Vehicle part management method and system based on automatic driving, medium and equipment

Abstract

The invention discloses a vehicle part management method and system based on automatic driving, a medium and equipment, and relates to the technical field of intelligent vehicle control. The method comprises the following steps: acquiring vehicle surrounding environment information and historical operation information of each component; processing the historical operation information of each component by using a driving habit model to obtain driver habit information; generating an optimal route according to preset navigation information in combination with surrounding environment information, driving according to the optimal route, and adjusting real-time operation parameters of each component according to driver habit information by taking minimum output as a target; and according to the real-time operation parameters of each part, carrying out residual life estimation on the corresponding part to obtain a safety evaluation prediction result of each part in each travel. The service life of the safety parts of the intelligent networked automobile with the automatic driving system can be prolonged, and meanwhile more accurate replacement can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent vehicle control technology, and in particular to a vehicle component management method, system, medium and equipment based on automatic driving. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of intelligent connected vehicles, the application of autonomous driving technology is becoming more and more extensive. The number of components in the vehicle controlled by autonomous driving is increasing, and the probability of vehicle failure and function failure is also increasing. At present, the conventional fault alarm method of vehicles is to remind the component to fail only after the component fails, or only display the mileage. Everyone's driving habits and vehicle usage conditions are different. Replacing components that have not reached the end of their service life in advance will cause unnecessary waste, or when traveling long distances, the components suddenly fail, causing great trouble to the driver. Due to different driving habits of drivers, the use of vehicles varies greatly. If the utilization rate of driving components cannot be improved, the cost of using the vehicle will increase.

[0004] Therefore, the prior art lacks a system that can adaptively manage multiple components in a vehicle according to the driver's habits and improve component utilization and driver comfort while ensuring vehicle driving safety. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a vehicle component management method, system, medium and equipment based on autonomous driving. On the one hand, it manages the operating conditions of each component according to the driver's driving habits, reduces energy output and improves travel experience; on the other hand, it monitors the service life of each component, which can increase the service life of safety components of intelligent connected vehicles with autonomous driving systems while achieving more accurate replacement.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0007] A first aspect of the present invention provides a vehicle component management method based on automatic driving, comprising the following steps:

[0008] Obtain vehicle surrounding environment information and historical operation information of each component;

[0009] The driving habit model is used to process the historical operation information of each component to obtain the driver's habit information;

[0010] Generate the optimal route based on preset navigation information combined with surrounding environment information, drive along the optimal route, and adjust the real-time operating parameters of each component according to the driver's habit information and with the goal of minimizing output;

[0011] The remaining life of the corresponding components is estimated according to the real-time operating parameters of each component, and the safety assessment prediction results of each trip of each component are obtained.

[0012] Furthermore, the surrounding environment information includes obstacle information and traffic sign information around the vehicle.

[0013] Furthermore, the driving habit information includes steering habits, braking habits and vehicle speed habits.

[0014] Furthermore, the specific steps of using the driving habit model to process the historical operation information of each component and obtain the driver's habit information are as follows:

[0015] Use convolutional neural network to build driving habit model;

[0016] The driving habit model is trained using the historical operation information of each component to obtain the driver's steering habit range, braking habit range and speed habit range as the driver's habit information.

[0017] Furthermore, the specific steps for adjusting the real-time operating parameters of each component according to the driver's habit information and with the goal of minimizing output are as follows:

[0018] Calculate the output of each component based on the optimal route with the minimum total output as the goal and the driver's habit information as the constraint condition;

[0019] The real-time operating parameters of each component are adjusted according to the calculation results.

[0020] Furthermore, the specific steps of estimating the remaining life of the corresponding components according to the real-time operating parameters of each component are as follows:

[0021] Obtain the accumulated usage time and control range of each component, and compare them with the known life parameters of the component to determine whether the service life has been reached;

[0022] Obtain the predicted usage time and control range of each component for this trip, and compare them with the known life parameters of the component to determine whether the service life has been reached.

[0023] A second aspect of the present invention provides a management system for the vehicle component management method based on automatic driving according to the first aspect, comprising:

[0024] Data collection equipment, used to collect information about the vehicle's surrounding environment and send the surrounding environment information to the autonomous driving domain controller;

[0025] Integrated control equipment, used to control each component and collect operating parameters, and send the collected operating parameters to the central gateway;

[0026] The central gateway is used to receive operating parameters and exchange information with the autonomous driving domain controller;

[0027] The autonomous driving domain controller is used to receive the collected surrounding environment information, plan the driving trajectory based on the surrounding environment information and navigation information, and interact with the central gateway to conduct safety assessment of the vehicle based on the interaction information;

[0028] Communication equipment is used to realize the communication function of the system.

[0029] A third aspect of the present invention provides a vehicle component management system based on automatic driving, comprising:

[0030] An information collection module is configured to obtain vehicle surrounding environment information and historical operation information of each component;

[0031] A model processing module is configured to process the historical operation information of each component using the driving habit model to obtain driver habit information;

[0032] A route planning module is configured to generate an optimal route based on preset navigation information combined with surrounding environment information, drive according to the optimal route, and adjust the real-time operating parameters of each component according to the driver's habit information and with the goal of minimizing output;

[0033] The safety assessment module is configured to estimate the remaining life of the corresponding components according to the real-time operating parameters of the corresponding components, and obtain the safety assessment prediction results of each trip of each component.

[0034] A fourth aspect of the present invention provides a medium having a program stored thereon, which, when executed by a processor, implements the steps in the vehicle component management method based on automatic driving as described in the first aspect of the present invention.

[0035] The fifth aspect of the present invention provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the vehicle component management method based on autonomous driving as described in the first aspect of the present invention are implemented.

[0036] One or more of the above technical solutions have the following beneficial effects:

[0037] The present invention discloses a vehicle component management method, system, medium and device based on automatic driving. On the one hand, the operating conditions of each component are managed according to the driver's driving habits, energy output is reduced, and travel experience is improved. On the other hand, the service life of each component is monitored, which can improve the service life of the safety components of the intelligent networked vehicle with the automatic driving system while achieving more accurate replacement, and provide more accurate information for the replacement of automobile brake components and tires. At the same time, according to the driver's habits and navigation positioning and other related information, the output of each integrated control device is statically and accurately controlled to save the energy of the vehicle to achieve higher endurance.

[0038] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0040] Figure 1 This is a structural diagram of a vehicle component management system based on autonomous driving in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0041] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0042] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "include" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or their combinations;

[0043] Embodiment 1:

[0044] Embodiment 1 of the present invention provides a vehicle component management method based on automatic driving, comprising the following steps:

[0045] Step 1: Obtain vehicle surrounding environment information and historical operation information of each component.

[0046] The surrounding environment information includes obstacle information and traffic sign information around the vehicle. In this embodiment, large objects that affect traffic on the road, such as vehicles, pedestrians, and curbstones around the vehicle, are considered obstacles.

[0047] The size and shape information of obstacles around the vehicle are collected through data collection equipment, and traffic sign information is obtained through the cloud. The historical operation information of each component is obtained through the data statistics of historical trips.

[0048] The various components in this embodiment include an integrated control device and tires, and the integrated control device includes: HCU (hybrid control unit), IPB (integrated brake controller assembly), EPS (electric power steering controller), and DMC (infotainment domain controller).

[0049] Step 2: Use the driving habit model to process the historical operation information of each component to obtain the driver habit information.

[0050] The driving habit information includes steering habits, braking habits and vehicle speed habits.

[0051] In a specific implementation, the specific steps of processing the historical operation information of each component using the driving habit model to obtain the driver habit information are as follows:

[0052] Step 2.1: Use convolutional neural network to build a driving habit model.

[0053] Step 2.2: Use the historical operation information of each component to train the driving habit model, and obtain the driver's steering habit range, braking habit range and speed habit range as the driver's habit information.

[0054] The collected information includes historical operation information of vehicle components, including historical steering amplitude, historical vehicle speed, historical braking frequency and amplitude. The above historical operation information is learned and classified using a convolutional neural network to obtain the driver's commonly used steering amplitude, common vehicle speed on straight roads, common vehicle speed on curved roads, braking amplitude, and common braking frequency on curved roads. Since the braking frequency of the straight road section is randomly determined according to the traffic volume and traffic conditions, this embodiment only learns braking on curved roads.

[0055] The steering habit range is obtained according to the steering amplitude commonly used by the driver, the speed habit range is obtained according to the driver's commonly used speeds on straight roads and curved roads, and the braking habit range is obtained according to the driver's commonly used braking amplitude and the commonly used braking frequency on curved roads.

[0056] Step 3: Generate an optimal route based on preset navigation information combined with surrounding environment information, drive along the optimal route, and adjust the real-time operating parameters of each component according to the driver's habit information and with the goal of minimizing output.

[0057] Step 3.1: Generate the optimal route based on the preset navigation information and the surrounding environment information.

[0058] Step 3.1.1: The surrounding environment information includes obstacle information and traffic sign information around the vehicle. The driving route is determined according to the traffic sign information of the preset navigation destination.

[0059] Step 3.1.2: According to the real-time update of various obstacles on the driving route, obstacle avoidance planning is carried out to obtain the optimal route.

[0060] Step 3.2: The specific steps for adjusting the real-time operating parameters of each component according to the driver's habit information and with the goal of minimizing output are as follows:

[0061] Step 3.2.1: Calculate the output of each component based on the optimal route with the minimum total output as the goal and the driver's habit information as the constraint condition.

[0062] In a specific implementation, the objective function is constructed:

[0063] F=min∑P HCU +P IPB +P EPS .

[0064] Among them, F is the total output, P HCU , P IPB , P EPS They are the outputs of HCU, IPB and EPS respectively.

[0065] The constraints are:

[0066] D min <P HCU <D max ;

[0067] H min <P IPB <H max ;

[0068] E min <P eps <E max .

[0069] Among them, D min , D max are the minimum and maximum values ​​of the vehicle speed habit range, H min , H max are the minimum and maximum values ​​of the braking habit range, E min 、E max are the minimum and maximum values ​​of the special habit range respectively.

[0070] Step 3.2.2: Adjust the real-time operating parameters of each component based on the calculation results.

[0071] In a specific implementation, the autonomous driving domain controller adjusts the outputs of the HCU, IPB, and EPS according to the above calculation results, and records the comparison of the vehicle operating conditions before and after the adjustment.

[0072] Step 4: Estimate the remaining life of the corresponding components based on the real-time operating parameters of each component to obtain the safety assessment prediction results of each trip of each component.

[0073] Step 4.1: Real-time evaluation of the current trip: Obtain the cumulative usage time and control range of each component at present, and compare them with the known life parameters of the component to determine whether the service life has been reached.

[0074] In a specific implementation, when the autonomous driving domain controller starts working, the autonomous driving domain controller counts the usage time and control range of each control of HCU, IPB, and EPS, such as the braking force of IPB, the steering wheel angle of EPS, the acceleration of the vehicle power provided by HCU, and other parameters. That is, HCU, IPB, and EPS will transmit the driver's usage time and control range to the autonomous driving domain controller through the gateway using Ethernet. The autonomous driving domain controller collects the cumulative count when the autonomous driving function is turned on and the information statistics transmitted by HCU, IPB, and EPS, and uses data methods to count whether HCU, IPB, and EPS have reached their service life. If they have reached their service life, they will be sent to DMC through the gateway using Ethernet to remind the driver to replace or repair the component.

[0075] Taking IPB braking statistics as an example, its working principle is:

[0076] Normal wear statistics of working condition A: vehicle speed V≠0, the actual time when the automatic driving controller receives IPB braking is accumulated, and the statistics at this time are all effective braking.

[0077] Condition B has wear statistics after the vehicle speed drops to 0: When the vehicle speed V drops to 0, the time when the automatic driving controller sends the braking command and the actual braking time of IPB are counted. At this time, only the statistics from the start of braking until the vehicle speed reaches 0 are counted, and the time after the braking speed reaches 0 is not included in the effective duration. The sum of the two statistical results of conditions AB is compared with the service life of the brake pads. When the sum of the braking time statistics of conditions AB is close to the life of the brake pads, the automatic driving controller sends an alarm to the DMC via Ethernet to remind the driver to replace the brake pads in time. At the same time, it can recommend the nearest 4S shop for repairs, and synchronize the information about the customer's brake pads to be updated to the corresponding 4S shop to prepare spare parts.

[0078] For vehicle tire life estimation, the acceleration and deceleration of the tires can be calculated by statistically analyzing the usage of IPB and HCU, and the tire service life can be inferred based on the dynamic model: the wear caused by braking can be calculated based on the braking statistics time provided by IPB, plus the wear caused by the acceleration provided by HCU power to the vehicle, and then the sum of the natural wear of the tires is compared with the tire service life. The driver can be reminded by DMC to replace the tires in time, providing the driver with safer guarantees.

[0079] The calculation processes of the above-mentioned wear amount, acceleration and deceleration conditions, and dynamic model are common knowledge in the field and will not be elaborated here.

[0080] Step 4.2: Complete prediction evaluation of the current trip: Obtain the predicted usage time and control range of each component in this trip, and compare them with the known life parameters of the components to determine whether the service life has been reached.

[0081] Step 4.2.1: Predict the component usage time and control range for completing the entire route based on the planned optimal route and the driver's habit information. The prediction model can be trained using the existing machine learning model using the driver's historical data.

[0082] Step 4.2.2: Add the predicted results to the accumulated values ​​before departure, compare the accumulated results with the life parameters, and determine whether the service life has been reached.

[0083] Step 4.2.3: After the trip, optimize the prediction model based on the feedback.

[0084] The present invention controls the minimum output of each vehicle component within the driver's habitual range, thereby maximizing energy utilization and improving the driver's comfort level. In addition, the present invention also realizes the travel prediction and evaluation of the life of each vehicle component, preventing the vehicle from having no maintenance station during driving, resulting in the inability to replace the vehicle even if it is known that the life has reached the critical value, and only road rescue can be used, resulting in human and financial losses. The present invention combines the travel prediction and evaluation of components with real-time evaluation to further improve the utilization rate of vehicle components.

[0085] Embodiment 2:

[0086] Embodiment 2 of the present invention provides a management system for the vehicle component management method based on autonomous driving described in Embodiment 1. The system is safe, reliable, and highly practical, and is applicable to any intelligent vehicle with an autonomous driving system. It includes:

[0087] Data acquisition equipment is used to collect information about the vehicle's surrounding environment and send the surrounding environment information to the autonomous driving domain controller.

[0088] In this embodiment, the data acquisition equipment includes infrared night vision equipment, millimeter wave radar, perimeter camera, surround camera, multi-function front view camera, laser radar, etc. installed on the vehicle. Among them, the infrared night vision equipment, perimeter camera, surround camera and multi-function front view camera transmit information through LVDS, the laser radar transmits information through Ethernet, and the millimeter wave radar transmits information through CAN FD.

[0089] The surrounding environment information includes obstacle information and traffic sign information around the vehicle. In this embodiment, vehicles, pedestrians, curbstones and other large objects that affect traffic on the road around the vehicle are regarded as obstacles. The size and shape information of the obstacles around the vehicle are collected using data acquisition equipment.

[0090] Integrated control equipment, used to control each component and collect operating parameters, and send the collected operating parameters to the central gateway.

[0091] The integrated control equipment includes: HCU (hybrid control unit), IPB (integrated brake controller assembly), EPS (electric power steering controller), DMC (infotainment domain controller).

[0092] The central gateway is used to receive operating parameters and exchange information with the autonomous driving domain controller.

[0093] The autonomous driving domain controller is used to receive the collected surrounding environment information, plan the driving trajectory based on the surrounding environment information and navigation information, exchange information with the central gateway, and conduct safety assessment of the vehicle based on the interaction information.

[0094] During information exchange with the central gateway and vehicle safety assessment based on the interactive information, HCU provides vehicle power, IPB provides vehicle braking force, EPS controls vehicle steering, and DMC reminds the driver. The autonomous driving domain controller collects the usage of HCU, IPB, and EPS and compares them with their design life to give the driver advance reminders so that the driver can replace components in advance. It provides safety protection and travel reminders for the driver, and uploads them to the cloud through T-BOX for reference by other vehicles and for after-sales 4S stores to provide sufficient supply for stocking.

[0095] Communication equipment is used to realize the communication function of the system. In this embodiment, the communication equipment adopts T-BOX (TelematicBOX, remote information processor). The method of obtaining navigation information in the present invention is: T-BOX transmits the location information of the vehicle to the autonomous driving domain controller through Ethernet using the ADAS map function. The autonomous driving domain controller plans the driving route of the vehicle through the collected surrounding environment information and the navigation information of the positioning of T-BOX, and sends a control signal of the driving route to the integrated control device through the central gateway. The HCU controls the energy output of the vehicle, the IPB controls the braking device of the vehicle, the EPS controls the steering of the vehicle, and the DMC controls the display screen of the vehicle and the alarm when encountering danger.

[0096] In a specific implementation, the process of performing safety assessment on a vehicle based on the interaction information includes:

[0097] (1) Real-time evaluation of the current trip: Obtain the cumulative usage time and control range of each component and compare them with the known life parameters of the component to determine whether the service life has been reached.

[0098] (2) Complete prediction and evaluation of the current trip: Obtain the predicted usage time and control range of each component in this trip, and compare them with the known life parameters of the component to determine whether the service life has been reached.

[0099] When the autonomous driving domain controller starts working, the autonomous driving domain controller counts the usage time and control range of each control of HCU, IPB, and EPS, such as the braking force of IPB, the steering wheel angle of EPS, the acceleration of the vehicle power provided by HCU, and other parameters. That is, HCU, IPB, and EPS will transmit the driver's usage time and control range to the autonomous driving domain controller through the gateway using Ethernet. The autonomous driving domain controller collects the cumulative count when the autonomous driving function is turned on and the information statistics transmitted by HCU, IPB, and EPS, and uses data methods to count whether HCU, IPB, and EPS have reached their service life. If they have reached their service life, they will be sent to DMC through the gateway using Ethernet to remind the driver to replace or repair the component.

[0100] Taking IPB braking statistics as an example, its working principle is:

[0101] Normal wear statistics of working condition A: vehicle speed V≠0, the actual time when the automatic driving controller receives IPB braking is accumulated, and the statistics at this time are all effective braking.

[0102] Condition B has wear statistics after the vehicle speed drops to 0: When the vehicle speed V drops to 0, the time when the automatic driving controller sends the braking command and the actual braking time of IPB are counted. At this time, only the statistics from the start of braking until the vehicle speed reaches 0 are counted, and the time after the braking speed reaches 0 is not included in the effective duration. The sum of the two statistical results of conditions AB is compared with the service life of the brake pads. When the sum of the braking time statistics of conditions AB is close to the life of the brake pads, the automatic driving controller sends an alarm to the DMC via Ethernet to remind the driver to replace the brake pads in time. At the same time, it can recommend the nearest 4S shop for repairs, and synchronize the information about the customer's brake pads to be updated to the corresponding 4S shop to prepare spare parts.

[0103] For vehicle tire life estimation, the acceleration and deceleration of the tires can be calculated by statistically analyzing the usage of IPB and HCU, and the tire service life can be inferred based on the dynamic model: the wear caused by braking can be calculated based on the braking statistics time provided by IPB, plus the wear caused by the acceleration provided by HCU power to the vehicle, and then the sum of the natural wear of the tires is compared with the tire service life. The driver can be reminded by DMC to replace the tires in time, providing the driver with safer guarantees.

[0104] like Figure 1 As shown, HCU, IPB, EPS and other components transmit signals to the gateway via Ethernet. The gateway forwards the signals of each controller to the autonomous driving domain controller for collection, and uses the driving habit model to learn the driver's driving habits. When the user turns on the autonomous driving mode, the autonomous driving domain controller plans the route according to the surrounding environment information and preset navigation information, and controls the output of HCU, IPB, and EPS under different working conditions according to the planned route and driving habits, and provides the driver with the vehicle speed, braking amplitude, braking frequency and special amplitude commonly used by the driver, so as to save vehicle energy, reduce the wear of IPB and tires, and increase their service life while meeting the driver's habits.

[0105] like Figure 1 As shown, the autonomous driving domain controller uploads the above data to the cloud in real time to provide data reference for other vehicles. After obtaining this data, the after-sales service can also prepare spare parts for the vehicle in advance to meet the driver's needs more accurately.

[0106] Embodiment three:

[0107] Embodiment 3 of the present invention provides a vehicle component management system based on automatic driving, such as Figure 1 As shown, including:

[0108] An information collection module is configured to obtain vehicle surrounding environment information and historical operation information of each component;

[0109] A model processing module is configured to process the historical operation information of each component using the driving habit model to obtain driver habit information;

[0110] A route planning module is configured to generate an optimal route based on preset navigation information combined with surrounding environment information, drive according to the optimal route, and adjust the real-time operating parameters of each component according to the driver's habit information and with the goal of minimizing output;

[0111] The safety assessment module is configured to estimate the remaining life of the corresponding components according to the real-time operating parameters of the corresponding components, and obtain the safety assessment prediction results of each trip of each component.

[0112] Embodiment 4:

[0113] Embodiment 4 of the present invention provides a medium on which a program is stored. When the program is executed by a processor, the steps in the vehicle component management method based on automatic driving as described in Embodiment 1 of the present invention are implemented.

[0114] Embodiment five:

[0115] Embodiment 5 of the present invention provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the vehicle component management method based on automatic driving as described in Embodiment 1 of the present invention are implemented.

[0116] The steps involved in the above embodiments 2, 3, 4 and 5 correspond to the method embodiment 1. For the specific implementation methods, please refer to the relevant description part of embodiment 1.

[0117] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

[0118] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0119] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A vehicle component management method based on automatic driving, characterized in that: The following steps are involved: Obtain vehicle surrounding environment information and historical operation information of each component; The driving habit model is used to process the historical operation information of each component to obtain the driver's habit information; Generate the optimal route based on preset navigation information combined with surrounding environment information, drive along the optimal route, and adjust the real-time operating parameters of each component according to the driver's habit information and with the goal of minimizing output; The remaining life of the corresponding components is estimated according to the real-time operating parameters of each component, and the safety assessment prediction results of each trip of each component are obtained.

2. The vehicle component management method based on automatic driving according to claim 1, characterized in that: The surrounding environment information includes obstacle information and traffic sign information around the vehicle.

3. The vehicle component management method based on automatic driving according to claim 1, characterized in that: The driving habit information includes steering habits, braking habits and vehicle speed habits.

4. The vehicle component management method based on automatic driving according to claim 3, characterized in that: The specific steps of using the driving habit model to process the historical operation information of each component and obtain the driver's habit information are as follows: Use convolutional neural network to build driving habit model; The driving habit model is trained using the historical operation information of each component to obtain the driver's steering habit range, braking habit range and speed habit range as the driver's habit information.

5. The vehicle component management method based on automatic driving according to claim 4, characterized in that: The specific steps to adjust the real-time operating parameters of each component according to the driver's habit information and with the goal of minimizing output are: Calculate the output of each component based on the optimal route with the minimum total output as the goal and the driver's habit information as the constraint condition; The real-time operating parameters of each component are adjusted according to the calculation results.

6. The vehicle component management method based on automatic driving according to claim 1, characterized in that: The specific steps for estimating the remaining life of corresponding components according to the real-time operating parameters of each component are as follows: Obtain the accumulated usage time and control range of each component, and compare them with the known life parameters of the component to determine whether the service life has been reached; Obtain the predicted usage time and control range of each component for this trip, and compare them with the known life parameters of the component to determine whether the service life has been reached.

7. A management system for the vehicle component management method based on automatic driving according to any one of claims 1 to 6, characterized in that: include: Data collection equipment, used to collect information about the vehicle's surrounding environment and send the surrounding environment information to the autonomous driving domain controller; Integrated control equipment, used to control each component and collect operating parameters, and send the collected operating parameters to the central gateway; The central gateway is used to receive operating parameters and exchange information with the autonomous driving domain controller; The autonomous driving domain controller is used to receive the collected surrounding environment information, plan the driving trajectory based on the surrounding environment information and navigation information, and interact with the central gateway to conduct safety assessment of the vehicle based on the interaction information; Communication equipment is used to realize the communication function of the system.

8. A vehicle component management system based on automatic driving, characterized in that: include: An information collection module is configured to obtain vehicle surrounding environment information and historical operation information of each component; A model processing module is configured to process the historical operation information of each component using the driving habit model to obtain driver habit information; A route planning module is configured to generate an optimal route based on preset navigation information combined with surrounding environment information, drive according to the optimal route, and adjust the real-time operating parameters of each component according to the driver's habit information and with the goal of minimizing output; The safety assessment module is configured to estimate the remaining life of the corresponding components according to the real-time operating parameters of the corresponding components, and obtain the safety assessment prediction results of each trip of each component.

9. A computer-readable storage medium, characterized in that: A plurality of instructions are stored therein, and the instructions are suitable for being loaded by a processor of a terminal device and executing the vehicle component management method based on autonomous driving as described in any one of claims 1-6.

10. A terminal device, characterized in that: It includes a processor and a computer-readable storage medium, the processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the vehicle component management method based on automatic driving as described in any one of claims 1-6.

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