A system and method for calculating the liability of a vehicle driver.

Abstract

The present invention provides a system, apparatus, and method for generating or calculating vehicle liability and operating costs based on the driver's operation of the vehicle. [Solution] In architecture 100, the vehicle module 104 uses a combination of sensors, video input and onboard artificial intelligence and / or machine learning algorithms to identify dangerous events performed by the vehicle driver, generate, calculate and evaluate the vehicle driver's driving score, and transmit the calculation results to one or more entities.

Description

Technical Field

[0001] The technology described below generally relates to systems, devices, and methods for generating and calculating vehicle accident risks and operating costs by evaluating a driver's vehicle operations or performance. More specifically, an individual's driving score is generated or calculated based at least on sensors, video input, and artificial intelligence algorithms.

[0002] Priority Claim This application claims the priority and benefit of Provisional Patent Application No. 62 / 849,763, filed with the United States Patent and Trademark Office on May 17, 2019, the entire content of which is incorporated herein by reference as if fully set forth below for all applicable purposes.

Background Art

[0003] Currently, the insurance industry relies on complex processes with overlapping systems and administrative processes that rely heavily on humans to process claims. The insurance claim processing process often includes (1) the step of receiving an accident report, (2) the step of contacting the customer and verifying contract information, (3) the step of creating a hand-drawn accident diagram and sending it to the claim processing department, (4) the step of the claim processing department contacting the customer to re-verify accident information, and (5) the step of manually entering information into the claim processing system.

[0004] After a claim is entered into the system, an appraiser needs to review the damaged vehicle that is the subject of the claim to confirm the facts and determine a cost estimate. If a personal injury accident is reported, an interview with the injured person is conducted. Next, the appraiser manually checks similar accidents, determines the expected costs, determines the percentage of fault, and makes payments to each party.

[0005] The process described above requires a great deal of manual processing and review, which increases insurance company costs and delays the settlement process. Furthermore, the accuracy and neutrality of the process vary depending on which assessor handles the claim. For these reasons, there is a need for automated systems and methods that streamline the claims process, extend turnaround time, and ensure process neutrality. [Overview of the project]

[0006] The following outlines one or more aspects of the Disclosure to provide a basic understanding of such aspects. This outline is not a comprehensive overview of all intended features of the Disclosure, nor is it intended to identify the main or important elements of all aspects of the Disclosure, nor to clarify the scope of any or all aspects of the Disclosure. Its sole purpose is to present some concepts of one or more aspects of the Disclosure in a predetermined format as a preliminary step to the more detailed descriptions that will be presented later.

[0007] In one embodiment, the Disclosure provides one or more non-temporary computer-readable media for storing computer-executable instructions that cause one or more processors to perform operations at runtime. These operations include: receiving performance data relating to the performance of a vehicle driver from multiple sources; identifying at least one hazardous event affecting the driver's performance, wherein the at least one hazardous event adversely affects the performance data such that it falls below a predetermined threshold; analyzing the performance data using a trained machine learning model to determine the severity level of each identified hazardous event affecting the driver's performance, wherein the trained machine learning model employs multiple types of machine learning algorithms to analyze the performance data; generating at least one score based on the performance data for presentation via a user interface; and refining the machine learning model based on the generated at least one score, wherein the refinement includes retraining the machine learning model on at least one modified training corpus based on the performance data.

[0008] The above operation may further include the steps of notifying at least one user of at least one score via a user interface, initiating the recording of video data using a camera mounted on the vehicle, and analyzing the recorded video data using vehicle threshold events to identify at least one dangerous event.

[0009] The above operation may further include the steps of using an object detection algorithm to identify external objects from recorded video footage that affect performance data, and transmitting the latitude and longitude coordinates of the identified objects to a diagnostic module processor for further processing to determine the object's impact on at least one hazardous event.

[0010] The above operation may further include the steps of initiating the saving of vehicle data from the diagnostic module, the engine control unit module, and the autonomous driving module, and analyzing the vehicle threshold events in the saved vehicle data to identify at least one dangerous event.

[0011] The above operation may further include the steps of determining the occurrence of an accident from performance data and transmitting information related to the accident to a third party.

[0012] The above operation may further include the steps of updating at least one score after a severity level has been assigned to at least one hazardous event, storing the updated score in an onboard memory device in the vehicle, and transmitting the updated score to a remote data database.

[0013] In another embodiment, a computer-based method for evaluating driver risk is provided. The method comprises the steps of: a processor in a vehicle module installed in a vehicle receiving performance data relating to the driver performance of the vehicle from multiple sources installed in the vehicle; the processor in the vehicle module identifying at least one hazardous event affecting the driver performance, wherein the at least one hazardous event adversely affects the performance data to a threshold set in advance; analyzing the performance data using a trained machine learning model in the processor in the vehicle module and determining the severity level of each identified hazardous event affecting the driver performance, wherein the trained machine learning model employs multiple types of machine learning algorithms to analyze the performance data; the processor in the vehicle module generating at least one score based on the performance data for presentation via a user interface; and the processor in the vehicle module refining the machine learning model based on the generated at least one score, wherein the refinement includes retraining the machine learning model based on at least one modified training corpus based on the performance data.

[0014] In yet another embodiment, a computing device mounted in a vehicle is provided. The device includes an interface and a processing circuit coupled to the interface. The processing circuit is configured to perform the steps of: receiving performance data relating to the performance of a vehicle driver from multiple sources; identifying at least one hazardous event affecting the driver's performance, wherein the at least one hazardous event has such an adverse effect that the performance data falls below a predetermined threshold; analyzing the performance data using a trained machine learning model and determining the severity level of each identified hazardous event affecting the driver's performance, wherein the trained machine learning model employs multiple types of machine learning algorithms to analyze the performance data; generating at least one score based on the performance data for presentation to a user; and improving the machine learning model based on the generated at least one score, wherein the improvement includes retraining the machine learning model based on at least one training corpus modified based on the performance data. [Brief explanation of the drawing]

[0015] Various features, properties, and advantages will become clear from the detailed description below, along with the drawings. In the drawings, similar reference numerals are used throughout to identify them accordingly. [Figure 1] Figure 1 is a conceptual diagram showing the operating environment in which embodiments of the system and method of this disclosure are found. [Figure 2] Figure 2 is a block diagram showing exemplary hardware configurations of a vehicle module according to some aspects of the present disclosure. [Figure 3] Figure 3 is a block diagram showing exemplary hardware configurations of a user interface module and a data platform module according to some aspects of this disclosure. [Figure 4]Figure 4 is a block diagram illustrating exemplary hardware configurations of a device (e.g., an electronic device) capable of supporting communications according to some aspects of the present disclosure. [Figure 5] Figure 5 is a block diagram of an exemplary hardware configuration of a data platform module / device configured to communicate according to one or more aspects of the present disclosure. [Figure 6] Figure 6 shows a block diagram of an exemplary hardware configuration of a user interface module / device configured to communicate according to one or more aspects of this disclosure. [Figure 7] Figure 7 is a flowchart illustrating an exemplary method for calculating a driver's score for a vehicle driver. [Figure 8] Figure 8 is a flowchart illustrating an exemplary method for determining the safety of the driver and the vehicle. [Figure 9] Figure 9 is a diagram that summarizes the overall process of the insurance system related to insurance. [Figure 10] Figure 10 shows the process for detecting an event. [Figure 11] Figure 11 shows the process of using data from multiple device sensors to improve the accuracy of estimating the position and movement of vehicles and objects when detecting an event. [Figure 12] Figure 12 illustrates how to assign fault to the entities involved using an accident report. [Figure 13] Figure 13 illustrates the process of reducing the amount of manual work involved in handling claims and payments to the parties involved. [Modes for carrying out the invention]

[0016] overview Exemplary systems, devices, and methods for generating or calculating vehicle liability and operating costs based on a vehicle driver's operations and performance are described herein. Using a combination of vehicle sensors, video inputs, and on-board artificial intelligence and / or machine learning algorithms, the systems and methods of the present disclosure can identify dangerous events executed by a vehicle driver, generate, calculate, and evaluate a driving score and a trip score for the vehicle driver, and transmit the calculation results to one or more entities. Also, the first notice of loss (FNOL) process, which is the first report made to an insurance company after a loss, theft, or damage to an insured asset, is also automated for higher accuracy, response time, and lower operating costs.

[0017] The term "sensor" can refer to any type of known sensor for detecting the dynamic state of a vehicle. The sensor may be an original equipment or a commercially available tool. Sensors include, but are not limited to, mass air flow sensors, engine speed sensors, oxygen sensors, spark knock sensors, coolant sensors, manifold absolute pressure (MAP) sensors, fuel temperature sensors, voltage sensors, camshaft position sensors, throttle position sensors, vehicle speed sensors or speedometers, proximity sensors, accelerometers, global positioning systems, odometers, steering angle sensors, safety system data, radio detection and ranging (RADAR), light detection and ranging (LIDAR), and diagnostic trouble codes.

[0018] The terms "sensor data" and "vehicle sensor data" refer to data received from any vehicle sensor, regardless of whether it is an original equipment or a commercially available tool.

[0019] The term "vehicle" refers to any type of machine that transports people or goods, including, but not limited to, automobiles, trucks, buses, motorcycles, airplanes, and helicopters.

[0020] The term "hazardous event" refers to any event or incident that occurs while driving and adversely affects the driver's performance, and includes, but is not limited to, braking at a certain speed, accelerating at a certain speed, time spent swerving before a collision, cornering at a certain speed, trip time, trip distance, failure to stop at a stop sign, rolling stop, complete stop, speeding distance, trip cost, trip start time, trip end time, trip circumstances, and trip score.

[0021] Some of the methods described herein may be implemented in hardware such as servers, user interfaces or devices, vehicle modules and data modules. Each of these devices can determine whether a dangerous event has occurred, generate a driver score, and transmit accident data to an emergency response vehicle via cellular and / or other network communication.

[0022] As used herein, the term “computer-readable medium” refers to any tangible storage device involved in providing instructions to a processor for execution. Such media can take many forms, including, but are not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, NVRAM, or magnetic disks and optical disks. Volatile media include dynamic memory such as main memory. Common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tapes, other magnetic media, magneto-optical media, CD-ROMs, other optical media, punch cards, paper tapes, other physical media having a pattern of holes, RAM, PROMs, EPROMs, FLASH-EPROMs, solid-state media such as memory cards, other memory chips and cartridges, or other computer-readable media. When computer-readable media is configured as a database, it should be understood that it may be any type of database, such as relational, hierarchical, and / or object-oriented. For this reason, this disclosure is considered to include tangible storage media and equivalents and successor media recognized in the prior art in which the software implementation of this disclosure is stored.

[0023] As used herein, “Central Processing Unit,” “Processor,” “Processor Circuit,” and “Processing Circuit,” as well as their variations, are interchangeable and designed to perform the functions described herein, and include, but are not limited to, general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic components, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may include, in addition to a microprocessor, any conventional processor, controller, microcontroller, or state machine. Furthermore, a processor may be implemented as a combination of computing components, such as a combination of a DSP and a microprocessor, a number of microprocessors, one or more microprocessors combined with a DSP core, an ASIC and a microprocessor, or any other variety of configurations. These examples of processors are for illustrative purposes only, and other suitable configurations within the scope of this disclosure are also contemplated. In addition, a processor may be implemented as one or more processors, one or more controllers, and / or other structures configured to perform executable programming.

[0024] As used herein, the terms “specify,” “calculate,” and “operate,” as well as their variations, are used interchangeably and include any type of methodology, process, mathematical operation, or technique.

[0025] As used herein, the term “module” refers to known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and software that is capable of performing functions related to its elements.

[0026] The terms “user interface” and “user interface module” can be embodied or implemented in servers, personal computers, mobile phones, smartphones, tablets, portable computers, machines, entertainment devices, or other electronic devices having circuits. The systems described herein can identify and draw bounding boxes around a variety of objects. These objects include, but are not limited to, people, bicycles, automobiles, motorcycles, buses, trains, trucks, boats, traffic lights, fire hydrants, stop signs, and dogs.

[0027] The term "driver" can refer to a person or a vehicle with autonomous driving features in operation.

[0028] The detailed descriptions below, in relation to the attached drawings, are intended to illustrate various configurations and are not intended to show only the configurations in which the concepts described herein can be implemented. The detailed descriptions include specific details to provide a complete understanding of the various concepts. However, it will be apparent to those skilled in the art that these concepts can be implemented without specific details. Sometimes, well-known structures and components are shown in block diagram form to avoid obscuring such concepts. In this specification, a singular reference to an element is intended to refer to a plural element.

[0029] Figure 1 shows an exemplary architecture 100 for generating or calculating vehicle liability and operating costs based on the driver's operation or performance of the vehicle, from which embodiments of the systems and methods of this disclosure may be found. The system includes a vehicle module 104, a user interface module 106, a data platform 108, and a communication network 102 connecting a remote file system or server 142. A third-party application protocol interface 109 can access what is generated on the vehicle module 104 and the data platform 108. The vehicle module 104, user interface 106, data platform 108, remote file system or server 142, and third-party application protocol interface 109 are described in more detail below.

[0030] System 100 uses data obtained from the vehicle to calculate one or more risk scores regarding the driver's vehicle operation. The data is obtained from a combination of vehicle sensors, video input, and onboard artificial intelligence / machine learning.

[0031] In some embodiments, the system 100 can communicate with a local data storage device 112 and / or a remote server 142, or any combination of local and remote data storage devices and a file system.

[0032] A local file system (and / or remote file system) can control how data in the local data storage device 112 and / or remote data storage device 142 is stored and retrieved. In some embodiments, the structure and logical rules used to manage the set of information stored as data in the local data storage device 112 and / or remote data storage device 142 can be called a “file system” (e.g., local file system and / or remote file system). Local file systems and / or remote file systems may each have different structures and logics, speeds, flexibility, security, size, and other characteristics. In some embodiments, the structure and logic of local file systems and / or remote file systems provide improved speed and security compared to other known file systems. Local data storage devices and / or remote data storage devices can use the same or different media on which data is stored. Examples of media include magnetic disks, magnetic tapes, optical disks, and electronic memory (such as flash memory).

[0033] Communication between any or all of the apparatus, devices, systems, functions, modules, and services and servers described herein may be conducted via one or more wired and / or wireless communication networks 134. Examples of one or more communication networks 134 include TCP / IP data networks such as public switched telephone networks (PSTNs), wide area networks (WANs), local area networks (LANs), the Internet, and wireless networks such as 3G, 4G, LTE, and 5G networks published by the Third Generation Partnership Project (3GPP). One or more communication networks 134 may be any one or more of the following two or more communication networks, but are not limited to them.

[0034] Figure 2 is a block diagram showing exemplary hardware configurations of an onboard vehicle module according to some aspects of the present disclosure. The vehicle module 104 monitors the driver's actions within the vehicle. According to at least some embodiments, the vehicle module 104 may include a processor 110, local data storage 112, one or more sensors 114, a diagnostic module 116, an engine control unit (ECU) 118, a communication interface 120, and an autopilot module 121 having an autopilot switch 123 for tracking when the vehicle's autopilot function is activated, when it has been activated, whether the driver or the vehicle has activated the autopilot function, and when the autopilot function has been deactivated, if the autopilot function is available. Information on whether the autopilot function was activated can be used to determine whether a dangerous event was caused by the driver or the vehicle.

[0035] The vehicle module 104 collects driver data using various sensors 114 installed in the vehicle (e.g., speed sensors, accelerometers, GPS locators, tire pressure sensors, autonomous driving sensors, and audio / visual sensors such as backup cameras, and anti-theft devices), which are typically connected to the ECU via, for example, a Controller Area Network (CAN) bus. From the collected vehicle sensor data and / or video metadata, the processor 110 uses artificial intelligence and / or machine learning module 124, based on insurance machine learning algorithms and extensive data collection and analysis previously gathered, to calculate a driving score that includes the risk and safety of a particular trip.

[0036] In one embodiment, the system can determine which vehicle sensors are available and, if necessary, disable modules and sensors. When modules and sensors are disabled, the system adjusts the risk calculation accordingly, taking into account the amount of available data. In the trip summary for each vehicle, the system can output the trip score and the overall trip severity. Over time, the system can build a driver risk profile based on the average trip score. This score can be used with the risk profile to estimate the cost and risk of the vehicle itself, as well as to evaluate the vehicle's driver.

[0037] The vehicle module 104 is also equipped with a user interface 106, a data platform 108, and a server 142 connected to the communication network 102, as well as a network communication interface 120 that enables communication via a wireless communication link to the communication network 102.

[0038] The machine learning module 124 consists of at least one insurance machine learning algorithm for analyzing data from the sensor 114, the diagnostic module 116, the engine control unit 118, and the autonomous driving module 121 to generate driver scores and trip information. In various embodiments, the machine learning module 124 generates a machine learning model using a machine learning training pipeline.

[0039] The diagnostic module 116 can store data related to the vehicle's self-diagnostic and reporting functions in onboard memory or local data storage 112, and retrieve data from there. The diagnostic module 116 can analyze the received data, diagnose potential problems, and prepare data for presentation to the user, driver, insurance company, or other entity. The diagnostic module 116 can communicate with the user interface module 106 and the data platform module 108 via the communication interface 120. The diagnostic module 106 can receive commands / instructions via the communication interface 120 or the processor 110, acquire and / or generate appropriate information, and provide it to the data platform module 108 for presentation to the user, driver, insurance company, or other entity.

[0040] The ECU118 controls a series of actuators in the internal combustion engine to ensure optimal engine performance by reading values ​​from sensors in the engine compartment. It interprets the data using a multidimensional performance map (called a lookup table) and adjusts the engine actuators accordingly. The ECU118 can monitor and set, for example, the air-fuel mixture, ignition timing, idle speed, etc. Data from the ECU118 is provided to the machine learning module 124, which uses machine learning algorithms and pre-collected data to calculate a driver risk score based on the driver's driving behavior.

[0041] Figure 3 is a block diagram showing exemplary hardware configurations of the user interface module 106 and the data platform module 108 according to several aspects of the present disclosure. The user interface module 106 enables third parties to obtain vehicle and driver data information as a result of accident / anomaly analysis, fault liability assignment, payment subrogation, and claim payment. This information can be transmitted to drivers and insurance companies via various user interfaces based on API connections configured to accept output from insurance systems.

[0042] The user interface module 106 may include a processor 132, a vehicle head-up display (HUD) module 134, a dashboard module 136, a communication interface 138, and a memory or data storage module 140. The user interface module allows the user to load and process sensor data that is directly provided to the machine learning module 124 (see Figure 2).

[0043] The user interface module 106 enables external parties or third parties to receive event and abnormal behavior data. The user interface module 106 can use its communication interface to establish a connection to an API on the user interface module 106 and retrieve data stored in local memory or remote data storage 142. The vehicle HUD module 136 and dashboard module 136 on the user interface module 106 enable the user to view the data.

[0044] The user interface module 106 is connected to the communication network 104 and can therefore be considered a network computing device. The user interface 106 may include a network or communication interface 138 or multiple network interfaces that enable the user interface module 106 to communicate over various types of communication networks. For example, the user interface module 106 may include a network interface card, an antenna, an antenna driver, an Ethernet port, etc. Other examples of the user interface module 106 include, but are not limited to, laptops, tablets, mobile phones, personal digital assistants (PDAs), thin clients, supercomputers, servers, proxy servers, communication switches, set-top boxes (STBs), smart TVs, etc. The processor 110 calculates a driving score, including the risk and safety of a particular trip, using the artificial intelligence and / or machine learning module 124, based on the insurance machine learning algorithm and the extensive data collection and analysis previously gathered. In other words, the machine learning module 124 uses its built-in machine learning methods to locally calculate scores (such as driver score, trip score, and risk score) on the vehicle module based on captured vehicle sensor and video data that has been evaluated against a wide range of previously collected datasets. Once the score and trip summary are complete, the vehicle module 102 establishes a connection with a networked server to transmit the data, thereby allowing the user to evaluate the driver's performance against the established dataset.

[0045] Turning to the data platform module 108, it has higher computing and memory capabilities compared to the vehicle processing system of the vehicle module 104. This data platform 108 is used to further process the incoming data and can improve the accuracy of estimation results by using algorithms that are difficult to run on the vehicle due to performance, cost, or physical size constraints. Furthermore, the data platform module 108 houses a process that enables communication with third parties for a) requesting data from other vehicles or devices, b) collecting data from other vehicles or devices, and / or c) communicating with other vehicles or devices. In addition, this data platform 108 includes a processor 126, a graphics processing unit (GPU) 128, and a communication interface 130 that enables communication with third parties for a) requesting data from other vehicles or devices, b) collecting data from other vehicles or devices, and / or c) communicating with other vehicles or devices.

[0046] For example, the system can use the GPU to retain up to 60 seconds of video data at a time. When the system detects a hazardous event, the GPU can save 10 seconds of video before and after the hazardous event to the vehicle module's onboard storage. The data is retained and observed so that the system can reconstruct video clips and telemetry data from around the event when it occurs. The system can update trip data to remote data storage or a server at regular intervals. These regular intervals include, but are not limited to, the start of a trip, the end of a trip, and every 10 seconds of a trip.

[0047] Information resulting from a) accident / anomaly analysis, b) fault liability assignment, c) payment subrogation, and / or d) claim payment can be transmitted to the driver and insurance company via various user interfaces, based on API connections configured to accept output from the insurance system.

[0048] GPU input can be received from vehicle cameras such as dashboard cameras and driver assistance cameras. When the dashboard camera is activated, the system verifies that the module is operational by taking a camera shot. If the system fails to take a camera shot, it can retry, for example, every 0.01 seconds. If the system detects that the camera is not working or that video recording has ended, the system sends a kill command to a subprocess that analyzes and processes the video recording. The video can be processed locally on the vehicle or on another device.

[0049] The user interface 106 is connected to the communication network 104 and can therefore be considered a network computing device. The user interface 106 may include a network or communication interface 138 or multiple network interfaces that enable the user interface 106 to communicate over various types of communication networks. For example, the user interface 106 may include a network interface card, an antenna, an antenna driver, an Ethernet port, etc. Other examples of the user interface 106 include, but are not limited to, laptops, tablets, mobile phones, personal digital assistants (PDAs), thin clients, supercomputers, servers, proxy servers, communication switches, set-top boxes (STBs), smart TVs, etc.

[0050] Hazardous events are detected by monitoring data observed by vehicle sensors or the CAN bus. If the system observes sensory data approaching a preset threshold level, or a risk pattern matching a machine learning-simulated model, the system records the hazardous or abnormal situation. Upon recording a hazardous or abnormal situation, the system's sensor module filters the event data and available video recordings and passes them to a module that handles the scoring model.

[0051] Figure 4 shows a block diagram of an exemplary hardware configuration of a vehicle module / device 400 configured to communicate according to one or more aspects of the present disclosure. The vehicle module 400 may include, for example, a communication interface 402. The communication interface 402 may enable data and control input and output. The communication interface 402 may enable communication over one or more communication networks, for example, one or more communication networks 102 in Figure 1. The communication interface 402 may be communicably coupled to one or more communication networks 102, directly or indirectly. The vehicle module 400 may include a local working memory device 404 and a processor system / function / module / device (hereinafter, processor 406). The processor 406 may use the working memory device 404 to store data to be manipulated, data being manipulated, or recently manipulated data. The processor 406 may store instructions on the working memory device 404 and / or on one or more other memory structures or devices, such as a non-temporary computer-readable media system / function / module / device (hereinafter referred to as non-temporary computer-readable media 408). When the instructions are executed by the processor 406, the processor 406 may be made to execute, for example, one or more aspects of the methods described herein.

[0052] The vehicle module 400 may be implemented in a bus architecture generally represented by bus 410. Bus 410 may include any number of interconnection buses and bridges, depending on the specific application and overall design constraints of the vehicle module 400. Bus 410 can communicatively connect various circuits, including one or more processors (generally represented by processor 406), a working memory device 404, a communication interface 402, and a non-temporary computer-readable medium 408. Bus 410 can also connect various other circuits and devices, such as timing sources, peripherals, voltage regulators, and power management circuits and devices. These are well known in the art and will not be described further.

[0053] The communication interface 402 provides means for communicating with other devices via a transmission medium. In some embodiments, the communication interface 402 includes circuitry and / or programming adapted to facilitate bidirectional communication of information with respect to one or more communication devices in a network. In some embodiments, the communication interface 402 is adapted to facilitate wireless communication of the vehicle module 400. In these embodiments, the communication interface 402 can be coupled to one or more antennas 412 for wireless communication within a wireless communication system, as shown in Figure 4. In some embodiments, the communication interface 402 can be configured for wire-based communication. For example, the communication interface 402 may be a bus interface, a transmit / receive interface, or other type of signal interface including drivers, buffers, or other circuitry for outputting and / or acquiring signals (e.g., outputting signals from and / or receiving signals to an integrated circuit). The communication interface 402 may consist of one or more standalone receivers and / or transmitters, as well as one or more transceivers. In the illustrated example, the communication interface 402 includes a transmitter 414 and a receiver 416. The communication interface 402 functions as an example of a receiving means and / or a transmitting means.

[0054] The processor 406 can be responsible for managing the bus 410 and for overall processing, including the execution of software stored in the non-temporary computer-readable medium 408. Once executed by the processor 406, the software can cause the processor 406 to perform various functions described below for any particular device or module. The non-temporary computer-readable medium 408 and the working memory device 404 can also be used to store data manipulated by the processor 406 when the software is executed.

[0055] One or more processors, such as the processor 406 of the vehicle module 400, can execute software. Software can be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, etc., whether they are called software, firmware, middleware, microcode, hardware description languages, etc. Software can reside on non-temporary computer-readable media, such as the non-temporary computer-readable media 408. Non-temporary computer-readable media 408 may include, for example, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, magnetic strips), optical discs (e.g., compact discs (CDs) or digital multipurpose discs (DVDs)), smart cards, flash memory devices (e.g., cards, sticks, or key drives), random access memory (RAM), read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), registers, removable disks, and other suitable non-temporary media for storing software, dates, and / or instructions that are accessed and read by the computer or processor 406. Computer-readable media may also include, for example, carriers, transmission lines, and any other suitable media for transmitting software and / or instructions that are accessed and read by the computer or processor 406. The non-temporary computer-readable medium 408 may reside in the vehicle module 400 (for example, the local data storage device 112 in Figure 1), or it may reside outside the vehicle module 400 (for example, the remote data storage device 142), or it may be distributed across multiple entities including the vehicle module 400.

[0056] The processor 406 is configured to acquire, process and / or transmit data, control access to and storage of data, issue commands, and control other desired operations. In at least one example, the processor 406 may include circuitry configured to implement desired programming provided by a suitable medium.

[0057] Non-temporary computer-readable media 408 may be embodied by a computer program product. For example, a computer program product may include computer-readable media in its packaging material. Those skilled in the art will recognize the best way to implement the functions described throughout this disclosure, depending on the specific application and the overall design constraints imposed on the entire system.

[0058] In some aspects of this disclosure, the processor 406 may include circuits configured for various functions. For example, the processor 406 may include circuits / modules 420 for operation, which manage the operation of sensors and displays, perform input / output operations related to accessing the Internet Web, and are configured to perform, for example, the methods described herein. For example, the processor 406 may include a data storage system / function / module / device 422 configured to store data, which includes, but is not limited to, sensory data, event data, threshold levels, video data, driver data, score data, and previously collected datasets. For example, the processor 406 may include a file system / function / module / device 424 configured to control how data in local data storage and / or remote data storage is stored and retrieved. For example, the processor 406 may include a sensor system / function / module / device 426 configured to control sensor inputs and video inputs. For example, processor 406 may include a diagnostic system / function / module / device 426 configured to handle email accounts, process email messages, consolidate emails for sending, acquire and store vehicle self-report data and recorded video, and perform, for example, the methods described herein. For example, processor 406 may include an engine control unit system / function / module / device 430 configured to control one or more electrical systems of subsystems within a vehicle to an external server, and perform, for example, the methods described herein. For example, processor 406 may include an artificial intelligence system / function / module / device 432 configured to build models of past use. For example, processor 406 may include an autonomous driving system / function / module / device 432 configured to determine whether the vehicle's autonomous driving system was activated during a dangerous event, and whether the driver activated the autonomous driving system or the vehicle activated the autonomous driving function.

[0059] In some aspects of this disclosure, the non-transient computer-readable medium 408 of the vehicle module 400 may include instructions that cause various systems / functions / modules / devices of the processor 406 to perform the methods described herein. For example, the non-transient computer-readable medium 408 may include operation instructions or code 420 to a circuit / module 420 for operation. For example, the non-transient computer-readable medium 408 may include a data storage instruction 436 corresponding to a data storage system / function / module / device 422. For example, the non-transient computer-readable medium 408 may include a file system instruction 438 corresponding to a file system / function / module / device 424. For example, the non-transient computer-readable medium 408 may include a sensor instruction 440 corresponding to a sensor system / function / module / device 426. For example, the non-transient computer-readable medium 408 may include a diagnostic instruction 442 corresponding to an engine control unit system / function / module / device 430. For example, the non-transient computer-readable medium 408 may include an engine control unit instruction 444 corresponding to an engine control unit system / function / module / device 430. For example, the non-temporary computer-readable medium 408 may contain artificial intelligence instructions 446 corresponding to an artificial intelligence system / function / module / device 432. For example, the non-temporary computer-readable medium 408 may contain autonomous driving instructions 446 corresponding to an autonomous driving system / function / module / device 433.

[0060] Figure 5 shows a block diagram of an exemplary hardware configuration of a data platform module / device 500 configured to communicate according to one or more aspects of the present disclosure. The data platform module 500 may include, for example, a communication interface 502. The communication interface 502 may enable data and control input and output. The communication interface 502 may enable communication over one or more communication networks, for example, one or more communication networks 102 in Figure 1. The communication interface 502 may be communicatively coupled to one or more communication networks 102, directly or indirectly. The data platform module 500 may include a working memory device 504 and a processor system / function / module / device (hereinafter, processor 506). The processor 506 may use the working memory device 504 to store data to be manipulated, data being manipulated, or recently manipulated data. The processor 506 may store instructions on the working memory device 504 and / or on one or more other memory structures or devices, such as a non-temporary computer-readable media system / function / module / device (hereinafter referred to as non-temporary computer-readable media 508). When the instructions are executed by the processor 506, they can cause the processor 506 to execute, for example, one or more aspects of the methods described herein.

[0061] The data platform module 500 may be implemented in a bus architecture generally represented by bus 510. Bus 510 may include any number of interconnection buses and bridges, depending on the specific application and overall design constraints of the data platform module 500. Bus 510 can communicatively connect various circuits, including one or more processors (generally represented by processor 506), a working memory device 504, a communication interface 502, and a non-temporary computer-readable medium 508. Bus 510 can also connect various other circuits and devices, such as timing sources, peripherals, voltage regulators, and power management circuits and devices. These are well known in the art and will not be described further.

[0062] The communication interface 502 provides means for communicating with other devices via a transmission medium. In some embodiments, the communication interface 502 includes circuitry and / or programming adapted to facilitate bidirectional communication of information with respect to one or more communication devices in a network. In some embodiments, the communication interface 502 is adapted to facilitate wireless communication of the data platform module 500. In these embodiments, the communication interface 502 can be coupled to one or more antennas 512 for wireless communication within a wireless communication system, as shown in Figure 5. In some embodiments, the communication interface 502 can be configured for wired-based communication. For example, the communication interface 502 may be a bus interface, a transmit / receive interface, or other type of signal interface including drivers, buffers, or other circuitry for outputting and / or acquiring signals (e.g., outputting signals from and / or receiving signals to an integrated circuit). The communication interface 502 may consist of one or more standalone receivers and / or transmitters, as well as one or more transceivers. In the illustrated example, the communication interface 502 includes a transmitter 514 and a receiver 516. The communication interface 502 functions as an example of a receiving means and / or a transmitting means.

[0063] The processor 506 can be responsible for managing the bus 510 and for overall processing, including the execution of software stored in the non-temporary computer-readable medium 508. Once executed by the processor 506, the software can cause the processor 506 to perform various functions described below for any particular device or module. The non-temporary computer-readable medium 508 and the working memory device 504 can also be used to store data manipulated by the processor 506 when the software is executed.

[0064] One or more processors, such as the processor 506 of the vehicle module 500, can execute software. Software can be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, etc., whether they are called software, firmware, middleware, microcode, hardware description languages, etc. Software can reside on non-temporary computer-readable media, such as non-temporary computer-readable media 508. Non-temporary computer-readable media 508 may include, for example, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, magnetic strips), optical discs (e.g., compact discs (CDs) or digital multipurpose discs (DVDs)), smart cards, flash memory devices (e.g., cards, sticks, or key drives), random access memory (RAM), read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), registers, removable disks, and other suitable non-temporary media for storing software, dates, and / or instructions that are accessed and read by a computer or processor 506. Computer-readable media may also include, for example, carriers, transmission lines, and any other suitable media for transmitting software and / or instructions that are accessed and read by a computer or processor 506. The non-temporary computer-readable medium 508 may reside in the data platform module 500 (for example, the local data storage device 112 in Figure 1), or it may reside outside the data platform module 500 (for example, a remote data storage device 142), or it may be distributed among multiple entities including the data platform module 500.

[0065] The processor 506 is configured to acquire, process and / or transmit data, control access to and storage of data, issue commands, and control other desired operations. In at least one example, the processor 506 may include circuitry configured to implement desired programming provided by a suitable medium.

[0066] The non-temporary computer-readable medium 508 may be embodied by a computer program product. For example, a computer program product may include computer-readable medium in its packaging material. Those skilled in the art will recognize the best way to implement the functions described throughout this disclosure, depending on the specific application and the overall design constraints imposed on the entire system.

[0067] In some aspects of this disclosure, the processor 506 may include circuits configured for various functions. For example, the processor 506 may include circuits / modules 520 for operation, which manage the operation of data received from the vehicle module 104 (Figure 1) and the user interface module 106 (Figure 1), perform input / output operations related to accessing the Internet Web, and perform, for example, the methods described herein. For example, the processor 506 may include a data storage system / function / module / device 522 configured to store data, which includes, but is not limited to, sensory data, event data, threshold levels, video data, driver data, score data, and previously collected datasets. For example, the processor 506 may include a file system / function / module / device 524 configured to control how data in local data storage and / or remote data storage is stored and retrieved. For example, the processor 506 may include a graphics processor system / function / module / device 526 configured to control video input and output from a camera mounted in the vehicle.

[0068] In some aspects of this disclosure, the non-temporary computer-readable medium 508 of the data platform module 500 may include instructions that cause various systems / functions / modules / devices of the processor 506 to perform the methods described herein. For example, the non-temporary computer-readable medium 508 may include operation instructions or code 528 for a circuit / module 520 for operation. For example, the non-temporary computer-readable medium 508 may include data storage instructions 530 corresponding to a data storage system / function / module / device 522. For example, the non-temporary computer-readable medium 508 may include file system instructions 532 corresponding to a file system / function / module / device 524. For example, the non-temporary computer-readable medium 508 may include graphics processor instructions 534 corresponding to a graphics processor system / function / module / device 526.

[0069] Figure 6 shows a block diagram of an exemplary hardware configuration of a user interface module / device 600 configured to communicate according to one or more aspects of the present disclosure. The user interface module 600 may include, for example, a communication interface 402. The communication interface 602 may enable data and control input and output. The communication interface 602 may enable communication over one or more communication networks, for example, one or more communication networks 102 in Figure 1. The communication interface 602 may be communicatively coupled to one or more communication networks 102, directly or indirectly. The user interface module 600 may include a working memory device 604 and a processor system / function / module / device (hereinafter, processor 606). The processor 606 may use the working memory device 604 to store data to be manipulated, data being manipulated, or recently manipulated data. The processor 606 may store instructions on the working memory device 604 and / or on one or more other memory structures or devices, such as a non-temporary computer-readable media system / function / module / device (hereinafter referred to as non-temporary computer-readable media 608). When the instructions are executed by the processor 606, they can cause the processor 606 to execute, for example, one or more aspects of the methods described herein.

[0070] The user interface module 600 may be implemented in a bus architecture commonly represented by bus 610. Bus 610 may include any number of interconnection buses and bridges, depending on the specific application and overall design constraints of the user interface module 600. Bus 610 can communicatively connect various circuits, including one or more processors (commonly represented by processor 606), a working memory device 604, a communication interface 602, and a non-temporary computer-readable medium 608. Bus 610 can also connect various other circuits and devices, such as timing sources, peripherals, voltage regulators, and power management circuits and devices. These are well known in the art and will not be described further.

[0071] The communication interface 602 provides means for communicating with other devices via a transmission medium. In some embodiments, the communication interface 602 includes circuitry and / or programming adapted to facilitate bidirectional communication of information with respect to one or more communication devices in a network. In some embodiments, the communication interface 602 is adapted to facilitate wireless communication of the user interface module 600. In these embodiments, the communication interface 602 can be coupled to one or more antennas 612 for wireless communication within a wireless communication system, as shown in Figure 6. In some embodiments, the communication interface 602 can be configured for wired-based communication. For example, the communication interface 602 may be a bus interface, a transmit / receive interface, or other type of signal interface including drivers, buffers, or other circuitry for outputting and / or acquiring signals (e.g., outputting signals from and / or receiving signals to an integrated circuit). The communication interface 602 may consist of one or more standalone receivers and / or transmitters, as well as one or more transceivers. In the illustrated example, the communication interface 602 includes a transmitter 614 and a receiver 616. The communication interface 602 functions as an example of a receiving means and / or a transmitting means.

[0072] The processor 606 can be responsible for managing the bus 610 and for overall processing, including the execution of software stored in the non-temporary computer-readable medium 608. Once executed by the processor 606, the software can cause the processor 606 to perform various functions described below for any particular device or module. The non-temporary computer-readable medium 608 and the working memory device 404 can also be used to store data manipulated by the processor 606 when the software is executed.

[0073] One or more processors, such as the processor 606 of the user interface module 600, can execute software. Software can be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, etc., whether they are called software, firmware, middleware, microcode, hardware description languages, etc. Software can reside on non-temporary computer-readable media, such as non-temporary computer-readable media 608. Non-temporary computer-readable media 608 may include, for example, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, magnetic strips), optical discs (e.g., compact discs (CDs) or digital multipurpose discs (DVDs)), smart cards, flash memory devices (e.g., cards, sticks, or key drives), random access memory (RAM), read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), registers, removable disks, and other suitable non-temporary media for storing software, dates, and / or instructions that are accessed and read by a computer or processor 606. Computer-readable media may also include, for example, carrier waves, transmission lines, and any other suitable media for transmitting software and / or instructions that are accessed and read by a computer or processor 606. The non-temporary computer-readable medium 608 may reside in the user interface module 600 (for example, the local data storage device 112 in Figure 1), or it may reside outside the user interface module 600 (for example, the remote data storage device 142), or it may be distributed among multiple entities including the user interface module 600.

[0074] The processor 606 is configured to acquire, process and / or transmit data, control access to and storage of data, issue commands, and control other desired operations. In at least one example, the processor 606 may include circuitry configured to implement desired programming provided by a suitable medium.

[0075] The non-temporary computer-readable medium 608 may be embodied by a computer program product. For example, a computer program product may include computer-readable medium in its packaging material. Those skilled in the art will recognize the best way to implement the functions described throughout this disclosure, depending on the specific application and the overall design constraints imposed on the entire system.

[0076] In some aspects of this disclosure, the processor 606 may include circuits configured for various functions. For example, the processor 606 may include circuits / modules 620 for operation, which manage the operation of data received from the vehicle module 104 (Figure 1) and the data platform module 108 (Figure 1), perform input / output operations related to accessing the Internet Web, and be configured to perform, for example, the methods described herein. For example, the processor 606 may include a data storage 622 system / function / module / device configured to store data, which includes, but is not limited to, sensory data, event data, threshold levels, video data, driver data, score data, and previously collected datasets. For example, the processor 606 may include a file system / function / module / device 624 configured to control how data in local data storage and / or remote data storage is stored and retrieved. For example, the processor 606 may include a vehicle HUD system / function / module / device 626 configured to provide video input and output. For example, the processor 606 may include a vehicle HUD system / function / module / device 626 configured to control an automotive head-up display on a vehicle.

[0077] In some aspects of this disclosure, the non-transient computer-readable medium 608 of the user interface module 600 may include instructions that cause various systems / functions / modules / devices of the processor 606 to perform the methods described herein. For example, the non-transient computer-readable medium 608 may include operation instructions or code 630 for circuits / modules 620 for operation. For example, the non-transient computer-readable medium 608 may include data storage instructions 632 corresponding to a data storage system / function / module / device 622. For example, the non-transient computer-readable medium 608 may include file system instructions 634 corresponding to a file system / function / module / device 624. For example, the non-transient computer-readable medium 608 may include vehicle HUD instructions 636 corresponding to a vehicle HUD system / function / module / device 626. For example, the non-transient computer-readable medium 608 may include dashboard instructions 638 corresponding to a vehicle HUD system / function / module / device 626.

[0078] Figures 7 and 8 illustrate an exemplary process for collecting data from a combination of vehicle sensors, video inputs, and onboard artificial intelligence and / or machine learning modules, and for generating, calculating, and evaluating driving scores and trip information from the data for the driver of a vehicle when the driver is a human, or for the driver of a vehicle when the driver is an autonomous driving function activated. The process can also transmit the calculation results to one or more entities, or enable one or more entities to retrieve the calculation results. Each process is shown as a collection of blocks in a logical flowchart that represents a set of actions that can be performed in hardware, software, or a combination thereof. In the case of software, the blocks represent computer executable instructions that, when executed by one or more processors, perform the described actions. Generally, computer executable instructions include routines, programs, objects, components, and data structures that perform a particular function or implement a particular abstract data type. The order in which the actions are described is not intended to be interpreted as limiting, and any number of the described blocks can be combined in any order and / or mirrored to implement the process. For illustrative purposes, the processes described herein will be described with reference to Architecture 100 in Figures 1 to 3.

[0079] Figure 7 is a flowchart illustrating an exemplary method 700 for calculating the driver score of a vehicle's driver. As described above, the driver may be a person or, if the autonomous driving function is activated, the vehicle itself. The execution of the driver score calculation process 700 may begin when the vehicle's engine is started and the trip begins (702). As the vehicle progresses through the trip, the vehicle module installed in the vehicle collects or takes data from sensors, diagnostic modules, engine control units and / or autonomous driving modules (704). The vehicle module continuously monitors for hazardous events (706). If no hazardous events are detected (708), the vehicle module determines whether the trip has ended or not (710).

[0080] If a hazardous event is detected (712), the vehicle module initiates recording of video and / or vehicle data for onboard and offboard analysis for vehicle threshold events to indicate an abnormal situation (714). For example, the vehicle module may use a 5-10 second window to determine the peak of the hazardous event. For instance, if the driver suddenly brakes, the vehicle module can check the corresponding sensor data over a 10-second timeframe to determine the time of maximum deceleration. If the vehicle module determines that a hazardous event has occurred, a severity level, for example, 0-3 (or 0-4), is assigned, and the trip summary is updated. The trip summary may be stored in the vehicle module installed in the vehicle, as well as sent to a remote database or server at the start and end of the trip, and every 10 seconds. All severity levels of hazardous events detected during the trip can be used to calculate the trip severity, which can be used to evaluate the trip score. For example, the system can automatically assume a trip severity of 4 for each minute of a trip if the trip score is a number between 0 and 100, taking into account the typical severity of the trip (or a pre-set severity or threshold) and the actual severity for each minute. Table 1 below shows an example of severity associated with a hazardous event. Table 1 is intended to be illustrative and non-limiting and may include other hazardous events and severity levels. TIFF2026090325000002.tif116170

[0081] When an event is detected, the system can use 60 seconds of video data to save 10 seconds of video before and after the event's timestamp. The vehicle module then analyzes the recorded content and uses an object detection algorithm to draw a box around each recognized object. After creating the object boundary boxes, the vehicle module sends the box coordinates to the processor for processing of the hazardous event. In other words, the system can identify external objects that affected the driver's driving performance. The boxes identify external objects from the recorded video that affected the performance data using the object detection algorithm. The latitude and longitude coordinates of the identified objects are obtained and may be sent to the diagnostic module processor for further processing to determine the impact on driver performance.

[0082] Based on all data collected from the trip, the system calculates a trip-based driver scoring for a cumulative overall driver score (716). Next, based on the data collection and analysis, the system uses artificial intelligence and / or machine learning to calculate the collected vehicle sensor and / or video metadata and develop a driving score that includes risk and safety scores (718). If no further dangerous events are detected and the system determines that the trip has ended (720), the system stops collecting data.

[0083] Figure 8 is a flowchart illustrating an exemplary method 800 for determining driver and vehicle safety. The execution of the safety score calculation process 800 begins with collecting, gathering, and / or detecting data during the trip. As described above, the driver may be a person or the vehicle itself if the autonomous driving function is activated. Diagnostic data is collected (802), from which it is determined whether trouble codes occurred during the trip (804), hazardous event data is identified (806), and severity levels are assigned to the hazardous events (808). Driving data is also collected (810), from which it is determined whether abnormal behavior was detected (812), hazardous event data is identified (814), and severity levels are assigned to the hazardous events (816). Using the collected data, detected trouble codes, identified hazardous events, and assigned severity levels, the system calculates an intermediate score as the trip progresses (818). When the trip ends (820), a trip severity level is assigned (822), and a trip score is calculated (824). The driver score is calculated using the trip severity level and the trip score (826).

[0084] Factors that can be analyzed when determining the trip score include, but are not limited to, observed driver behavior, available vehicle safety systems, vehicle maintenance level, and mileage and time traveled.

[0085] According to one aspect of this disclosure, when the system determines an anomaly of sufficient severity, it can contact and dispatch emergency response personnel and tow trucks, and can also assist people involved in selecting medical and vehicle repair facilities. The system can track the vehicle's position and orientation. If an inertial measurement unit (IMU) is installed, the system can process IMU data separately from GPS samples, thereby providing more detailed vehicle information. The system can acquire vehicle information from GPS and IMU every second, including longitude, latitude, altitude, speed, direction, longitude error (meters), latitude error (meters), speed error (meters / second), altitude error (meters), and direction error (degrees).

[0086] The GPS module then processes the GPS data using an unscented Kalman localization filter, which helps improve the accuracy and precision of the information the system receives. The localization filter itself estimates the vehicle's position based on speed and direction and compares it with the information received from the GPS. In areas with poor reception, the system can rely more on the localization filter's predictions than on the GPS data.

[0087] Figure 9 is a diagram summarizing the overall process of the insurance system related to insurance. It shows four main processes that take place when an incident is detected. These main processes are a) detection of the accident and other anomalies, b) reconstruction and explanation of the anomaly, c) attribution of the cost and fault of the incident, and d) subrogation and payment.

[0088] Figure 10 illustrates the process for detecting accidents or anomalies, i.e., incidents. While driving, an allocated amount of data storage is ensured to hold one minute's worth of vehicle sensor information. When an accident or anomaly is detected, alerts are issued to surrounding vehicles and devices that may have witnessed the incident. Data from vehicles and devices within range that may have witnessed the incident is sent to the insurance system's data platform for further analysis.

[0089] Figure 11 illustrates the process of using data from multiple device sensors to improve the accuracy of estimating the position and movement of vehicles and objects at the time of a detected incident. This figure also shows the process of reconstructing the activity of detected objects using device and vehicle sensor data, including predicting the path of the detected object and indicating actions that may have led to the detected anomaly / accident. As a result of this process, an incident report and event log of the activity that occurred are created.

[0090] Figure 12 illustrates how fault is assigned to the relevant entities using an incident report. A model trained on past insurance claim results based on anomaly type is run on the incident report. This model assigns fault to objects and entities detected at the incident location, along with potential faults that may be caused by parts manufacturers or vehicle software manufacturers, although these are not limited to them. After fault is assigned, the cost is estimated from the model trained on past insurance claim information. If the driver or passenger takes a photo of the incident with their mobile phone, that photo can be uploaded to the insurance system's data platform to run the cost estimation model trained on the incident image. Based on the estimated cost and the accuracy of the cost estimation, a decision can be made to automatically make a payment to the insured or to require approval from a human agent.

[0091] Figure 13 illustrates a process that reduces the amount of manual work involved in subrogating claims and payments to parties. First, using data generated from the incident, an identification model is run on the detected object, and vehicle license plate information is detected to retrieve the parties' insurance companies from a remote database. If this information alone is insufficient to identify the parties, individuals can take a picture of their driver's license with their mobile phones. The driver's license image is then used to extract information about the parties by a model trained to detect written letters and numbers. Subsequently, based on the output of past fault models, the identified parties and their insurance companies are notified of their percentage of fault. Based on the assigned percentage of fault, payments are subrogated, and the parties receive their insurance payouts.

[0092] The above system can store accident data on the vehicle and on the device.

[0093] The above system can be used to coordinate the calculation of hazardous features and anomaly detection for available sensors and / or data inputs.

[0094] The above system can be used for driver and vehicle risk estimation and safety assessment for fleet management.

[0095] The above system can be used to transmit accident data to emergency response services via mobile phone and / or other network connections after an accident.

[0096] The above system can be used to estimate vehicle repairs, insurance claims, and personal injury accidents from accident data.

[0097] The above system can be used for driver and / or vehicle safety analysis.

[0098] The system described above can be applied to both the financial and insurance industries. It can be used to calculate the residual value of leases and loans. The generated risk calculations and estimates can be used to estimate the residual value of a vehicle by identifying its movement and mileage. The residual value can then be used to estimate lease prices, fleet management sales, vehicle prices, auction prices, and total loss insurance valuations. The estimated driver score and trip summary can be used to calculate the cost per mile traveled, changes in driver insurance premiums, and vehicle wear and tear.

[0099] knot In this disclosure, the term “exemplary” is used to mean “serving as an example, case, or illustration.” Any aspect or feature described herein as “exemplary” should not necessarily be construed as being preferable or advantageous to other aspects of this disclosure. Similarly, the term “aspect” does not require that all aspects of this disclosure include the described features, advantages, or modes of operation. In this specification, the term “bonded” is used to refer to a direct or indirect bond between two objects. For example, if object A is in physical contact with object B, and object B is in contact with object C, then objects A and C can be considered bonded to each other even if they are not in direct physical contact. For example, an object may be bonded to a second object even if the first object is not in direct physical contact with the second object. The terms “circuit” and “electrical circuit” are used broadly and are intended to include both hardware implementations of electrical devices and conductors that, when connected and configured, enable the performance of the functions described herein, and software implementations of information and instructions that, when executed by a processor, enable the performance of the functions described herein, without being limited to types of electronic circuits. "At least one" and "one or more" may be used interchangeably herein.

[0100] In this disclosure, the use of the component “A and / or B” may mean “A or B or A and B,” and alternatively, it may be expressed as “A, B, or a combination thereof” or “A, B, or both.” In this disclosure, the use of the component “A, B, and / or C” may mean “A or B or C, or any combination thereof,” and alternatively, it may be expressed as “A, B, C, or any combination thereof.”

[0101] One or more of the components, steps, features, and / or functions described herein can be rearranged and / or combined into a single component, step, feature, or function, or embodied in multiple components, steps, or functions. Additional elements, components, steps, and / or functions can also be added without departing from the novel features disclosed herein. Apparatus, devices, and / or components described herein can be configured to perform one or more of the methods, features, or steps described herein. Furthermore, the novel algorithms described herein can be efficiently implemented in software and / or incorporated into hardware.

[0102] It should be understood that the specific order or hierarchy of steps in the disclosed method is an example of an exemplary process. It should be understood that the specific order or hierarchy of steps in the method may be rearranged based on design desirability. The claims of the attached method present elements of various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy presented unless specifically stated therein.

[0103] The above description is provided to enable a person skilled in the art to carry out the various embodiments described herein. Various modifications to these embodiments will be obvious to a person skilled in the art, and the general principles set forth herein can be applied to other embodiments. For this reason, the claims are not intended to be limited to the embodiments shown herein, but the entire scope consistent with the language of the claims is recognized, and references to singular elements shall mean "one or more" and not "only" unless otherwise specified. Unless otherwise specified, the term "several" refers to one or more. The phrase "at least one of" in a list of items refers to any combination of those items, including a single member. For example, "at least one of a, b, or c" is intended to include a;b;c;a and b;a and c;b and c;a, b and c. All structural and functional equivalents to elements of the various embodiments described throughout this disclosure, known to or to a person skilled in the art, are expressly incorporated herein by reference and are intended to be included in the claims. Furthermore, nothing disclosed herein is intended to be made available to the public, whether such disclosure is expressly contained in the claims or not. Unless an element of a claim is expressly described using the phrase “means for,” or, in the case of a method claim, “step for,” no element of a claim should be construed under Section 112(f) of the United States Patent Act.

[0104] In this specification, the term “specify” encompasses a wide range of actions. For example, “specify” can include calculating, performing calculations, processing, deriving, investigating, searching (e.g., searching in tables, databases or other data structures), and confirming. It can also include receiving (e.g., receiving information), accessing (e.g., accessing data in memory), and seeking, selecting, choosing, and establishing.

[0105] While the above disclosures illustrate exemplary embodiments, it should be noted that various changes and modifications are possible herein without departing from the scope of the attached claims. Furthermore, the functions, steps, or operations claimed in the methods relating to embodiments described herein do not need to be performed in a specific order unless otherwise specified. Additionally, while elements may be described or claimed in the singular form, the plural form is intended unless explicitly stated to be limited to the singular form.

Claims

1. One or more non-temporary computer-readable media for storing computer executable instructions, The aforementioned computer executable instruction causes one or more processors to perform an action during execution, and the said action is The steps include receiving performance data related to the vehicle driver's performance from multiple sources, A step of identifying at least one dangerous event that affects the driver's performance, wherein the at least one dangerous event has an adverse effect such that the performance data falls below a predetermined threshold; A step of analyzing performance data using a trained machine learning model and determining the severity level of each identified hazardous event that affects driver performance, wherein the trained machine learning model employs multiple types of machine learning algorithms to analyze the performance data. The steps include generating at least one score based on performance data for presentation via a user interface, A step of improving a machine learning model based on at least one generated score, wherein the improvement includes retraining the machine learning model based on at least one training corpus modified based on performance data. One or more non-temporary computer-readable media characterized by including the following:

2. In one or more non-temporary computer-readable media according to claim 1, One or more non-temporary computer-readable media, characterized in that the operation further includes the step of notifying at least one user of the at least one score via the user interface.

3. In one or more non-temporary computer-readable media according to claim 1, One or more non-temporary computer-readable media, characterized in that the performance data is selected from at least one of sensor data, data from a diagnostic module, data from an engine control unit module, and data from an autonomous driving module.

4. In one or more non-temporary computer-readable media according to claim 1, The aforementioned operation, The steps include: starting the recording of video data using a camera mounted on the vehicle, One or more non-temporary computer-readable media, further comprising the step of analyzing recorded video data using vehicle threshold events to identify at least one of the dangerous events.

5. In one or more non-temporary computer-readable media according to claim 4, One or more non-temporary computer-readable media, characterized in that a memory device mounted on a vehicle stores 10 seconds of video footage recorded before and after at least one of the hazardous events.

6. In one or more non-temporary computer-readable media according to claim 4, The aforementioned operation, The process involves using an object detection algorithm to identify external objects from the recorded video that could affect performance data, and One or more non-temporary computer-readable media, further comprising the step of transmitting the latitude and longitude coordinates of an identified object to a diagnostic module processor to perform further processing to determine the object's impact on at least one hazardous event.

7. In one or more non-temporary computer-readable media according to claim 1, The aforementioned operation, The steps include: initiating the saving of vehicle data from the diagnostic module, engine control unit module, and autonomous driving module; One or more non-temporary computer-readable media, further comprising the step of analyzing vehicle threshold events in stored vehicle data to identify at least one of the dangerous events.

8. In one or more non-temporary computer-readable media according to claim 1, The aforementioned operation, Steps to determine the occurrence of an accident from performance data, One or more non-temporary computer-readable media, further comprising the step of transmitting information related to the accident to a third party.

9. In one or more non-temporary computer-readable media according to claim 1, One or more non-temporary computer-readable media characterized in that the at least one score is generated on the vehicle.

10. In one or more non-temporary computer-readable media according to claim 1, The aforementioned operation, The steps include updating the score of the at least one dangerous event after a severity level has been assigned to it, The steps include storing the updated score in the vehicle's onboard memory device, One or more non-temporary computer-readable media, further comprising the step of transmitting the updated score to a remote data database.

11. In one or more non-temporary computer-readable media according to claim 10, One or more non-temporary computer-readable media, characterized in that the at least one score is a driver score and a trip score.

12. A method that is performed on a computer, The process involves a step in which the processor of a vehicle module installed in the vehicle receives performance data regarding the driver's performance from multiple sources installed in the vehicle, A step of identifying at least one dangerous event affecting the driver's performance using the processor of the vehicle module, wherein the at least one dangerous event has an adverse effect such that the performance data falls below a preset threshold. A step of analyzing performance data using a trained machine learning model in the processor of the vehicle module and determining the severity level of each identified hazardous event affecting driver performance, wherein the trained machine learning model employs multiple types of machine learning algorithms to analyze the performance data. The processor of the vehicle module generates at least one score based on performance data for presentation via a user interface. A method performed on a computer, comprising the steps of: improving a machine learning model based on at least one score generated by the processor of the vehicle module, wherein the improvement includes retraining the machine learning model based on at least one training corpus modified based on performance data.

13. In the method performed by a computer according to claim 1, The processor initiates the recording of video data using a camera mounted on the vehicle. A computer-based method characterized in that the processor further comprises the step of analyzing the recorded video data using vehicle threshold events to identify at least one risk.

14. In the computer-based method described in claim 13, A diagnostic module communicating with a module mounted on the vehicle uses an object detection algorithm to identify external objects from recorded video that may affect performance data. A computer-based method further comprising the steps of transmitting the latitude and longitude coordinates of an identified object to a diagnostic module processor to perform further processing to determine the object's impact on at least one hazardous event.

15. In the computer-based method described in claim 13, The processor initiates the saving of vehicle data from the diagnostic module, engine control unit module, and autonomous driving module installed in the vehicle. A method performed on a computer, further comprising the step of the processor analyzing vehicle threshold events in stored vehicle data to identify at least one dangerous event.

16. A computing device installed in a vehicle, Interface and The interface is coupled to a processing circuit, and the processing circuit is The steps include receiving performance data related to the vehicle driver's performance from multiple sources, A step of identifying at least one dangerous event that affects the driver's performance, wherein the at least one dangerous event has an adverse effect such that the performance data falls below a predetermined threshold; A step of analyzing performance data using a trained machine learning model and determining the severity level of each identified hazardous event that affects driver performance, wherein the trained machine learning model employs multiple types of machine learning algorithms to analyze the performance data. The steps include generating at least one score based on performance data for presentation to the user, A step of improving a machine learning model based on at least one generated score, wherein the improvement includes retraining the machine learning model based on at least one training corpus modified based on performance data. A computing device characterized by being configured to perform the following.

17. In the computing device according to claim 16, The processing circuit described above Start recording video data using the camera mounted on the vehicle. A computing device further configured to analyze recorded video data using vehicle threshold events and to identify at least one of the aforementioned dangerous events.

18. In the computing device according to claim 16, The processing circuit described above Using an object detection algorithm, external objects affecting performance data are identified from recorded video footage. A computing device characterized by being configured to transmit the latitude and longitude coordinates of an identified object to a diagnostic module processor to perform further processing to determine the object's impact on at least one hazardous event.

19. In the computing device according to claim 16, The processing circuit described above Start saving vehicle data from the diagnostic module, engine control unit module, and autonomous driving module. A computing device further configured to analyze vehicle threshold events in stored vehicle data to identify at least one of the dangerous events.

20. In the computing device according to claim 16, The processing circuit described above Based on the aforementioned performance data, the occurrence of an accident is determined. A computing device further configured to transmit information related to the aforementioned accident to a third party.

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