smalltalk, C++ or similar) and / or process programming languages ​​(such as "C" programming languages ​​and / or similar programming languages). Computer-readable program instructions may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and / or partially on a remote computer, or entirely on a remote computer and / or server. In the latter scenario, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider). In one or more embodiments, electronic circuits including, for example, programmable logic circuits, field programmable gate arrays (FPGAs), and / or programmable logic arrays (PLAs) may be executed by utilizing state information of computer-readable program instructions to personalize the electronic circuits so as to perform aspects of one or more embodiments described herein.

[0144] The various aspects of one or more embodiments described herein are described with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to one or more embodiments described herein. Each frame of the flowchart and / or block diagram and the combination of frames in the flowchart and / or block diagram can be realized by computer-readable program instructions. These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer and / or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device can create a device for realizing the function / action specified in one or more frames of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can guide a computer, a programmable data processing device and / or other equipment to work in a specific manner, so that the computer-readable storage medium having instructions stored therein may include such a product, which includes instructions for the aspects of the function / action specified in one or more frames of the flowchart and / or block diagram. 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 actions to be performed on the computers, other programmable devices and / or other equipment to produce a computer-implemented process, so that the instructions executed on the computers, other programmable devices and / or other equipment implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0145] The flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, function and / or operation of the system, computer-implemented method and / or computer program product according to one or more embodiments described herein. In this regard, each frame in the flow chart or block diagram may represent a module, segment and / or portion of an instruction, which includes one or more executable instructions for implementing a specified logical function (multiple logical functions). In one or more alternative implementations, the functions marked in the frame may not occur in the order marked in the figure. For example, depending on the functions involved, the two frames shown in succession may be executed substantially simultaneously, and / or these frames may sometimes be executed in reverse order. It will also be noted that each frame of the block diagram and / or flow chart and / or the combination of frames in the block diagram and / or flow chart may be implemented by a dedicated hardware-based system that can perform specified functions and / or actions and / or perform one or more combinations of dedicated hardware and / or computer instructions.

[0146] Although the subject matter described herein is in the general context of computer executable instructions of computer program products running on computers and / or multiple computers, it will be appreciated by those skilled in the art 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 similarities that perform specific tasks and / or implement specific abstract data types.In addition, computer system configurations including single processor and / or multiprocessor computer systems, small computing devices, large computers and computers, handheld computing devices (e.g., PDAs, phones), microprocessor-based or programmable consumer products and / or industrial electronics and / or similar methods implemented by the computer described herein may be practiced.Distributed computing environments may also practice the various aspects shown, in which remote processing devices linked by a communication network perform tasks.However, stand-alone computers may practice one or more aspects of one or more embodiments described herein, if not all aspects.In distributed computing environments, program modules may be located in both local and remote memory storage devices.

[0147] As used in this application, the terms "component", "system", "platform", "interface" and / or similar terms may refer to and / or may include computer-related entities or entities related to operating machines with one or more specific functions. The entity described herein may be any of 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. As an illustration, both the application program and the server running on the server may be components. One or more components may reside in a process and / or a thread of execution, and the component may be located on a computer and / or distributed between two or more computers. In another example, the corresponding component may be executed from various computer-readable media having various data structures stored thereon. These components may communicate via local and / or remote processes, such as according to a signal with one or more data packets (e.g., data from a component, which interacts with another component in a local system, a distributed system, and / or interacts with other systems via a network such as the Internet). As another example, a component may be a device having a specific function provided by a mechanical component operated by an electrical or electronic circuit, which is operated by a software and / or firmware application 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 application. As yet another example, a component may be a device that provides a specific function by electronic components rather than mechanical components, wherein the electronic components may include a processor and / or other device to execute the software and / or firmware that at least partially imparts the function to the electronic components. In one aspect, the component may simulate the electronic component via a virtual machine, such as within a cloud computing system.

[0148] 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 processor with software multithreaded execution capability; a multi-core processor; a multi-core processor with software multithreaded execution capability; a multi-core processor with hardware multithreading technology; a parallel platform; and / or a parallel platform with distributed shared memory. In addition, a processor may 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 designed to perform the functions described herein. In addition, the processor may adopt a nanoscale architecture, such as but not limited to molecular-based transistors, switches, and / or gates, in order to optimize space usage and / or enhance the performance of related devices. A combination of computational processing units may implement a processor.

[0149] As used herein, terms such as "storage," "memory," "data storage," "data storage," "database," and any other information storage components related to the operation and functionality of a component refer to a "memory component," an entity embodied in "memory," or a component that includes a memory. The memory and / or memory components described herein may be either volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. By way of illustration 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 as external cache memory, such as by way of illustration and not limitation, RAM is available in many 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 Rambus dynamic RAM (DRDRAM), and / or Rambus dynamic RAM (RDRAM). In addition, the memory components of the systems and / or computer-implemented methods described herein include, but are not limited to, only these and / or any other suitable types of memory.

[0150] 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, to the extent the terms "include," "have," "have," or similar terms are used in the detailed description, claims, appendices, and / or drawings, these terms are intended to be inclusive in a manner similar to that in which the term "comprising" is interpreted when used as a transitional word in the claims.

[0151] The description of one or more embodiments is for the purpose of illustration, not to be exhaustive or to limit 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 found in the marketplace, and / or enable others of ordinary skill in the art to understand the embodiments described herein.

[0152] Embodiments are provided involving systems and methods for automatic action taken by a vehicle when another vehicle turns into its lane, which may occur for a variety of reasons, like detecting a problem in the lane ahead or realizing they are in the wrong lane for an exit. Slamming on the brakes immediately may cause an accident coming from behind or into a sidewalk or other obstacle. The vehicle needs to make decisions based on its surroundings, and early prediction is critical. When a vehicle including the system senses a fast-moving neighboring vehicle, the vehicle's sensing becomes high, and the vehicle can adjust and slow down to let the neighboring vehicle pass. The vehicle can predict the neighboring vehicle's intentions based on its map and adjust its speed accordingly.

[0153] Reference Figure 1A , some drivers are distracted and they notice too late that Lane 1 is disappearing. a) As soon as they realize that the lane is disappearing, they immediately try to merge into Lane 2, which puts other drivers at risk. Figure 1B , the driver is not distracted, but he / she believes they can overtake and merge, but sometimes this miscalculation results in two vehicles next to each other in lane reduction. Autonomous vehicles may also face these situations.

[0154] Reference Figure 2A , Figure 2B and Figure 2C , which show a real-world scenario due to lane reduction. Figure 2A In , two vehicles V1 and V2 traveling at the same speed are approaching a lane reduction. Figure 2B At some point, the lanes begin to disappear and both vehicles continue to drive next to each other. Figure 2C In the example, V2 merges into the lane too late and hits V1.

[0155] In one embodiment, the vehicle in which the emergency is occurring will transmit a message. It may use any one or a combination of vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), or vehicle-to-infrastructure (V2I) types of communication. In one embodiment, it uses vehicle-to-vehicle (V2V) communication, which enables vehicles to wirelessly exchange information about their speed, location, and heading.

[0156] Neighboring vehicle detection module: V2V will be based on a peer-to-peer mesh network, in which each element (vehicle) of the network is able to generate, receive and relay messages. With this approach, a vast network can be created in a populated area without the need for expensive infrastructure. Typically, each vehicle will be able to transmit information about their speed, direction, positioning, braking and lane change intentions. In one embodiment, V2V communication is used to detect and communicate with neighboring vehicles. V2V will form a mesh network using dedicated short-range communication (DSRC). Dedicated short-range communication (DSRC) is a wireless communication technology or infrastructure technology designed to allow cars in an intelligent transportation system (ITS) to communicate with other cars.

[0157] Embodiments described herein relate to a system for identifying lane reduction and making appropriate adjustments to a vehicle's speed and trajectory to avoid a collision. The system includes a computing platform, the computing platform including a processor, a computer vision module, a communication module, and a control system, wherein the system is configured to: detect lane reduction in a lane that has recently disappeared by the processor; detect neighboring vehicles and driving actions of neighboring vehicles in the lane that has recently disappeared by the computer vision module; detect license plates of neighboring vehicles by the computer vision module; receive a first signal for a driving mode from a neighboring vehicle by the communication module; detect a point where a lane disappears in the lane that has recently disappeared by the computer vision module; determine a time interval for a neighboring vehicle to reach a point where a lane disappears by the processor; detect driving actions of the neighboring vehicle via the processor; perform a risk assessment of a collision based on the driving actions by the processor; and transmit a control command to an actuator system of a host vehicle via the communication module based on the risk assessment.

[0158] In one embodiment, the system is configured as a component of a host vehicle.

[0159] In another embodiment, the host vehicle is a first autonomous vehicle.

[0160] In yet another embodiment, the neighboring vehicle is one of a second autonomous vehicle and a non-autonomous vehicle.

[0161] In yet another embodiment, the driving mode is received via one of V2X communication or V2V communication.

[0162] In yet another embodiment, the driving mode is one of an autonomous mode and a manual mode.

[0163] In yet another embodiment, lane reduction is detected via a processor using information received via at least one of GPS, traffic signals received via a traffic control processor, and a computer vision module.

[0164] In yet another embodiment, neighboring vehicles are detected using at least one of radar, lidar, camera, GPS, and machine learning algorithms.

[0165] In yet another embodiment, lane reduction is detected using at least one of radar, lidar, camera, GPS, and a machine learning algorithm.

[0166] In yet another embodiment, the vehicle type of the neighboring vehicle is determined from the license plate number by referencing a database.

[0167] In yet another embodiment, signals from neighboring vehicles are received via vehicle-to-vehicle (V2V) communications.

[0168] In yet another embodiment, 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) technologies. Dedicated short-range communication (DSRC) is a wireless communication technology that enables short-range communication between vehicles and roadside infrastructure. DSRC operates on the 5.9GHz frequency band and is designed to support vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. C-V2X technology operates on an LTE (Long Term Evolution) network and is based on 3GPP (Third Generation Partnership Project) standards. C-V2X technology can use both direct short-range communication and network-based communication modes to implement a range of safety and non-safety applications. Using C-V2X, vehicles can communicate with each other and with surrounding infrastructure in real time, exchanging information such as speed, positioning, and direction. This information can be used to warn drivers of potential dangers, optimize traffic flow, and improve the overall safety and efficiency of transportation systems. C-V2X is a key communication technology considered for use in the development of connected and automated vehicles (CAVs). C-V2X complements other communication technologies, such as dedicated short-range communications (DSRC), and helps create a more robust and reliable communication system for CAVs.

[0169] DSRC is primarily used for safety applications in the transportation sector, such as collision avoidance, traffic signal timing optimization, and emergency vehicle warning systems. Using DSRC, vehicles can communicate with each other and with roadside infrastructure in real time, exchanging information such as speed, location, and direction. This information can be used to warn drivers of potential dangers and improve the overall safety and efficiency of the transportation system.

[0170] DSRC is also being used in the development of connected and automated vehicles (CAVs) as it provides a means for CAVs to communicate with each other and with surrounding infrastructure. DSRC is one of the communication technologies being considered for use in a connected vehicle environment, along with cellular-based technologies such as 4G and 5G.

[0171] In yet another embodiment, the signal from the neighboring vehicle is received via an Internet connection. The vehicle can establish an Internet connection using a variety of methods, including cellular networks, satellite communications, and dedicated short-range communications (DSRC).

[0172] In yet another embodiment, the driving mode of the neighboring vehicle is a manual mode.

[0173] In yet another embodiment, the driving maneuver of the neighboring vehicle is detected by capturing at least one of a steering angle, a steering rate, a speed, and an acceleration via a computer vision module.

[0174] In yet another embodiment, the host vehicle is configured to establish communications with neighboring vehicles to obtain steering angles, speeds, and accelerations.

[0175] In yet another embodiment, a collision may occur during a lane change of an adjacent vehicle.

[0176] In yet another embodiment, the host vehicle is configured to establish communication with a neighboring vehicle and provide boundaries for a collision zone to merge into a lane change when the neighboring vehicle is moving.

[0177] In yet another embodiment, the host vehicle establishes communication with a neighboring vehicle and requests an adjustment of the motion trajectory while merging into a lane change. If the vehicle's control system predicts a collision or detects an obstacle or environmental change that requires an adjustment of the motion trajectory, it may take various actions depending on the situation. For example, the vehicle's control system may adjust the steering angle of the wheels to change the direction of the vehicle and avoid the collision / obstacle. The vehicle's control system may apply brakes to slow or stop the vehicle to avoid an obstacle. In some cases, the vehicle's control system may accelerate the vehicle to move it out of the path of damage. The vehicle's control system may plan a new motion trajectory based on the new information it has received and execute the trajectory.

[0178] In some embodiments, the vehicle can also use machine learning and artificial intelligence algorithms to improve its trajectory planning and adjust its motion trajectory in real time based on past experience and predictions of future environments. The control system can continuously optimize the trajectory to ensure safe and efficient movement of the vehicle.

[0179] In yet another embodiment, the host vehicle establishes communication with a neighboring vehicle and provides the boundaries of the impact zone of the neighboring vehicle.

[0180] In yet another embodiment, the lane vanishing point is estimated by performing lane width detection.

[0181] In yet another embodiment, a control command is generated by determining a collision avoidance maneuver for the host vehicle to avoid the impact zone; and the collision avoidance maneuver is autonomously performed by the host vehicle. Autonomous vehicles use various sensors and technologies, including lidar, cameras, and advanced algorithms, to detect potential collisions and take appropriate actions to avoid them.

[0182] In yet another embodiment, the system is configured to determine a time period for a neighboring vehicle to reach a lane vanishing point.

[0183] In yet another embodiment, the system is configured to transmit a second signal for changing driving maneuvers to avoid a collision.

[0184] Reference Figure 3A , which shows a schematic configuration of a lane reduction determination system according to one or more embodiments. Figure 3A As shown, the system 300 includes a computing platform 302 configured to generally manage the device, a sensor 316, and an actuator 318. The computing platform 302 includes a processor 304, a memory 312, a control module 310, a communication module 308, a computer vision module 306, and a database. The processor analyzes data obtained from multiple sensors 316, a database 314, a map database 320, and the communication module 308, and instructs the actuator 318 to physically perform actions such as changing gears, applying brakes, and steering.

[0185] Sensor 316 is 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 316 can be installed to measure the characteristics of the vehicle's exterior surroundings. Additionally or alternatively, one or more of the sensors 316 can be installed in the vehicle's compartment or in the vehicle's body (e.g., engine compartment, wheel well, etc.) to measure the characteristics of the vehicle and / or the interior of the vehicle. For example, sensor 316 includes an accelerometer, an odometer, a tachometer, a pitch and yaw sensor, a wheel speed sensor, a microphone, a tire pressure sensor, a biometric sensor, an ultrasonic sensor, an infrared sensor, a light detection and ranging (Lidar), a radio detection and ranging system (radar), a global positioning system (GPS), a camera and / or any other suitable type of sensor. In the example shown, sensor 316 includes a distance detection sensor configured to monitor an object (a plurality of objects) positioned in the surrounding area of ​​the vehicle.

[0186] Reference Figure 3B, which shows various types of sensors attached to an autonomous vehicle according to one or more embodiments. The sensors include an infrared camera 316a, a stereo camera 316b, a wide-angle camera 316c, an ultrasonic sensor 316d, a radar sensor 316e, a lidar sensor 316f, a break sensor system 316g, a vibration sensor 316h, a speed sensor 316i, a GNSS sensor 316j, a microphone 316k, a sensor controller 316l, a network interface 316m, a wireless antenna 316n, and an IMU sensor (multiple IMU sensors) 316o. Autonomous vehicles typically use various sensors to monitor the surrounding environment and detect neighboring vehicles, including cameras, lidar (light detection and ranging), radar, and ultrasonic sensors.

[0187] To monitor the speed, acceleration, and braking of neighboring vehicles, the most commonly used sensors are one or more of an infrared camera 316a, a wide-angle camera 316c, an ultrasonic sensor 316d, a radar sensor 316e, a lidar sensor 316f, a microphone 316k, and a wireless antenna 316n. Radar can detect the distance and relative speed of neighboring vehicles by emitting radio waves and analyzing the signals that bounce back. Lidar uses laser beams to measure the distance and speed of surrounding objects (including vehicles). By combining data from these sensors, an autonomous vehicle can create a detailed 3D map of its environment and track the movement of neighboring vehicles, allowing it to anticipate their movements and adjust its own speed and trajectory accordingly.

[0188] The extension of the embodiment can provide software that simulates the sensor information and the reaction of the environment to the actuator. The actuator can be commanded or controlled to actuate. Based on the decision made by the computing platform 302, the actuator 318 of the vehicle is activated to control its movement. The various types of actuators that can be used in the autonomous vehicle are electric motors, linear actuators, solenoids, piezoelectric actuators, electrohydraulic actuators, pneumatic actuators and the like. The electric motor is used to provide the necessary torque and speed to control the movement of the wheels of the vehicle. The linear actuator is used to control the position and movement of various parts of the vehicle (such as throttles, brakes and steering systems). The solenoid is used to control the flow of fluids, such as hydraulic fluids, in the transmission and braking systems of the vehicle. Piezoelectric actuators are used to produce small, precise movements, such as those required for the vehicle suspension system. Electrohydraulic actuators are used to provide precise control of hydraulic systems, such as those used for braking and steering. Pneumatic actuators are used to control various systems, such as air pressure in the suspension system of the vehicle.

[0189] Reference Figure 3C, which shows various types of actuators attached to an autonomous vehicle according to one or more embodiments. The actuators include a steering actuator 318a, a brake actuator 318b, and an accelerator actuator 318c. The steering actuator 318a may include a first steering actuator control unit operating on a first on-board network for controlling the first steering actuator. The actuator system steering actuator 318a further includes a second steering actuator operating on a second on-board network of the vehicle, the second on-board network being designed to be redundant with the first on-board network; and a brake actuator 318b operating on the second on-board network of the vehicle, having a brake actuator and a brake actuator control unit for controlling the brake actuator. Here, the brake actuator device 318b further includes a second steering actuator control unit operating on the second on-board network of the vehicle for controlling the second steering actuator. Due to the configuration of the second steering actuator inside the brake control device, the number of devices to be installed can be reduced. In this way, the assembly cost is also reduced. In addition, by installing such a brake actuator device, it is possible to prepare a conventional vehicle for automated driving relatively easily. In a specific embodiment of the invention, it is assumed that the brake actuator device comprises a housing which accommodates a brake actuator and a brake actuator control unit assigned to the brake actuator. The steering actuator control unit is located inside the housing. By installing the steering actuator control unit in the housing of the brake actuator device, the assembly costs when upgrading the vehicle can be significantly reduced. By using a common housing for the brake actuator device and the steering actuator control unit, the space requirement inside the vehicle is also reduced.

[0190] The memory 312 may include a read-only memory (ROM), a random access memory (RAM), etc. The computing platform 302 loads the program stored in the ROM into the RAM, and implements various functions by executing the program loaded into the RAM by the CPU. The computing platform 302 may be composed of a control module 310.

[0191] The database 314 stores programs to be executed by the processor, data to be used for processing by the processor, and the like.

[0192] The control module 310 may include multiple controllers. A first controller may be used for autonomous driving functions, a second controller may be used for functional safety functions, a third controller including the computer vision module 306 may be used for artificial intelligence functions (e.g., computer vision and motion prediction of neighboring vehicles), a fourth controller may be used for infotainment functions, a fifth controller may be used for redundancy in emergency situations, and / or other controllers. In some examples, a single controller may handle two or more of the above functions, two or more controllers may handle a single function, and / or any combination thereof.

[0193] The computer vision module 306 includes a plurality of algorithms for lane reduction detection, adjacent vehicle detection, license plate detection, speed detection, distance detection, lane width detection, and turn signal detection. The map database 320 is a database storing map information. The map database 320 is formed in a storage device such as a hard disk drive (HDD) provided in the vehicle V. The map information includes position information about the road, information about the shape of the road (e.g., such as the type of curved and straight sections, the curvature of the curved road, and similar information), position information about intersections and branch points, speed limits set for the road, position information about fixed obstacles such as poles, and the like. The map database 320 may be provided in a computer in a facility such as an information processing center that is communicable with the vehicle V. In addition, the map information includes information about the dividing line of the lane. The information about the dividing line of the lane is information that can specify the position and shape of the dividing line. Note that the dividing line of the lane is a line (e.g., a white line) that divides the lane from an area outside the lane. The license plate database 322 is a database in which information related to the license plate is stored. It typically stores information related to a vehicle associated with a specific license plate. This can include: a) registration information, including the name and address of the registered owner of the vehicle, and the make, model, year, and VIN (vehicle identification number) of the vehicle; b) vehicle history, including information about the history of the vehicle, such as any accidents, thefts, or other incidents that may have been reported; and c) outstanding parking tickets or violations, including information about any outstanding parking tickets, traffic violations, or other violations associated with the vehicle.

[0194] In some embodiments, a GPS receiving unit and a map database (map information storage unit) are connected to the computing platform 302. The GPS receiving unit measures the position of the vehicle V (e.g., the latitude and longitude of the vehicle V) by receiving signals from three or more GPS satellites. The GPS receiving unit transmits the measured position information of the vehicle V to the computing platform 302.

[0195] One embodiment relates to a vehicle including a system according to one or more embodiments. The vehicle includes a system, further including: a processor, a computer vision module, a communication module, and a control system, wherein the system is configured to: detect lane reduction in a lane that has recently disappeared by the processor; detect a neighboring vehicle and a driving action of the neighboring vehicle in the lane that has recently disappeared by the computer vision module; detect a license plate of the neighboring vehicle by the computer vision module; receive a first signal for a driving mode from the neighboring vehicle by the communication module; detect a lane vanishing point in the lane that has recently disappeared by the computer vision module; determine a time interval for the neighboring vehicle to reach the lane vanishing point by the processor; detect the driving action of the neighboring vehicle via the processor; perform a risk assessment of a collision based on the driving action by the processor; and transmit a control command to an actuator system of the host vehicle via the communication module based on the risk assessment.

[0196] Reference Figure 4A , which shows the response of a vehicle including the system in a lane reduction scenario in accordance with one or more embodiments. Vehicle (V) will detect if it is approaching a lane reduction ("Lane 1" 1 is the lane that will disappear, and "Lane 2" 2 is the lane that will continue). Vehicle V will detect if there is a neighboring vehicle (O) in Lane 1, and will detect if O is traveling faster or slower than V. V will detect at what point P the lane begins to disappear, and will determine how long it will take to reach point P. V will do the same calculations for O. V will compare O's time to V's, and based on that comparison and other factors, such as speed limits / other vehicles / lane availability, V will or will not modify its travel to prevent an accident.

[0197] Reference Figure 4B , the vehicle V includes a lane reduction detection system 402 that uses a plurality of sensors 404a and 404b to detect lane reduction and neighboring vehicles. By predicting / detecting the driving maneuvers of neighboring vehicles, the lane reduction system takes appropriate decisions to avoid the risk of collision and transmits control commands to the vehicle's actuator system 406.

[0198] Reference Figure 5A , which shows a flow chart of a method for detecting lane reduction situations and making appropriate adjustments to avoid collisions according to one embodiment. The method 500 includes the following steps:

[0199] Step 502 of method 500 includes detecting a lane reduction in a recently disappeared lane. Detection of a lane reduction includes detection of a lane that disappears or becomes narrower or blocked.

[0200] Step 504 of method 500 includes detecting neighboring vehicles in the most recently disappeared lane and the driving maneuvers of the neighboring vehicles.

[0201] Step 506 of method 500 includes detecting license plates of neighboring vehicles.

[0202] Step 508 of method 500 includes receiving a first signal for a driving mode from a neighboring vehicle.

[0203] Step 510 of method 500 includes detecting a lane vanishing point in a most recently vanished lane.

[0204] Step 512 of method 500 includes determining a time interval for a neighboring vehicle to reach a lane vanishing point.

[0205] Step 514 of method 500 includes performing a risk assessment of a collision based on the driving maneuver and the time interval.

[0206] Step 516 of method 500 includes transmitting control commands to actuator systems of the vehicle based on the risk assessment.

[0207] Reference Figure 5B , which shows a traffic sign for a lane reduction situation according to one or more embodiments. In some embodiments, the vehicle via a computer vision module can identify an upcoming lane reduction situation and activate a lane reduction protocol in a computing unit.

[0208] One embodiment relates to a method for detecting and preventing a possible accident in a lane reduction. The method includes: detecting a lane reduction in a lane that has recently disappeared; detecting a neighboring vehicle in the lane that has recently disappeared; detecting a license plate of the neighboring vehicle; receiving a first signal for a driving mode from the neighboring vehicle; detecting a lane vanishing point in the lane that has recently disappeared; determining a time interval for the neighboring vehicle to reach the lane vanishing point; detecting a driving action of the neighboring vehicle; performing a risk assessment of a collision based on the driving action and the time interval; and transmitting a control command to an actuator system of the vehicle based on the risk assessment.

[0209] In one embodiment, lane reduction is detected using at least one of GPS information, traffic signs, and lane markings.

[0210] In another embodiment, the method further includes detecting a cone distance from a lane marking.

[0211] In yet another embodiment, a camera of the host vehicle is used to detect neighboring vehicles.

[0212] In yet another embodiment, a camera is used to detect the license plate.

[0213] In yet another embodiment, the camera is connected to the host vehicle's computer vision system.

[0214] In yet another embodiment, the method further includes determining current locations of neighboring vehicles and the host vehicle.

[0215] In yet another embodiment, the current position of the neighboring vehicle is detected by a radar sensor of a computer vision system of the host vehicle.

[0216] In yet another embodiment, a global navigation satellite system (GPS) is used to detect the current position of the host vehicle and the current positions of neighboring vehicles.

[0217] In yet another embodiment, the method further includes predicting a lane change of a neighboring vehicle.

[0218] In yet another embodiment, lane changes are predicted by capturing at least one of steering angle, steering rate, speed, and acceleration.

[0219] In yet another embodiment, the method further includes detecting a lane change of a neighboring vehicle.

[0220] In yet another embodiment, a computer vision system is used to detect lane changes through turn signals of neighboring vehicles.

[0221] In yet another embodiment, lane changes by neighboring vehicles are detected by tracking the position and movement of the neighboring vehicles over time.

[0222] In yet another embodiment, a lane change of a neighboring vehicle is detected via a sensor of the host vehicle by detecting a speed and direction of the neighboring vehicle's movement, wherein the sensor is at least one of a radar and a lidar.

[0223] In yet another embodiment, the method further includes detecting a lane width of a most recently disappeared lane.

[0224] Reference Fig. 6A , when vehicle V approaches lane reduction, vehicle V is traveling to its destination. This can be detected using GPS information and / or using traffic signs and / or lane markings. V will detect whether there is a neighboring vehicle (O) in lane 1 (the lane that will disappear). If there is no neighboring vehicle O in lane 1, the process will end.

[0225] If V detects a neighboring vehicle O, it will detect whether O is autonomous or non-autonomous by performing a license plate detection and checking in the database whether the license plate number is assigned to an autonomous or non-autonomous vehicle.

[0226] If O is autonomous, the AV will query via V2V whether O is driving in autonomous mode or in manual mode. If O is driving in autonomous mode, the process will end; if O is autonomous but driving in manual mode, the process will continue. If O is non-autonomous, the process will continue.

[0227] Reference Figure 6B, which shows lane width detection performed by a vehicle according to one or more embodiments. The vehicle V will detect the position where the lane begins to disappear by performing lane width detection. The computing unit is configured via a computer vision module to detect the nominal width n and the widths shorter than the nominal width n (n-x1, n-x1, ..., nx n ).

[0228] Reference Figure 6C , which shows the time period during which a vehicle including a system for detecting / tracking neighboring vehicles can reach a lane vanishing point, according to one or more embodiments. Vehicle V will determine how long it will take for V to reach point P where the lane begins to vanish (5 seconds in the example). V will detect if vehicle O turns on the "turn signal" and begins braking. If so, the process will end, meaning a collision between O and V is unlikely.

[0229] V will detect O's speed in real time. V will compare its own speed to O's speed to see who is going faster, to detect if O's driver has any intention of overtaking V. If O is moving faster, it may be dangerous to V, depending on the speed and the reduced distance to the lane. If O is moving at a similar speed to V, it may be dangerous to V, depending on where O is relative to V. If O is moving slower than V, it may be safe for V, depending on where O is relative to V. Vehicle V will perform hazard assessments based on different scenarios (more scenarios can be added, following similar logic).

[0230] In one example, there may be a scenario 1 where a neighboring vehicle has dropped too far from the lane. Fig.6D Scenario 1 is shown in FIG. Neighboring vehicle O will reach the lane reduction within 15 seconds, while V will reach the lane reduction within 5 seconds. In this case, vehicle V will take no action related to lane reduction, and the process will end because an accident is unlikely to occur.

[0231] In one example, there may be a scenario where a neighboring vehicle is too far removed from the lane. However, the neighboring vehicle and the host vehicle are moving side by side at the same speed. Fig. 6E As shown, neighboring vehicle O is moving at a similar speed to vehicle V, and neighboring vehicles O and V are adjacent to each other. In this scenario, vehicle V will detect if there is a neighboring vehicle behind V. It will also compare its speed to the speed limit. If vehicle V can speed up and give vehicle O more room in lane 2, vehicle V will do so.

[0232] Fig. 6FScenario 2 is shown in FIG. Neighboring vehicle O is moving at a similar speed to vehicle V and neighboring vehicles O and V are adjacent to each other. In this case, vehicle V will detect whether there is a neighboring vehicle O2 behind vehicle V. V will also compare its speed to the speed limit. If there is scope to increase speed, V can speed up and give O more room in lane 1 for lane change.

[0233] In one example, there may be scenario 3 where the neighboring vehicle is too far removed from the lane and the vehicle is moving at the speed limit. Figure 6G Scenario 3 is shown in FIG. Neighboring vehicle O is moving at a similar speed to vehicle V and neighboring vehicle O and vehicle V are adjacent to each other. V cannot accelerate beyond the speed limit because there is no scope to increase speed. In this case, vehicle V will detect whether there is a neighboring vehicle O2 behind vehicle V. If there is no vehicle behind V, V can brake and at some point, O will have enough time to merge into lane 2.

[0234] In one example, there may be a scenario 4 where there is a lane 3 available for the vehicle to make a lane change. Figure 6H Scenario 4 is shown in FIG. The neighboring vehicle O is moving at a similar speed to the vehicle V and the neighboring vehicle O and the vehicle V are adjacent to each other. Since there is a lane 3 available for the vehicle to make a lane change, the vehicle V will perform a lane change.

[0235] In one example, there may be scenario 5 where neighboring vehicle O is traveling faster than vehicle V and vehicle O is behind vehicle V. Fig.6I Scenario 5 is shown in FIG, where neighboring vehicle O is moving at 90 km / h and vehicle V is moving at 50 km / h. In this case, vehicle V will infer that O wants to overtake V. Vehicle V will determine the time required for neighboring vehicle O to reach the lane reduction. V will estimate how long it will take V to reach the lane reduction.

[0236] like Figure 6J As shown, the time period T1 (4 seconds) for O to reach point P is less than the time period T2 (10 seconds) for V to reach point P. In this case, if the time of O is significantly shorter than the time of V, the vehicle V will not change its travel. In another case, if the time of O is similar to the time of V, the vehicle V may slow down. In yet another case, if the time of O is significantly longer than the time of V, the vehicle V will not change its travel.

[0237] In one embodiment, the system may consider the type of neighboring vehicle based on the size of the neighboring vehicle. The type of neighboring vehicle may be one of a subcompact vehicle, a compact vehicle, a mid-size vehicle, a full-size vehicle, a sport utility vehicle (SUV), a truck, and a bus. Fig. 7A and Figure 7B, vehicle V is traveling towards its destination when vehicle V approaches a lane reduction. This can be detected using GPS information and / or using traffic signs and / or lane markings. V will detect whether there is a neighboring vehicle (O) in lane 1 (the lane that will disappear). If there is no neighboring vehicle O in lane 1, the process will end. If V detects a neighboring vehicle O, it will detect whether O is autonomous or non-autonomous by performing a license plate detection and checking in the database whether the license plate number is assigned to an autonomous or non-autonomous vehicle. The system will also obtain the type or model of the vehicle from the license plate database and therefore know the length of the vehicle, which will be used to determine the time required for vehicle O to merge.

[0238] In one embodiment, the system may use computer vision to detect the type of neighboring vehicles.

[0239] If O is autonomous, the AV will query via V2V whether O is traveling in autonomous mode or in manual mode. If O is traveling in autonomous mode, the process will end; if O is autonomous but traveling in manual mode, the process will continue. If O is non-autonomous, the process will continue.

[0240] Reference Fig. 7A , the adjacent vehicle O is a compact car (van. Figure 7B , the neighboring vehicle is a truck (semi-truck). While considering scenarios 1 to 5, the vehicle will also consider the type of vehicle for hazard assessment and decision making.

[0241] In one embodiment, the system can detect a temporarily closed lane. The computer vision module can detect one or more of a road cone, a road barrier, a New Jersey style guardrail, a traffic barrel, a portable traffic signal, a temporary pavement marking, and a flagger, and detect the temporarily closed lane based on the presence of the one or more markings.

[0242] Reference Fig. 8A , a vehicle V including the system, which is traveling toward its destination, approaches a temporarily closed lane having a traffic barrel as a traffic diversion sign. Figure 8B , a vehicle V including the system, traveling toward its destination, approaches a temporarily closed lane with a cone as a traffic diversion marker. This can be detected using multiple cameras and a computer vision module. In this case, instead of detecting a lane width change, the vehicle V detects the distance from the traffic diversion marker (e.g., cone) to the lane marker and thus detects the approach of a temporary lane closure, such as Figure 8CAs shown. V will detect if there is a neighboring vehicle (O) in lane 1 (the temporarily closed lane). If there is no neighboring vehicle O in lane 1, the process will end. If V detects a neighboring vehicle O, it will detect whether O is autonomous or non-autonomous by performing a license plate detection and checking in the database whether the license plate number is assigned to an autonomous or non-autonomous vehicle. The system can also obtain the type or model of the vehicle from the license plate database, and therefore it knows the length of the vehicle, which is used to determine the time required for vehicle O to merge.

[0243] If O is autonomous, the AV will query via V2V whether O is driving in autonomous mode or in manual mode. If O is driving in autonomous mode, the process will end; if O is autonomous but driving in manual mode, the process will continue. If O is non-autonomous, the process will continue. The vehicle will consider scenarios 1 to 5 for hazard assessment and decision making.

[0244] Fig. 9A A block diagram of a system for detecting a possible lane change due to lane reduction and prediction of possible actions of neighboring vehicles according to one embodiment is shown. According to one embodiment, it is a system 940, including: a processor 942, a computer vision module 944, and a control module 946; wherein the processor is operable to: detect lane reduction in a recently disappeared lane 902 by the processor; detect neighboring vehicles and driving actions of neighboring vehicles in the recently disappeared lane 904 by the computer vision module; detect license plates of neighboring vehicles 906 by the computer vision module; receive a first signal for driving mode from neighboring vehicles 908 by the communication module; detect a lane vanishing point in the recently disappeared lane 910; determine a time interval for neighboring vehicles to reach the lane vanishing point 912 by the processor; perform a risk assessment of collision based on the driving action 914 by the processor; and transmit control commands to an actuator system of a host vehicle via the communication module based on the risk assessment 916.

[0245] According to one embodiment, the host vehicle is an autonomous vehicle.

[0246] According to one embodiment, the neighboring vehicle includes one of a semi-trailer, a bus, an SUV, a tractor, a truck, a recreational vehicle, a trailer, and heavy construction equipment.

[0247] According to one embodiment, the system is further operable to detect neighboring vehicles and neighboring lanes. According to one embodiment, neighboring vehicles and neighboring lanes are detected via a computer vision module that includes radar, lidar, camera, GPS, and an artificial intelligence engine including a machine learning algorithm.

[0248] Fig. 9BA block diagram of a non-transitory computer-readable medium 974 is shown that performs operations to detect a possible lane change due to lane reduction, prediction of possible actions of neighboring vehicles, and collisions according to one embodiment. The non-transitory computer-readable medium 974 has instructions stored thereon that are executable by a computer system 971 including a processor 972 to perform operations via a software application 976, including: detecting a lane reduction in a recently disappeared lane 922; detecting a neighboring vehicle in a recently disappeared lane and a driving action of the neighboring vehicle 924; detecting a license plate of a neighboring vehicle 926; receiving a first signal for a driving mode from a neighboring vehicle 928; detecting a lane vanishing point in a recently disappeared lane 930; determining a time interval for a neighboring vehicle to reach the lane vanishing point 932; performing a risk assessment of a collision based on the driving action and the time interval 934; and transmitting a control command to an actuator system of a vehicle based on the risk assessment 936.

[0249] Fig. 10A A block diagram of a system according to one embodiment is shown, which detects a lane reduction situation and transmits appropriate messages to neighboring vehicles to avoid collisions. According to one embodiment, it is a system 1040, including: a processor 1042, a computer vision module 1044, and a control module 1046; wherein the processor is operable to: detect lane reduction in a recently disappeared lane 1002 by the processor; detect neighboring vehicles and driving actions of neighboring vehicles in the recently disappeared lane 1004 by the computer vision module; detect license plates of neighboring vehicles 1006 by the computer vision module; receive a first signal for driving mode from neighboring vehicles 1008 by the communication module; detect a lane vanishing point in the recently disappeared lane 1010; determine a time interval for neighboring vehicles to reach the lane vanishing point 1012 by the processor; predict the driving action of neighboring vehicles 1014 by the processor; perform a risk assessment of collision based on the driving action 1016 by the processor; and transmit a risk signal to neighboring vehicles 1018 via the communication module based on the risk assessment.

[0250] According to one embodiment, the host vehicle is an autonomous vehicle.

[0251] According to one embodiment, the neighboring vehicle includes one of a semi-trailer, a bus, an SUV, a tractor, a truck, a recreational vehicle, a trailer, and heavy construction equipment.

[0252] According to one embodiment, the system is further operable to detect neighboring vehicles and adjacent lanes. According to one embodiment, the neighboring vehicles and adjacent lanes are detected via a computer vision module, which includes radar, lidar, camera, GPS, and an artificial intelligence engine including machine learning algorithms.

[0253] One embodiment relates to a method for detecting and preventing a possible accident in a lane reduction by transmitting a message to a neighboring vehicle. The method includes: detecting a lane reduction; detecting a neighboring vehicle in a lane that has recently disappeared; performing a license plate detection and checking in a database whether the license plate number is assigned to a non-autonomous vehicle; performing a lane width detection to detect a point in the lane that has recently disappeared where the lane disappears; determining a time interval for a neighboring vehicle to reach the lane disappearance point; predicting a driving action of the neighboring vehicle; performing a hazard assessment based on the driving action and the time interval; transmitting a hazard signal to the neighboring vehicle based on the hazard assessment; and transmitting a control command to an actuator system of the vehicle based on the hazard assessment, wherein the neighboring vehicle is traveling in a manual mode.

[0254] Reference Fig. 10B , which shows a flow chart of a method for detecting a lane reduction situation and then transmitting appropriate messages to neighboring vehicles to avoid collisions according to one embodiment. The method 1000 includes the following steps:

[0255] Step 1022 of method 1000 includes detecting a lane reduction in a recently disappeared lane. Detection of a lane reduction includes detection of a lane that disappears or becomes narrower or blocked.

[0256] Step 1024 of method 1000 includes detecting neighboring vehicles in the most recently disappeared lane.

[0257] Step 1026 of method 1000 includes detecting license plates of neighboring vehicles.

[0258] Step 1028 of method 1000 includes receiving a first signal for a driving mode from a neighboring vehicle.

[0259] Step 1030 of method 1000 includes detecting a lane vanishing point in a most recently vanished lane.

[0260] Step 1032 of method 1000 includes determining a time interval for a neighboring vehicle to reach a lane vanishing point.

[0261] Step 1034 of method 1000 includes predicting the driving maneuvers of neighboring vehicles.

[0262] Step 1036 of method 1000 includes performing a risk assessment of a collision based on the driving maneuver and the time interval.

[0263] Step 1038 of method 1000 includes transmitting a hazard signal to a neighboring vehicle based on the hazard assessment.

[0264] Fig. 10CA block diagram of a non-transitory computer readable medium 1074 is shown that performs operations to detect a lane reduction situation and transmit appropriate messages to neighboring vehicles to avoid collisions according to one embodiment. The non-transitory computer readable medium 107 has instructions stored thereon that are executable by a computer system 1071 including a processor 1072 to perform operations via a software application 1076, including: detecting a lane reduction in a recently disappeared lane 1022; detecting a neighboring vehicle in a recently disappeared lane 1024; detecting a license plate of a neighboring vehicle 1026; receiving a first signal for a driving mode from a neighboring vehicle 1028; detecting a lane vanishing point in a recently disappeared lane 1030; determining a time interval for a neighboring vehicle to reach the lane vanishing point 1032; predicting a driving action of a neighboring vehicle 1034; performing a risk assessment of a collision based on the driving action and the time interval 1036; and transmitting a risk signal to a neighboring vehicle based on the risk assessment 1038.

[0265] Fig.11A An example message illustrating a dedicated short range communications (DSRC) message format. Fig.11A Shown: (i) Basic Safety Message (BSM) format: BSM is the basic message type in DSRC, used to transmit information about the status, location and movement of the vehicle to other vehicles in the area. In the example message, the header contains the message ID and protocol version, while the data segment contains specific information about the vehicle, such as its ID, speed, direction of travel and position. The timestamp indicates when the message was generated and can be used by other vehicles to determine the current status of the passing vehicle; (ii) Signal Phase and Timing (SPaT) format: SPaT messages provide information about the current state of traffic signals at intersections, including which phase is currently active and when the signal will change. This information can be used by vehicles to optimize their driving behavior and avoid collisions. In the example message, the header contains the message ID and protocol version, while the data segment includes the ID of the intersection, the state of the movement phase, and the remaining time to the next phase and subsequent phases; (iii) Roadside Warning (RSA) format: RSA messages are used to notify vehicles about dangers or other important information on the road ahead, such as construction zones, accidents or detours. In the example message, the header contains the message ID and protocol version, while the data section includes the type of alarm (construction), the distance from the alarm, the direction of the alarm, and a text message providing additional details about the alarm.

[0266] According to one embodiment, there are other possible elements that may be included in each message format, for example, the BSM format may also include information about: acceleration, size and weight, vehicle safety extensions including brakes, lights, wipers, transmission and powertrain, vehicle dimensions, and vehicle identification information including identification number and vehicle type.

[0267] In addition to the three examples provided above, there are several other message formats available in the DSRC protocol. Some of these message formats include: MAP (Message Assistance for Route Provisioning) format: This message provides a map of the road network, including information about road layout, speed limits, and other relevant details; CAM (Collaborative Awareness Message) format: Similar to the BSM message format, this message provides information about the vehicle's current state, such as speed, location, and direction of travel. CAM messages are typically sent at a higher frequency than BSM messages to support more accurate situational awareness; DENM (Decentralized Environment Notification Message) format: This message provides information about environmental conditions that may affect driving, such as weather conditions, visibility, and road conditions; EV (Emergency Vehicle) format: This message provides information about emergency vehicles, such as their location and direction of travel, to help other drivers safely navigate around them; TCI (Traffic Control Information) format: This message provides information about traffic conditions, such as congestion, accidents, and construction zones, to help drivers make informed decisions about their routes.

[0268] In summary, the various message formats available in the DSRC protocol support a wide range of use cases from basic vehicle-to-vehicle communications to more complex applications such as real-time traffic management and emergency response.

[0269] Fig. 11B Example messages from a host vehicle to neighboring vehicles are shown. They may include elements related to lane reduction alerts and request messages, where they may request a speed reduction so that coordinated action can be performed. Such messages may be broadcast messages, or messages sent to individual neighboring vehicles or a selected group of vehicles that need to perform the request.

[0270] Fig. 11C Example messages from the host vehicle to neighboring vehicles are shown. In one embodiment, they may include information about lane reduction and possible collisions, as well as request messages to adjust them to target values ​​to avoid collisions.

[0271] Fig.11D Example messages from the host vehicle to neighboring vehicles are shown. In one embodiment, they may include information about lane reduction, possible collision, availability of a third available lane, and a request message for a lane change at a specific time to avoid a collision.

[0272] Fig.11E An example warning message is shown displayed in a host vehicle 1101 to a neighboring vehicle 1102. In one embodiment, it may include a warning message and a graphical representation to show lane reduction, the starting point P of lane disappearance, a possible collision C, a neighboring vehicle 1103 in the same lane as the host vehicle 1101, and a possible action A to avoid the collision.

[0273] Fig. 12A The structure of a neural network / machine learning model with a feedback loop is shown. The artificial neural network (ANNs) 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 above a specified threshold, the node is activated and the data is transmitted to the next layer of the network. Otherwise, no data is passed to the next layer of the network. A machine learning model or ANN model can be trained on a set of data to take 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 with different data sets. Supervised learning includes the logic of using at least one of a decision tree, logistic regression, and a support vector machine. Unsupervised learning includes the logic of using at least one of a k-means clustering, a hierarchical clustering, a hidden Markov model, and a priori algorithms. The output layer can predict or detect collisions and the severity of collisions based on input data, such as neighboring vehicle parameters, host vehicle and neighboring vehicle positioning and direction.

[0274] In one embodiment, ANN can be a deep neural network (DNN), which is a multi-layer tandem neural network, including artificial neural network (ANN), convolutional neural network (CNN) and recurrent neural network (RNN), which can identify features from input, perform expert review and perform actions requiring prediction, creative thinking and analysis. In one embodiment, ANN can be a recurrent neural network (RNN), which is an artificial neural network (ANN) that uses sequential data or time series data. Deep learning algorithms are generally used for sequential or time problems, such as language translation, natural language processing (NLP), speech recognition and image recognition. Similar to feedforward and convolutional neural networks (CNN), recurrent neural networks use training data to learn. Their difference lies in their "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 in the hidden layer (multiple hidden layers) will be adjusted to better fit the expected output. This will allow the model to provide results with much fewer errors.

[0275] Neural networks feature feedback loops to dynamically adjust the system output as it learns from new data. In machine learning, backpropagation and feedback loops are used to train AI models and continuously improve them as they are used. As the model receives more input data, the model has more opportunities to learn from the data. The feedback loop or backpropagation algorithm identifies inconsistencies and feeds the corrected information back into the model as input.

[0276] Even if an AI / ML model is well trained, utilizing a large set of labeled data and concepts, after a while the performance of the model may degrade when new unlabeled inputs are added due to many reasons including but not limited to concept drift, recall precision degradation due to drift away from true positives, and data drift over time. Feedback loops to the model keep AI results accurate and ensure that the model maintains its performance and improves even as new unlabeled data is assimilated. A feedback loop refers to the process of reusing the predicted output of an AI model to train a new version of that model.

[0277] Initially, when the AI / ML model is trained, several labeled samples, both positive and negative, of concepts (e.g., vehicles, tire orientation) are used, which are intended for the model to learn. The model is then tested using unlabeled data. By using, for example, deep learning and neural networks, the model can then predict whether the desired concept / concepts (e.g., lane vanishing point, probability of collision when lane markings are detected for lane vanishing, etc.) are in the unlabeled image. Each image is given a probability score, where a higher score indicates a higher confidence level in the model's prediction. In the case where the model gives an image a high probability score, it is automatically labeled with the predicted concept. However, in the case where the model returns a low probability score, the input can be sent to a controller (which can be a human arbitrator), which verifies and corrects the result if necessary. Human arbitrators can only be used in exceptional cases. The feedback loop dynamically feeds the automatically labeled or controller-verified labeled data back to the model and uses it as training data so that the system can improve its predictions in real time and dynamically.

[0278] Fig. 12B The structure of a neural network / machine learning model with reinforcement learning is shown. The network receives feedback from an authorized networked environment. Although this system is similar to supervised learning, the feedback obtained in this case is evaluative rather than instructive, meaning there is no teacher as in supervised learning. After receiving the feedback, the network performs adjustments to the weights to obtain better predictions in the future. Machine learning techniques, such as deep learning, allow the model to take labeled training data and learn to recognize those concepts in subsequent data and images. The model can be fed new data for testing, so training is enhanced by feeding the model with data that has already been predicted. If the machine learning model has a feedback loop, the rewards for each true example output by the system are used to further reinforce the learning. Feedback loops ensure that AI results do not stagnate. By incorporating feedback loops, the model outputs remain dynamic and improve with use / time.

[0279] Fig. 12CAn example block diagram for detecting impact zones using a machine learning model is shown. The machine learning model 1202 can take as input any data associated with the host vehicle and neighboring vehicles, and learn to identify features of predicted lane changes / speed changes / turning paths / trajectories within the data. The training data samples may include, for example, neighboring vehicle information 1204, such as size, dimensions, vehicle type, vehicle model, weight, length, etc. In one embodiment, it relates to a system and method for real-time identification of neighboring vehicles and their features using onboard cameras and / or other sensors. The vehicle identification and feature information is transmitted to the cloud, where the vehicle identification is coupled with available turning radius and trajectory predictions in the case of previously identified vehicles of similar categories and buildings with similar characteristics. Subsequently, this information is used to calculate / estimate lane changes, speed changes, and turning trajectories. The systems and methods of the present disclosure may also provide data analysis information that can be subsequently used to improve vehicle safety. When the host vehicle approaches a lane reduction, the host vehicle's sensors and cameras and facial recognition algorithms or the like can be used to detect vehicle features and estimate the motion profile of neighboring vehicles.

[0280] In one embodiment, the training data samples may also include current contextual information 1206 about 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, other obstacles, etc. The system may also obtain contextual information 1206 from a device associated with the vehicle. For example, via an application installed on the device, such as Map and positioning services, the system can know the vehicle details.

[0281] Real-time sensor data 1208 may include, for example, video, images, audio, infrared, temperature, 3D modeling, and any other suitable type of data that captures the current state of the host vehicle's surroundings. In one embodiment, real-time sensor data can be processed using one or more machine learning models 1202 that are trained and based on similar types of data to predict real-time features of neighboring vehicles. Real-time features may include, for example, the size, shape, dimensions, signal lights, etc. of neighboring vehicles. Current information about the target can be used to estimate the time to reach the lane vanishing point and predict neighboring vehicle actions. For example, currently detected sensor data and / or previously known information about neighboring vehicle categories and / or road obstacles can be used to predict collisions.

[0282] Sensor data may be received from, for example, but not limited to, global navigation satellite system sensor(s) (e.g., global positioning system sensor(s)), radar sensor(s), ultrasonic sensor(s), lidar sensor(s), inertial measurement unit (IMU) sensor(s) (e.g., accelerometer(s), gyroscope(s), magnetic compass(s), magnetometer(s), etc.), microphone(s), stereo camera(s), wide-angle camera(s) (e.g., fisheye camera), infrared camera(s), surround camera(s) (e.g., 360 degree camera), long-range and / or mid-range camera(s), speed sensor(s) (e.g., for measuring the speed of the vehicle), vibration sensor(s), steering sensor(s), brake sensor(s) (e.g., as part of a brake sensor system), and / or other sensor types.

[0283] Any of the aforementioned types of data (e.g., neighboring vehicle data 1204, contextual information 1206, sensor data 1208, or any other data) may be correlated with the collision zone and safety zone calculations, and such correlations may be automatically learned by the machine learning model 1202. In one embodiment, during training, the machine learning model 1202 may process training data samples (e.g., neighboring vehicles 1204 and / or contextual information 1206), and based on the current parameters of the machine learning model 1202, detections or predictions may be outputs 1210 of collision probabilities and / or safety zones and collision zones. The detection or prediction 1210 of collision probabilities and / or safety zones and collision zones may depend on training data 1212 having labels associated with the training data samples 1218. Predicting collision probabilities and / or safety zones and collision zones refers to predicting future events based on past and current data and most often through analysis, estimation of trends or data patterns. Prediction or predictive analysis employs probabilities based on data analysis and processing. Detection of collision probabilities and / or safety zones and collision zones refers to the onset of an event and the system detecting the event. Based on how the transition of the event occurs, the predicted event may or may not transition into a collision. In one embodiment, during training, the detected event at 1210 and the labeled training data 1212 may be compared at 1214. For example, the comparison 1214 may be based on a loss function that measures the difference between the detected collision event 1210 and the labeled training data 1212. Based on the corresponding output of the comparison or loss function at 1214, the training algorithm may update the parameters of the machine learning model 1202 with the goal of minimizing the difference or loss between subsequent predictions or detections of the event 1210 and the corresponding label 1212. By iterating the training in this manner, the machine learning model 1202 can "learn" from different training data samples and get better at detecting various collision events at 1210 that are similar to the collision event represented by the training label at 1212. In one embodiment, the machine learning model 1202 is trained using data specific to a type of target, where the model is used to detect collisions for that target to estimate safety zones and impact zones. In one embodiment, the machine learning model 1202 is trained using data that is common to the vehicle type and is used to detect collision probability and thereby determine safety and impact zones.

[0284] Using the training data, the machine learning model 1202 can be trained so that it recognizes features of the input data that represent or are associated with certain event types. For example, the trained machine learning model 1202 can identify data features that represent the possibility of an emergency situation as an actionable event. Through training, the machine learning model 1202 can learn to identify predictive and non-predictive features, and apply appropriate weights to each feature to optimize the prediction accuracy of the machine learning model 1202. In an embodiment where supervised learning is used and each training data sample 1218 has a label 1212, the training algorithm can iteratively process each training data sample 1218 (including neighboring vehicle data 1204, contextual information 1206, and / or sensor data 1208) and generate a prediction 1210 of a collision based on the current parameters of the model 1202. Based on the results of the comparison 1214, the training algorithm can adjust the parameters / configuration (e.g., weights) of the model 1202 accordingly to minimize the difference between the generated prediction 1210 of the collision event and the corresponding label 1212. Any suitable machine learning model and training algorithm may be used, including, for example, neural networks, decision trees, clustering algorithms, and any other suitable machine learning techniques. Once trained, the machine learning model 1202 may take input data associated with neighboring vehicles and output one or more predictions indicating a probability of collision based on the host vehicle's positioning, and may suggest a safe positioning for the host vehicle.

[0285] Fig.13 An example flow chart for detecting a collision with the current location of a host vehicle using a machine learning model is shown. The system can receive data associated with sensor output (multiple sensor outputs) from one or more sensors in the host vehicle, as shown at 1302. Any type of sensor can be used to collect data related to neighboring vehicles. The sensor output can be, for example, an image, video, audio, lidar measurement results, infrared measurement results, temperature measurement results, GPS data, or any other information measured or detected by a sensor. In one embodiment, the sensor output can be the result of one or more sensors capturing environmental information associated with the surrounding environment of the host vehicle. For example, the sensor output can be associated with passengers including a driver in the vehicle. In one embodiment, the data associated with the sensor output can include raw sensor output (e.g., image, video, audio, etc.) and / or data derived from processing sensor output. As an example, derived data from image, video or lidar data can include data representing recognized objects, motions, gestures, facial expressions, postures, eye positions, etc. The system can receive any data associated with sensor output from a sensor. In one embodiment, the system can process the received data and use a machine learning model to identify any interesting actionable events, which are trained using a training data set.

[0286] As shown in 1304, the system can extract features from the received data according to the machine learning model. The machine learning model can do this automatically based on what it learns during the training process. In one embodiment, appropriate weights learned during the training process can be applied to the features.

[0287] In step 1308, the machine learning model may generate a score representing the likelihood or confidence that the received data is associated with a particular event type, such as a collision due to a particular type of vehicle at an intersection, etc., based on the features of the received data.

[0288] As shown in 1310, the system can determine whether the score is high enough relative to a threshold or standard to warrant a specific action. If the score is not high enough, indicating a false positive, the system can return to step 1302 and continue to monitor subsequent input data. On the other hand, if the score is high enough, then at step 1312, the system can generate an appropriate alert and / or determine an appropriate action / response. In one embodiment, the system can transmit an alert to an appropriate recipient based on the type of event detected. For example, an alert is generated in the host vehicle and a message is sent to neighboring vehicles.

[0289] In one embodiment, the system may repeat, where appropriate, Fig.13 In one embodiment, steps 1302 to 1312 may be performed by the system, and any combination of those steps may be performed by any other computing system, such as a remote network or cloud network. In embodiments where 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 to allow event detection to occur locally. This may be desirable because the sensor data may be too large to be transmitted to a remote system in a timely manner.

[0290] In one embodiment, a system is provided wherein a facial expression recognition module utilizes a convolutional neural network (CNN) pre-training process; and / or wherein the machine learning (ML) algorithm includes a bounding box formation process around the face of an object; and / or wherein the bounding box formation utilizes a Viola-Jones detection algorithm; and / or wherein the facial recognition module utilizes a facial expression recognition (FER) algorithm. In one embodiment, one or more facial expressions currently obtained from a camera are compared to thresholds of pre-trained emergency classifications, and a determination is made as to when the current classification is an emergency. The use of a recurrent neural network architecture comes from its ability to use past temporal information to infer current inputs. Long term memory (LSTM) provides a computationally efficient way to train these networks. With this training mechanism, the model can predict when a collision is about to occur at a given host vehicle location given a real-time video input. According to one embodiment of the system, a bounding box algorithm is used in conjunction with a machine learning algorithm for object detection, wherein the machine learning algorithm is a convolutional neural network (CNN).

[0291] 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 the first data message for potential network security threats. If it is determined that the first data message does not contain a network security threat, the processor is further programmed to convert the first data message into a first data format associated with a vehicle environment, and transmit the converted first data message to the vehicle system using a first communication protocol associated with the vehicle system. According to one embodiment, security authentication for data transmission includes providing a hardware-based security engine (HSE) positioned in a communication system, the HSE having been manufactured in a secure environment and authenticated as part of an approved network in the secure environment; using the HSE to perform asynchronous authentication, verification and encryption of data, storing user permission data and connection status data in an access control list for defining allowable data communication paths of the approved network, enabling the communication system to communicate with other computing systems that comply with the access control list, using the security engine to perform asynchronous verification and encryption of data, including using a hardware-based module provided with one or more security aspects for making the system secure to identify a user device (UD), the user device (UD) incorporating credentials embodied in hardware, wherein the security aspects include the hardware-based module communicating with a user of the user device and the HSE.

[0292] Fig.14A FIG. 1 is a block diagram showing a network security module according to one embodiment. Fig.14A , data communications between the system 1400 including the processor 1408 and the server 1470 through the communication module 1412 are first verified by the information security management module 1432 before being transmitted from the system 1400 to the server 1470 or from the server 1470 to the system 1400. The information security management module is operable to analyze data for potential cybersecurity threats, encrypt the data when no cybersecurity threats are detected, and transmit the encrypted data to the system or server. Data communications between the system 1400 of the vehicle and the communication devices of the neighboring vehicles through the communication module 1412 of the host vehicle are also first verified by the information security management module 1432 before being transmitted from the system 1400 of the vehicle and the communication devices of the neighboring vehicles to the server 1470 or from the communication devices of the neighboring vehicles to the system 1400.

[0293] In one embodiment, the network security module further includes an information security management module that provides isolation between the system and the server. Fig. 14B A flow chart of protecting data by the network security module 1430 is shown. In step 1440, the information security management module is operable to receive data from the communication module. In step 1441, the information security management module exchanges security keys at the beginning of communication between the communication module and the server. In step 1442, the information security management module receives the security key from the server. In step 1443, the information security management module authenticates the identity of the server by verifying the security key. In step 1444, the information security management module analyzes the security key for potential network security threats. In step 1445, the information security management module negotiates an encryption key between the communication module and the server. In step 1446, the information security management module receives the encrypted data. In step 1447, when no network security threat is detected, the information security management module transmits the encrypted data to the server.

[0294] In one embodiment, Fig. 14CA flow chart for protecting data by the network security module 1430 is shown. In step 1451, the information security management module is operable to: exchange security keys at the beginning of communication between the communication module and the server. In step 1452, the information security management module receives the security key from the server. In step 1453, the information security management module authenticates the identity of the server by verifying the security key. In step 1454, the information security management module analyzes the security key for potential network security threats. In step 1455, the information security management module negotiates an encryption key between the communication module and the server. In step 1456, the information security management module receives encrypted data. In step 1457, the information security management module decrypts the encrypted data and performs an integrity check on the decrypted data. In step 1458, when no network security threat is detected, the information security management module transmits the decrypted data to the communication module.

[0295] In one embodiment, the integrity check is a hash signature verification using the Secure Hash Algorithm 256 (SHA256) or similar method.

[0296] In one embodiment, the information security management module is configured to perform asynchronous authentication and verification of communications between the communication module and the server.

[0297] In one embodiment, the information security management module is configured to issue an alarm if 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.

[0298] In one embodiment, the information security management module is configured to check the integrity of the decrypted data by checking for accuracy, consistency, and any possible data loss during communication through the communication module.

[0299] 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.14AAs 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 passes the information security management module, the communicating parties (system and server) negotiate the encryption key and integrity check key for the data communication between the communicating parties through the authenticated asymmetric key. The session ID number is transmitted during the identity authentication process, so that the key needs to be bound to the session ID number; when the system transmits data to the outside, the information security gateway receives the data through the communication module, performs integrity authentication on the data, and then encrypts the data through the negotiated key, and finally transmits the data to the server through the communication module. When the information security management module receives data through the communication module, it first decrypts the data, verifies the integrity of the decrypted data, and if the verification passes, sends the data out through the communication module; otherwise, the data is discarded.

[0300] In one embodiment, identity authentication is achieved by using an asymmetric key with a signature.

[0301] In one embodiment, the signature is implemented by a pair of asymmetric keys trusted by the information security management module and the system, where the private key is used to sign the identities of the two communicating parties, and the public key is used to verify that the identities of the two communicating parties are signed. The signing identity includes a public key and a private key pair. In other words, the signing identity is called the public name of the certificate installed in the user's machine.

[0302] In one embodiment, both communicating parties need to authenticate their own identities through a pair of asymmetric keys, and the task responsible for communicating with the information security management module of the system is identified by a unique pair of asymmetric keys.

[0303] In one embodiment, the dynamic negotiated 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.

[0304] In one embodiment, the data encryption method is a triple data encryption algorithm (3DES). The integrity check algorithm is a hash-based message authentication code (HMAC-MD5-128) algorithm. When outputting data, an integrity check calculation is performed on the data, and a message authentication code (MAC) value is calculated and added to the header before the data message of the value, and then the data (including the MAC of the header) is encrypted using the 3DES algorithm, and the header information of the security layer is added after encryption, 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.

[0305] The information security management module ensures the security, reliability and confidentiality of the communication between the system and the server through identity authentication when the communicating parties start data encryption and data integrity 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 cannot be compromised by hacker attacks under Internet conditions.

[0306] 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 those skilled in the art will recognize that many further combinations and / or permutations of one or more embodiments are possible. In addition, to the extent that the terms "including," "having," "having," or similar terms are used in the detailed description, claims, appendices, and / or drawings, such terms are intended to be inclusive in a manner similar to that in which the term "comprising" is interpreted when used as a transitional word in the claims.

[0307] Without departing from the spirit or features of the present invention, other specific forms may implement the present invention. The described embodiments are illustrative and non-restrictive in all respects. Therefore, the appended claims rather than the description herein indicate the scope of the present invention. All changes within the meaning and scope of the equivalents of the claims are within their scope.

[0308] Payment

[0309] 1. A system comprising:

[0310] A processor, a computer vision module, a communication module, and a control system, wherein the system is configured to:

[0311] detecting, by a processor, a lane reduction in a recently disappeared lane;

[0312] The computer vision module detects the neighboring vehicles in the lane that has recently disappeared and the driving actions of the neighboring vehicles;

[0313] The license plate numbers of neighboring vehicles are detected by the computer vision module;

[0314] receiving, by the communication module, a signal for a driving mode from a neighboring vehicle;

[0315] The lane vanishing point in the most recently disappeared lane is detected by the computer vision module;

[0316] The processor calculates the time interval between adjacent vehicles reaching the lane vanishing point;

[0317] performing, by the processor, a risk assessment of a collision based on the driving action; and

[0318] Control commands are transmitted to actuator systems of the host vehicle via the communication module based on the hazard assessment.

[0319] 2. A system according to clause 1, wherein the system is configured as a component of a host vehicle.

[0320] 3. The system of clause 2, wherein the host vehicle is a first autonomous vehicle.

[0321] 4. The system of clause 1, wherein the neighboring vehicle is one of a second autonomous vehicle and a non-autonomous vehicle.

[0322] 5. A system according to clause 4, wherein the driving mode is received via one of V2X communication or V2V communication.

[0323] 6. The system according to clause 5, wherein the driving mode is one of an autonomous mode and a manual mode.

[0324] 7. A system according to clause 1, wherein lane reduction is detected via the processor using information received via GPS, traffic signals received via a traffic control processor, and at least one of a computer vision module.

[0325] 8. A system according to clause 1, wherein at least one of radar, lidar, camera, GPS, and machine learning algorithms is used to detect neighboring vehicles.

[0326] 9. A system according to clause 1, wherein lane reduction is detected using at least one of radar, lidar, camera, GPS, and a machine learning algorithm.

[0327] 10. A system according to clause 1, wherein the vehicle type of the neighboring vehicle is determined from the license plate number by reference to a database.

[0328] 11. A system according to clause 1, wherein the signal from the neighboring vehicle is received via vehicle-to-vehicle (V2V) communication.

[0329] 12. A system according to clause 11, wherein 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.

[0330] 13. A system according to clause 1, wherein the signal from the neighboring vehicle is received via an internet connection.

[0331] 14. A system according to clause 6, wherein the driving mode of the neighboring vehicle is a manual mode.

[0332] 15. A system according to clause 19, wherein the driving action of the neighboring vehicle is detected by capturing at least one of steering angle, steering rate, speed and acceleration via a computer vision module.

[0333] 16. The system of clause 15, wherein the host vehicle is configured to establish communications with neighboring vehicles to obtain steering angle, speed, and acceleration.

[0334] 17. A system according to clause 19, wherein the collision may occur during a lane change of an adjacent vehicle.

[0335] 18. The system of clause 17, wherein the host vehicle is configured to establish communication with a neighboring vehicle and provide boundaries for the impact zone when the neighboring vehicle is moving to merge into a lane change.

[0336] 19. The system of clause 18, wherein the host vehicle establishes communication with neighboring vehicles and requests an adjustment of the motion trajectory while merging into a lane change.

[0337] 20. The system of clause 18, wherein the host vehicle establishes communication with the neighboring vehicle and provides the boundaries of the impact zone of the neighboring vehicle.

[0338] 21. A system according to clause 1, wherein the lane vanishing point is estimated by performing lane width detection.

[0339] 22. The system of clause 18, wherein the control command is generated by determining a collision avoidance strategy for the host vehicle to avoid the impact zone; and

[0340] The collision avoidance maneuver is performed autonomously by the host vehicle.

[0341] 23. A system according to clause 1, wherein the system is configured to determine a time period for neighboring vehicles to reach a lane vanishing point.

[0342] 24. A system according to clause 1, wherein the system is configured to transmit a second signal for changing the driving maneuver to avoid a collision.

[0343] 25. A vehicle comprising a system, the system further comprising:

[0344] Processor, computer vision module, communication module and control system,

[0345] The system is configured as follows:

[0346] detecting, by a processor, a lane reduction in a recently disappeared lane;

[0347] The computer vision module detects the neighboring vehicles in the lane that has recently disappeared and the driving actions of the neighboring vehicles;

[0348] The license plates of neighboring vehicles are detected by a computer vision module;

[0349] receiving, by the communication module, a first signal for a driving mode from a neighboring vehicle;

[0350] The lane vanishing point in the most recently disappeared lane is detected by the computer vision module;

[0351] Determining, by a processor, a time interval between adjacent vehicles reaching a lane vanishing point;

[0352] performing, by the processor, a risk assessment of a collision based on the driving action; and

[0353] Control commands are transmitted to actuator systems of the vehicle via the communication module based on the risk assessment.

[0354] 26. A method for detecting and preventing a possible accident of a host vehicle in lane reduction, the method comprising:

[0355] Detect lane reduction in recently disappeared lanes;

[0356] Detection of neighboring vehicles and driving maneuvers of neighboring vehicles in recently disappeared lanes;

[0357] Detecting license plates of neighboring vehicles;

[0358] receiving a first signal for a driving mode from a neighboring vehicle;

[0359] Detecting lane vanishing points in the most recently vanished lane;

[0360] Determine the time interval between adjacent vehicles reaching the lane vanishing point;

[0361] Performing a collision risk assessment based on driving maneuvers and time intervals; and

[0362] Control commands are transmitted to actuator systems of the host vehicle based on the risk assessment.

[0363] 27. The method of clause 26, wherein lane reduction is detected using at least one of GPS information, traffic signs, and lane markings.

[0364] 28. The method according to clause 27, wherein the method further comprises detecting a cone distance from a lane marking.

[0365] 29. A method according to clause 26, wherein a camera of the host vehicle is used to detect neighboring vehicles.

[0366] 30. A method according to clause 29, wherein a camera is used to detect the license plate.

[0367] 31. A method according to clause 29, wherein the camera is connected to a computer vision system of the host vehicle.

[0368] 32. A method according to clause 31, wherein the method further comprises determining the current position of the neighboring vehicles and the host vehicle.

[0369] 33. A method according to clause 32, wherein the current position of the neighboring vehicle is detected by a radar sensor of a computer vision module of the host vehicle.

[0370] 34. The method of clause 32, wherein a global navigation satellite system (GPS) is used to detect the current position of the host vehicle and the current position of the neighboring vehicles.

[0371] 35. A method according to clause 32, wherein the method further comprises predicting lane changes of neighboring vehicles.

[0372] 36. A method according to clause 35, wherein lane changes are predicted by capturing at least one of steering angle, steering rate, speed, and acceleration.

[0373] 37. A method according to clause 31, wherein the method further comprises detecting a lane change of an adjacent vehicle.

[0374] 38. A method according to clause 37, wherein a computer vision system is used to detect lane changes via turn signals of neighboring vehicles.

[0375] 39. A method according to clause 38, wherein lane changes of neighboring vehicles are detected by tracking the position and movement of neighboring vehicles over time.

[0376] 40. A method according to clause 38, wherein the lane change of the neighboring vehicle is detected by detecting the speed and direction of the movement of the neighboring vehicle via a sensor of the host vehicle, wherein the sensor is at least one of a radar and a lidar.

[0377] 41. A method according to clause 26, wherein the method further comprises detecting the lane width of the lane that most recently disappeared.

[0378] 42. A non-transitory computer-readable medium having instructions stored thereon, the instructions being executable by a computer system to perform operations comprising:

[0379] Detect lane reduction in recently disappeared lanes;

[0380] Detection of neighboring vehicles and driving maneuvers of neighboring vehicles in recently disappeared lanes;

[0381] Detecting license plates of neighboring vehicles;

[0382] receiving a first signal for a driving mode from a neighboring vehicle;

[0383] Detecting lane vanishing points in the most recently vanished lane;

[0384] Determine the time interval between adjacent vehicles reaching the lane vanishing point;

[0385] Performing a collision risk assessment based on driving maneuvers and time intervals; and

[0386] Control commands are transmitted to actuator systems of the host vehicle based on the risk assessment.

[0387] 43. A system comprising:

[0388] a processor, a computer vision system, and a control system, wherein the system is configured as a component of a host vehicle;

[0389] The processor is configured as:

[0390] Detect lane reduction in recently disappeared lanes;

[0391] Detection of neighboring vehicles in the recently disappeared lane;

[0392] Detecting license plates of neighboring vehicles;

[0393] receiving a first signal for a driving mode from a neighboring vehicle;

[0394] Detecting lane vanishing points in the most recently vanished lane;

[0395] Determine the time interval between adjacent vehicles reaching the lane vanishing point;

[0396] Predict the driving maneuvers of neighboring vehicles;

[0397] Performing hazard assessment based on driving actions and time intervals; and

[0398] Transmits hazard signals to neighboring vehicles based on the hazard assessment.

[0399] 44. The system of clause 43, wherein the system is further configured to transmit control commands to an actuator system of the host vehicle based on the risk assessment.

[0400] 45. A method comprising:

[0401] Detect lane reduction in recently disappeared lanes;

[0402] Detection of neighboring vehicles in the recently disappeared lane;

[0403] Detecting license plates of neighboring vehicles;

[0404] receiving a first signal for a driving mode from a neighboring vehicle;

[0405] Detecting lane vanishing points in the most recently vanished lane;

[0406] Determine the time interval between adjacent vehicles reaching the lane vanishing point;

[0407] Predict the driving maneuvers of neighboring vehicles;

[0408] Performing hazard assessment based on driving actions and time intervals; and

[0409] Transmits hazard signals to neighboring vehicles based on the hazard assessment.

[0410] 46. ​​A method according to clause 45, wherein the method is performed by a host vehicle.

[0411] 47. A method according to clause 46, wherein the host vehicle is an autonomous vehicle.

[0412] 48. The method of clause 46, further comprising transmitting control commands to an actuator system of the host vehicle based on the risk assessment.

[0413] 49. A non-transitory computer-readable medium having instructions stored thereon, the instructions being executable by a computer system to perform operations comprising:

[0414] Reduction of detection lanes;

[0415] Detection of neighboring vehicles in the recently disappeared lane;

[0416] detecting the license plate of a neighboring vehicle and checking in a license plate database whether the license plate number is assigned to a non-autonomous vehicle;

[0417] Lane width detection is performed to detect lane vanishing points in the most recently vanished lane;

[0418] Determine the time interval between adjacent vehicles reaching the lane vanishing point;

[0419] Predict the driving maneuvers of neighboring vehicles;

[0420] Performing hazard assessment based on driving actions and time intervals; and

[0421] Transmits hazard signals to neighboring vehicles based on the hazard assessment.

Claims

1. A system, include: A processor, a computer vision module, a communication module, and a control system, wherein the system is configured to: detecting, by the processor, a lane reduction in a recently disappeared lane; Detecting, by the computer vision module, a neighboring vehicle in the lane that most recently disappeared and a driving action of the neighboring vehicle; detecting the license plate number of the neighboring vehicle by the computer vision module; receiving, by the communication module, a signal for a driving mode from the neighboring vehicle; detecting, by the computer vision module, a lane vanishing point in the most recently vanished lane; Determining, by the processor, a time interval for the neighboring vehicle to reach the lane vanishing point; performing, by the processor, a collision risk assessment based on the driving action; as well as Control commands are transmitted to an actuator system of a host vehicle via the communication module based on the hazard assessment.

2. The system of claim 1, wherein the system is configured as a component of the host vehicle. 3 . The system according to claim 1 , wherein the driving mode is one of an autonomous mode and a manual mode.

4. The system of claim 1, wherein the neighboring vehicle is detected using at least one of radar, lidar, camera, GPS, and a machine learning algorithm. 5 . The system of claim 1 , wherein the vehicle type of the neighboring vehicle is determined from the license plate number by referencing a license plate database. 6 . The system of claim 1 , wherein the signal from the neighboring vehicle is received via vehicle-to-vehicle (V2V) communication.

7. The system of claim 6, wherein 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.

8. The system of claim 1, wherein the signal from the neighboring vehicle is received via an Internet connection.

9. The system of claim 1, wherein the driving maneuver of the neighboring vehicle is detected by capturing at least one of a steering angle, a steering rate, a speed, and an acceleration via the computer vision module.

10. The system of claim 1, wherein the lane vanishing point is estimated by performing lane width detection.

11. The system of claim 1, wherein the system is configured to calculate a time period for the neighboring vehicle to reach the lane vanishing point.

12. The system of claim 1, wherein the system is configured to transmit a second signal for changing the driving maneuver to avoid the collision.

13. A method for detecting and preventing a possible accident of a host vehicle in lane reduction, the method include: Detect lane reduction in recently disappeared lanes; detecting an adjacent vehicle in the lane that has most recently disappeared and a driving action of the adjacent vehicle; detecting a license plate of the neighboring vehicle; receiving a first signal for a driving mode from the neighboring vehicle; detecting a lane vanishing point in the most recently vanished lane; Determine the time interval between the adjacent vehicles reaching the lane vanishing point; performing a collision risk assessment based on the driving action and the time interval; as well as Control commands are transmitted to actuator systems of the host vehicle based on the risk assessment.

14. The method of claim 13, wherein the lane reduction is detected using at least one of GPS information, traffic signs, and lane markings. 15 . The method according to claim 13 , wherein the method further comprises detecting a cone distance from the lane marker.

16. The method of claim 13, wherein a camera is used to detect the license plate of the neighboring vehicle.

17. The method of claim 16, wherein the camera is connected to a computer vision system.

18. The method of claim 13, wherein the method further comprises predicting a lane change of the neighboring vehicle; and wherein the lane change is predicted by capturing at least one of a steering angle, a steering rate, a speed, and an acceleration.

19. The method of claim 17, wherein the method further comprises detecting a lane change of the neighboring vehicle; and wherein the lane change of the neighboring vehicle is detected via a sensor by detecting a speed and a direction of movement of the neighboring vehicle, wherein the sensor is at least one of a radar and a lidar.

20. A non-transitory computer readable medium having instructions stored thereon, the instructions being executable by a computer system to perform operations comprising: Detect lane reduction in recently disappeared lanes; detecting an adjacent vehicle in the lane that has most recently disappeared and a driving action of the adjacent vehicle; detecting a license plate of the neighboring vehicle; receiving a first signal for a driving mode from the neighboring vehicle; detecting a lane vanishing point in the most recently vanished lane; Determine the time interval between the adjacent vehicles reaching the lane vanishing point; performing a collision risk assessment based on the driving action and the time interval; as well as Control commands are transmitted to actuator systems of the host vehicle based on the risk assessment.

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