Vehicle operation risk assessment method and system

Abstract

The invention discloses a vehicle operation risk assessment method, and the method comprises the steps: collecting the multi-source information of a vehicle, and the multi-source information at least comprises vehicle operation data, driver state data, auxiliary driving system state data, and driving environment data; determining a current first risk assessment value of the vehicle based on the multi-source information, wherein the first risk assessment value represents at least one of a driver control risk, an auxiliary driving control risk and a vehicle operation environment risk; comparing the first risk assessment value with a corresponding risk threshold value; and if the risk level corresponding to the comparison result meets a risk prompt condition, outputting a risk prompt corresponding to the risk level. Meanwhile, the invention also discloses a vehicle operation risk assessment system.

Description

Technical Field

[0001] The present application relates to the field of vehicle driving, and specifically to a vehicle operation risk assessment method and system. Background Art

[0002] Existing driver assistance systems primarily operate in the risk avoidance phase. If they can detect vehicle anomalies early, they can issue warnings at the risk exposure stage, providing the driver with ample time to nip accidents in the bud and providing an early assessment of vehicle safety.

[0003] Current research on traffic safety risk assessment lacks a vehicle operation risk assessment method that considers multiple risk factors. Therefore, developing a vehicle operation risk assessment method that considers multiple risk factors is of great significance. Research on vehicle safety assessment methods is crucial for enhancing the core competitiveness of intelligent connected vehicles, fostering growth areas for my country's automotive industry, and assisting in accident investigation. Summary of the Invention

[0004] In view of this, the embodiments of the present application hope to provide a vehicle operation risk assessment method and system to at least solve the above-mentioned technical problems.

[0005] To achieve the above objectives, the technical solution of this application is implemented as follows:

[0006] According to one aspect of an embodiment of the present application, a vehicle operation risk assessment method is provided, the method comprising:

[0007] Collecting multi-source information of the vehicle, the multi-source information including at least vehicle operation data, driver status data, auxiliary driving system status data, and driving environment data;

[0008] determining a current first risk assessment value of the vehicle based on the multi-source information, the first risk assessment value representing at least one of a driver control risk, an assisted driving control risk, and a vehicle operating environment risk;

[0009] comparing the first risk assessment value with a corresponding risk threshold;

[0010] If the risk level corresponding to the comparison result meets the risk prompt condition, a risk prompt corresponding to the risk level is output.

[0011] In the above solution, determining the current first risk assessment value of the vehicle based on the multi-source information includes:

[0012] The multi-source information is input into a safety risk assessment model, and one or more current first risk assessment values ​​of the vehicle are obtained based on the safety risk assessment model.

[0013] In the above solution, comparing the first risk assessment value with the corresponding risk threshold value includes:

[0014] directly comparing each of the first risk assessment values ​​with a corresponding risk threshold to obtain a current risk level of the vehicle;

[0015] Alternatively, a plurality of the first risk assessment values ​​are weighted to obtain a second risk assessment value; and the second risk assessment value is compared with a corresponding risk threshold to obtain the current risk level of the vehicle.

[0016] In the above solution, before performing weighted processing on the plurality of first risk assessment values, the method further includes:

[0017] determining a current driving mode of the vehicle, the driving mode indicating whether the vehicle is currently in manual driving or assisted driving;

[0018] assigning a weight coefficient to each first risk assessment value based on the driving mode;

[0019] A weighted processing is performed on the plurality of first risk assessment values ​​based on the weight coefficient; wherein the weight coefficient changes according to a change curve of each first risk assessment value within a preset time period.

[0020] In the above solution, the risk level corresponding to the comparison result satisfies the risk warning conditions, including:

[0021] If the threshold range corresponding to the comparison result indicates a high risk, determining that the risk level corresponding to the comparison result meets a risk warning condition;

[0022] Alternatively, if the threshold range corresponding to the comparison result indicates a critical risk, determining that the risk level corresponding to the comparison result meets a risk warning condition;

[0023] The critical risk level is higher than the high risk level.

[0024] In the above solution, the outputting of the risk warning corresponding to the risk level includes:

[0025] If the risk level corresponding to the comparison result is high risk, the steering wheel of the vehicle is triggered to vibrate slightly and the instrument panel lights flash;

[0026] If the risk level corresponding to the comparison result is a critical risk, the vehicle's seat belt pre-tightening, steering wheel micro-vibration, instrument panel lights flashing, and voice reminders are triggered in the vehicle.

[0027] In the above solution, if the risk level corresponding to the comparison result meets the risk warning condition, the method further includes:

[0028] performing a data storage event; wherein the data storage event comprises at least storage of the multi-source information, storage of the first risk assessment value, storage of the second risk assessment value, and storage of the risk level.

[0029] In the above solution, the performing of the data storage event comprises:

[0030] counting a storage number of historical data storage events in the vehicle;

[0031] if the storage number is less than a number threshold, directly performing the data storage event;

[0032] if the storage number is greater than or equal to the number threshold, replacing the earliest historical data storage event in the vehicle with the data storage event.

[0033] In the above solution, the method further comprises:

[0034] if an accident event occurs in the vehicle, enabling a data interface access permission to output the data storage event and / or historical data storage events of the vehicle to a traffic management platform through the data interface, so that the traffic management platform analyzes the accident event based on the data storage event and / or the historical data storage events of the vehicle.

[0035] According to another aspect of the present application, a vehicle operation risk assessment system is provided, characterized in that the system comprises:

[0036] a data collection module configured to collect multi-source information of a vehicle, the multi-source information comprising at least vehicle operation data, driver state data, and auxiliary driving system state data;

[0037] a data processing module configured to determine a first risk assessment value of the vehicle based on the multi-source information, the first risk assessment value representing at least one of a driver control risk, an auxiliary driving control risk, and a vehicle operation environment risk, and to compare the first risk assessment value with a corresponding risk threshold value;

[0038] an output module configured to output a risk prompt corresponding to a risk level if the risk level meets a risk prompt condition.

[0039] The vehicle operation risk assessment method and system provided by the application is a scheme for collecting various factors directly related to vehicle operation risk to assess the vehicle operation risk. Specifically, the application collects multi-source information of the vehicle, and the multi-source information at least includes vehicle operation data, driver state data, auxiliary driving system state data, and driving environment data; determines a first risk assessment value of the vehicle based on the multi-source information, and the first risk assessment value represents at least one of driver control risk, auxiliary driving control risk, and vehicle operation environment risk; compares the first risk assessment value with a corresponding risk threshold value; and outputs a risk prompt corresponding to the risk level if the risk level meets a risk prompt condition. As can be seen, the application considers comprehensive data directly related to the state of the driver, the state of the vehicle operation, the auxiliary driving system, and the driving environment, which can fully reflect the complexity of the actual driving environment, not only can make the risk assessment result more accurate, improve the robustness of the system, but also can provide favorable data support for accident investigation in the case of vehicle accident, and improve the accident handling efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 A flow implementation schematic diagram of the vehicle operation risk assessment method in the application;

[0041] Figure 2 A structure composition schematic diagram of the vehicle operation risk assessment system in the application;

[0042] Figure 3 A structure composition schematic diagram of the vehicle in the application. DETAILED DESCRIPTION

[0043] The technical scheme of the application will be further described in detail below in combination with the drawings and specific embodiments.

[0044] In the specific embodiments, various specific technical features in each of the embodiments described in the specific embodiments can be combined in various combinations without contradiction, for example, different embodiments can be formed by combining different specific technical features. In order to avoid unnecessary repetition, various possible combinations of each specific technical feature in the application are not described again.

[0045] It should be noted that the terms "first", "second", and "third" in the embodiments of the application are only to distinguish similar objects, and do not represent a specific order of the objects. It can be understood that "first", "second", and "third" can be interchanged in a specific order or sequence as appropriate. It should be understood that the objects distinguished by "first", "second", and "third" can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein.

[0046] Figure 1 This is a schematic diagram of the process implementation of the vehicle operation risk assessment method in this application, such as Figure 1 As shown, the method includes:

[0047] Step 101: Collect multi-source information of the vehicle, wherein the multi-source information includes at least vehicle operation data, driver status data, auxiliary driving system status data, and driving environment data;

[0048] During vehicle operation, vehicle operation data can be collected through onboard sensors, high-precision maps, and cloud-based databases. Onboard eye trackers and heart rate monitors can be used to collect information such as the driver's gaze deviation duration, glance speed, blink rate, heart rate, facial expressions, stress, and steering wheel grip. Eye tracker cameras and heart rate monitors can be mounted on the steering wheel and rearview mirror, as long as they can capture information such as the driver's gaze deviation duration, glance speed, blink rate, heart rate, facial expressions, stress, and steering wheel grip. Rangefinders can be mounted on the front windshield and side door frames to collect distance data between the current vehicle and the vehicle ahead, as well as the current vehicle and the vehicle to the side. By activating the assisted driving system's assisted driving function, sensor target recognition data, design operating domain (ODD) detection data, driver departure warning data, and the time delay between perception decisions and control execution can be collected. Roadside sensors or onboard sensors can also collect driving environment data such as traffic density, weather conditions, road slope, and road surface conditions.

[0049] Here, the vehicle-mounted sensors include, but are not limited to, radar sensors, camera sensors, lidar sensors, and inertial measurement units. These sensors can collect information about the vehicle's longitudinal / lateral speed, acceleration, accelerator pedal opening, brake pedal opening, steering wheel angle, and steering direction.

[0050] Static environmental information such as road topology can be obtained through this high-precision map.

[0051] Through this cloud database, historical risk indicator data for the same road section, historical user takeover delay statistics, accident density and other historical risk data can be obtained.

[0052] Step 102 , determining a current first risk assessment value of the vehicle based on the multi-source information, where the first risk assessment value represents at least one of a driver control risk, an assisted driving control risk, and a vehicle operating environment risk;

[0053] Here, when the vehicle collects multi-source information, it can first pre-process and clean the multi-source information to remove duplicate, missing, and erroneous data points in the multi-source information, then standardize the formats of different data sources, and finally use data visualization tools to identify outliers in the data, add timestamps, and align the data.

[0054] Here, data alignment refers to adding a timestamp to each data point to record the precise time of data collection, and finally mapping the data of multi-source information to the same reference time base according to the timestamp. For data points that do not match in time, interpolation or extrapolation methods are used for processing.

[0055] In the present application, after preprocessing of the collected multi-source information is completed, the multi-source information can also be input into a safety risk assessment model, and one or more current first risk assessment values ​​of the vehicle can be obtained based on the safety risk assessment model.

[0056] Here, the first risk assessment value represents at least one of a driver control risk, an assisted driving control risk, and a vehicle operating environment risk.

[0057] The driver control risk includes driver fatigue and distracted driving risks. The main assessment sub-indicators include the driver's heart rate, yawn frequency, the angle and duration of vision deviation from the road, blink frequency, steering wheel grip and stability, frequency and amplitude of sudden steering wheel angle changes, and the driver's response time after the assisted driving system issues a takeover request;

[0058] The assisted driving control risk includes perception risk and decision-making control risk. The main evaluation sub-indicators include the differences in the recognition time and results of different sensors for the same target, the proximity and degree of exceeding the boundary of the designed operating domain of the assisted driving system, and the frequency and amplitude of sudden acceleration and deceleration commands issued by the assisted driving system;

[0059] The vehicle operating environment risk includes current risk and historical risk. The main evaluation sub-indicators include the relative distance to surrounding vehicles and minimum conflict time, lane line wear, potholes and slippery road conditions, weather and lighting, as well as historical risk indicator data of the road section, user historical takeover delay statistics, and accident density.

[0060] Step 103: Compare the first risk assessment value with a corresponding risk threshold;

[0061] In an implementation of the present application, when the vehicle obtains the first risk assessment value, each first risk assessment value may be directly compared with a corresponding risk threshold to obtain the current risk level of the vehicle.

[0062] For example, the first risk assessment may contain three values: driver control risk: 0.5, assisted driving control risk: 0.5, and vehicle operating environment risk: 0.5. The driver control risk: 0.5 is then compared with the driver control risk threshold; the assisted driving control risk: 0.5 is compared with the assisted driving control risk threshold; and the vehicle operating environment risk: 0.5 is compared with the vehicle operating environment risk. This is simple and convenient.

[0063] In another implementation of the present application, multiple first risk assessment values ​​may be weighted to obtain a second risk assessment value; and the second risk assessment value may be compared with a corresponding risk threshold to obtain the current risk level of the vehicle.

[0064] The weighted calculation formula is:

[0065] CSRI(t)=DR(t)*R_dri(t)+SR(t)*R_sys(t)+SE(t)*R_env(t)

[0066] Among them, CSRI(t) represents the comprehensive safety risk value calculated at time t, that is, the second risk assessment value; DR(t), SR(t), and SE(t) represent the weight coefficients of driver control risk, assisted driving control risk, and vehicle operating environment risk at time t, respectively. R_dri(t) represents the driver control risk assessment value at time t, R_sys(t) represents the assisted driving control risk assessment value at time t, and R_env(t) represents the vehicle operating environment risk assessment value at time t. In the formula, DR(t)+SR(t)+SE(t)=10 needs to be satisfied.

[0067] Here, before weighting the multiple first risk assessment values, it is also necessary to determine the current driving mode of the vehicle, which characterizes whether the vehicle is currently in manual driving or assisted driving; then, based on the driving mode, a weight coefficient is assigned to each first risk assessment value; and based on the weight coefficient, the multiple first risk assessment values ​​are weighted; wherein the weight coefficient changes according to the change curve of each first risk assessment value within a preset time period.

[0068] That is, DR(t), SR(t), and SE(t) in the above formula can vary depending on the current driving mode of the vehicle and can also vary based on the variation curve of the first risk assessment value under the current driving mode. For example, DR(t) is highest in manual driving mode, and SR(t) is highest in assisted driving mode.

[0069] For example, in manual driving mode, DR(t), SR(t), and SE(t) are 6, 2, and 2 respectively; in assisted driving mode (also known as unmanned driving mode), DR(t), SR(t), and SE(t) are 3, 5, and 2 respectively.

[0070] Finally, the calculated CSRI(t) value is compared with the risk threshold and mapped to the corresponding location or area in the four risk levels of low risk, medium risk, high risk, and critical risk according to the comparison result.

[0071] Step 104: If the risk level corresponding to the comparison result meets the risk warning condition, a risk warning corresponding to the risk level is output.

[0072] Here, if the threshold range corresponding to the comparison result indicates a high risk, then it is determined that the risk level corresponding to the comparison result meets the risk warning condition; or, if the threshold range corresponding to the comparison result indicates a critical risk, then it is determined that the risk level corresponding to the comparison result meets the risk warning condition;

[0073] The critical risk level is higher than the high risk level.

[0074] In this application, if the risk level corresponding to the comparison result is high, the vehicle's steering wheel will vibrate slightly and the instrument panel lights will flash. If the risk level corresponding to the comparison result is critical, the vehicle's seat belts will be pre-tightened, the steering wheel will vibrate slightly, the instrument panel lights will flash, and a voice reminder will be output in the vehicle. This can effectively remind the driver that the current vehicle's driving state is risky and avoid accidents.

[0075] In the present application, when the risk level corresponding to the comparison result meets the risk warning condition, the vehicle can also execute a data storage event; wherein, the data storage event includes at least the storage of the multi-source information, the storage of the first risk assessment value, the storage of the second risk assessment value, and the storage of the risk level.

[0076] When executing a data storage event, the number of times the historical data storage event has been stored in the vehicle can be counted and compared with a threshold to obtain a comparison result. If the comparison result indicates that the number of storage times is less than the threshold, the data storage event is directly executed. If the comparison result indicates that the number of storage times is greater than or equal to the threshold, the data storage event overwrites the oldest historical data storage event in the vehicle. This reduces data storage redundancy and reduces the storage space usage of the data memory.

[0077] In the present application, the vehicle can also open the access permission of the data interface in the case of an accident event, so as to output the data storage event and / or the historical data storage event of the vehicle to the traffic management platform through the data interface, so that the traffic management platform analyzes the accident event based on the data storage event and / or the historical data storage event of the vehicle.

[0078] It should be noted that, in the case of no accident, the data interface of the vehicle is usually in an encrypted mode to prevent illegal personnel from stealing data or tampering with data, so as to ensure the driving safety of the vehicle and the safety of the system. In the case of an accident of the vehicle, in order to assist the traffic management department to speed up the analysis and judgment result of the accident, the vehicle automatically opens the access permission of the data interface, and can automatically establish a communication connection with the traffic management platform of the traffic management department through the data interface, so as to output or send the data storage event and / or the historical data storage event of the vehicle to the traffic management platform, so that the traffic management platform analyzes the accident event based on the data storage event and / or the historical data storage event of the vehicle. In this way, the judgment complexity of the traffic management department on the accident can be greatly reduced, and the processing efficiency of the accident can be improved.

[0079] The vehicle operation risk assessment method provided in the present application covers multiple indexes such as vehicle operation data, driver state data, auxiliary driving system state data, and driving environment data which directly affect traffic safety by collecting multi-source information. Not only can the complexity of the actual driving environment be comprehensively reflected, but also the precision of risk assessment can be improved. Moreover, in the case of an accident of the vehicle, effective help can be brought to the accident investigation, the probability of the accident of the vehicle is greatly reduced, and the accident processing efficiency after the accident of the vehicle is improved.

[0080] Figure 2 The structure and composition of the vehicle operation risk assessment system in the present application are shown in the schematic diagram as shown in Figure 2 The system comprises:

[0081] The data collection module 201 is configured to collect multi-source information of the vehicle, and the multi-source information at least comprises vehicle operation data, driver state data, and auxiliary driving system state data.

[0082] The data processing module 202 is configured to determine a first risk assessment value of the vehicle based on the multi-source information, wherein the first risk assessment value represents at least one of a driver control risk, an auxiliary driving control risk, and a vehicle operation environment risk; and compare the first risk assessment value with a corresponding risk threshold value.

[0083] The output module 203 is configured to output a risk warning corresponding to the risk level if the risk level corresponding to the comparison result meets the risk warning condition.

[0084] In a preferred embodiment of the present application, the system further comprises:

[0085] The data storage module 204 is used to execute a data storage event if the risk level corresponding to the comparison result meets the risk prompt condition; wherein the data storage event at least includes the storage of the multi-source information, the storage of the first risk assessment value, the storage of the second risk assessment value, and the storage of the risk level.

[0086] In the preferred embodiment of the present application, the data processing module 202 can also count the number of storage times of historical data storage events in the vehicle; if the number of storage times is less than the number threshold, the data processing module 202 triggers the data storage module 204 to directly execute the data storage event; if the number of storage times is greater than or equal to the number threshold, the data processing module 202 triggers the data storage module 204 to overwrite the historical data storage event with the earliest storage time in the vehicle with the data storage event.

[0087] In a preferred embodiment of the present application, the data processing module 202 is specifically configured to input the multi-source information into a safety risk assessment model, and obtain one or more current first risk assessment values ​​of the vehicle based on the safety risk assessment model.

[0088] In a preferred embodiment of the present application, the data processing module 202 is specifically used to directly compare each of the first risk assessment values ​​with the corresponding risk threshold to obtain the current risk level of the vehicle; or, to weight multiple first risk assessment values ​​to obtain a second risk assessment value; and to compare the second risk assessment value with the corresponding risk threshold to obtain the current risk level of the vehicle.

[0089] In a preferred embodiment of the present application, the data processing module 202 is further configured to determine a current driving mode of the vehicle, wherein the driving mode indicates whether the vehicle is currently in manual driving or assisted driving;

[0090] assigning a weight coefficient to each first risk assessment value based on the driving mode;

[0091] A weighted processing is performed on the plurality of first risk assessment values ​​based on the weight coefficient; wherein the weight coefficient changes according to a change curve of each first risk assessment value within a preset time period.

[0092] In a preferred embodiment of the present application, the data processing module 202 is further configured to determine that the risk level corresponding to the comparison result satisfies a risk warning condition if the threshold range corresponding to the comparison result indicates a high risk; or determine that the risk level corresponding to the comparison result satisfies a risk warning condition if the threshold range corresponding to the comparison result indicates a critical risk;

[0093] The critical risk level is higher than the high risk level.

[0094] In the preferred embodiment of the present application, the output module 203 is used to trigger the vehicle's steering wheel to vibrate slightly and the instrument panel lights to flash if the risk level corresponding to the comparison result is high risk; if the risk level corresponding to the comparison result is critical risk, it is used to trigger the vehicle's seat belt pre-tightening, steering wheel to vibrate slightly, instrument panel lights to flash, and voice reminders to be output in the vehicle.

[0095] In a preferred embodiment of the present application, the data processing module 202 is further configured to enable data interface access rights if an accident occurs to the vehicle;

[0096] The output module 203 is used to output the data storage event and / or the historical data storage event of the vehicle to the traffic management platform through the data interface, so that the traffic management platform can analyze the accident event based on the data storage event and / or the historical data storage event of the vehicle.

[0097] It should be noted that the vehicle operation risk assessment system provided in the above embodiment is different from the above Figure 1 The vehicle operation risk assessment method provided belongs to the same concept. The specific implementation process can refer to the above method embodiment and will not be repeated here.

[0098] The vehicle operation risk assessment system provided by this application not only enriches the comprehensiveness of the data and improves the accuracy of vehicle operation risk assessment, but also can automatically connect to the traffic management platform in the event of a vehicle accident, provide effective data to the traffic management platform, and improve the efficiency of accident investigation.

[0099] Figure 3 This is a schematic diagram of the structure of the vehicle in this application, such as Figure 3As shown, the vehicle 300 includes at least one processor 301 and a memory 302 for storing a computer program that can be run on the processor 301. When the processor 301 runs the computer program, it executes the vehicle operation risk assessment method suggested by the above embodiment of the present application. The vehicle 300 also includes at least one network interface 304 and a user interface 303. The various components in the vehicle 300 are coupled together through a bus system 305. It can be understood that the bus system 305 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 305 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 3 Various buses are labeled as bus system 305 .

[0100] The user interface 303 may include a display, a keyboard, a mouse, a trackball, a click wheel, keys, buttons, a touch pad or a touch screen.

[0101] It can be understood that the memory 302 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example but not limitation, many forms of RAM can be used, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).The memory 302 described in the embodiments of the present application is intended to include, but is not limited to, these and any other suitable types of memories.

[0102] The memory 302 in the embodiment of the present application is used to store various types of data to support the operation of the vehicle 300. Examples of these data include: any computer program for operating on the vehicle 300, such as an operating system 3021, an application 3022; wherein the operating system 3021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 3022 can include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The program for implementing the method of the embodiment of the present application can be included in the application 3022.

[0103] The processor 301 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor 301 or by instructions in the form of software. The above-mentioned processor 301 may be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 301 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in the memory 302. The processor 301 reads the information in the memory 302 and completes the steps of the above method in combination with its hardware.

[0104] In an exemplary embodiment, the vehicle 300 may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0105] In the example embodiment, the application also provides a computer readable storage medium, such as the memory 302 including a computer program executable by the processor 301 of the vehicle 300 to complete the steps of the aforementioned method. The computer readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM, etc.; or a variety of devices including one or any combination of the above memories, such as a computer, a tablet device, a personal digital assistant, etc.

[0106] A computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to perform the vehicle operation risk assessment method disclosed in the embodiments of the application.

[0107] In the several embodiments provided by the present application, it should be understood that the disclosed device and system can be implemented in other ways. The device embodiments described above are only illustrative. In addition, the features disclosed in the several device embodiments provided by the present application can be combined arbitrarily without conflict, to obtain new device embodiments or system embodiments.

[0108] The above describes only the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A vehicle operation risk assessment method, characterized in that: The method comprises: Collecting multi-source information of the vehicle, the multi-source information including at least vehicle operation data, driver status data, auxiliary driving system status data, and driving environment data; determining a current first risk assessment value of the vehicle based on the multi-source information, the first risk assessment value representing at least one of a driver control risk, an assisted driving control risk, and a vehicle operating environment risk; comparing the first risk assessment value with a corresponding risk threshold; If the risk level corresponding to the comparison result meets the risk prompt condition, a risk prompt corresponding to the risk level is output.

2. The method according to claim 1, characterized in that Determining a first current risk assessment value of the vehicle based on the multi-source information includes: The multi-source information is input into a safety risk assessment model, and one or more current first risk assessment values ​​of the vehicle are obtained based on the safety risk assessment model.

3. The method according to claim 1, characterized in that The comparing the first risk assessment value with a corresponding risk threshold value includes: directly comparing each of the first risk assessment values ​​with a corresponding risk threshold to obtain a current risk level of the vehicle; Alternatively, a plurality of the first risk assessment values ​​are weighted to obtain a second risk assessment value; and the second risk assessment value is compared with a corresponding risk threshold to obtain the current risk level of the vehicle.

4. The method according to claim 3, characterized in that Before performing weighted processing on the plurality of first risk assessment values, the method further includes: determining a current driving mode of the vehicle, the driving mode indicating whether the vehicle is currently in manual driving or assisted driving; assigning a weight coefficient to each first risk assessment value based on the driving mode; A weighted processing is performed on the plurality of first risk assessment values ​​based on the weight coefficient; wherein the weight coefficient changes according to a change curve of each first risk assessment value within a preset time period.

5. The method according to claim 1, wherein The risk level corresponding to the comparison result meets the risk warning conditions, including: If the threshold range corresponding to the comparison result indicates a high risk, determining that the risk level corresponding to the comparison result meets a risk warning condition; Alternatively, if the threshold range corresponding to the comparison result indicates a critical risk, determining that the risk level corresponding to the comparison result meets a risk warning condition; The critical risk level is higher than the high risk level.

6. The method according to claim 1, characterized in that Outputting the risk prompt corresponding to the risk level includes: If the risk level corresponding to the comparison result is high risk, the steering wheel of the vehicle is triggered to vibrate slightly and the instrument panel lights flash; If the risk level corresponding to the comparison result is a critical risk, the vehicle's seat belt pre-tightening, steering wheel micro-vibration, instrument panel lights flashing, and voice reminders are triggered in the vehicle.

7. The method according to any one of claims 1 to 6, characterized in that If the risk level corresponding to the comparison result meets the risk warning condition, the method further includes: Execute a data storage event; wherein the data storage event at least includes the storage of the multi-source information, the storage of the first risk assessment value, the storage of the second risk assessment value, and the storage of the risk level.

8. The method according to claim 7, characterized in that The execution data storage event includes: Counting the number of storage events of historical data storage in the vehicle; If the storage times are less than the times threshold, directly executing the data storage event; If the storage times are greater than or equal to the times threshold, the historical data storage event with the earliest storage time in the vehicle is overwritten by the data storage event.

9. The method according to claim 8, characterized in that The method further comprises: If an accident occurs to the vehicle, the data interface access permission is enabled to output the data storage event and / or the historical data storage event of the vehicle to the traffic management platform through the data interface, so that the traffic management platform can analyze the accident based on the data storage event and / or the historical data storage event of the vehicle.

10. A vehicle operation risk assessment system, characterized in that: The system comprises: A data collection module is used to collect multi-source information of the vehicle, wherein the multi-source information includes at least vehicle operation data, driver status data, and auxiliary driving system status data; a data processing module, configured to determine a current first risk assessment value of the vehicle based on the multi-source information, the first risk assessment value representing at least one of a driver control risk, an assisted driving control risk, and a vehicle operating environment risk; and to compare the first risk assessment value with a corresponding risk threshold; The output module is used to output a risk warning corresponding to the risk level if the risk level corresponding to the comparison result meets the risk warning condition.

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