Methods, systems, media, and devices for managing vehicle components based on autonomous driving

Abstract

This invention discloses a method, system, medium, and device for managing vehicle components based on autonomous driving, relating to the field of intelligent vehicle control technology. The method includes the following steps: acquiring information about the vehicle's surrounding environment and historical operating information of each component; processing the historical operating information of each component using a driving habit model to obtain driver habit information; generating an optimal route based on preset navigation information combined with surrounding environment information; driving according to the optimal route; adjusting the real-time operating parameters of each component according to the driver habit information and with the goal of minimizing output; estimating the remaining lifespan of each component based on its real-time operating parameters to obtain a safety assessment prediction result for each component's journey. This invention can improve the lifespan of safety components in intelligent connected vehicles with autonomous driving systems while achieving more precise replacement.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle control technology, and in particular to a method, system, medium, and device for managing vehicle components based on autonomous driving. Background Technology

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

[0003] With the rapid development of intelligent connected vehicles and the increasingly widespread application of autonomous driving technology, the number of components controlling the entire vehicle is increasing, leading to a higher probability of vehicle malfunctions and functional failures. Currently, conventional vehicle fault alarms only alert drivers when a component fails, or simply display mileage. However, individual driving habits and vehicle usage conditions differ, and prematurely replacing components before their lifespan is reached can lead to unnecessary waste. Furthermore, sudden component failures during long journeys can cause significant inconvenience for drivers. Since driver habits and vehicle usage vary greatly, failing to improve the utilization rate of driving components will increase vehicle operating costs.

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

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a vehicle component management method, system, medium, and device based on autonomous driving. On the one hand, it manages the operating conditions of various components according to the driver's driving habits, reducing energy output and improving the travel experience. On the other hand, it monitors the service life of various components, which can improve the service life of safety components in intelligent connected vehicles with autonomous driving systems while achieving more precise replacement.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0007] The first aspect of this invention provides a vehicle component management method based on autonomous driving, comprising the following steps:

[0008] Acquire information about the vehicle's surrounding environment and the historical operating information of each component;

[0009] By processing the historical operating information of each component using a driving habit model, driver habit information is obtained.

[0010] The system generates the optimal route based on preset navigation information and surrounding environment information, drives according to the optimal route, and adjusts the real-time operating parameters of each component according to the driver's habits and with the goal of minimizing output.

[0011] Based on the real-time operating parameters of each component, the remaining lifespan of the corresponding component is estimated, and the safety assessment prediction results for each component's trip are obtained.

[0012] Furthermore, the surrounding environment information includes information on obstacles around the vehicle and traffic signs.

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

[0014] Furthermore, the specific steps for processing the historical operating information of each component using a driving habit model to obtain driver habit information are as follows:

[0015] A driving habit model is constructed using a convolutional neural network;

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

[0017] Furthermore, the specific steps for adjusting the real-time operating parameters of each component based on driver habits and with the goal of minimizing output are as follows:

[0018] Based on the optimal route and with the goal of minimizing the total output, and taking driver habit information as a constraint, calculate the output of each component;

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

[0020] Furthermore, the specific steps for estimating the remaining life of the corresponding components based on their real-time operating parameters are as follows:

[0021] Obtain the cumulative usage time and control range of each component, and compare them with the known lifespan parameters of the components to determine whether they have reached their service life.

[0022] The predicted usage time and control range of each component for this stroke are obtained and compared with the known life parameters of the components 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 autonomous driving described in the first aspect, comprising:

[0024] Data acquisition equipment is used to collect information about the vehicle's surrounding environment and send that information to the autonomous driving domain controller.

[0025] An integrated control device is used to control each component and collect operating parameters, and then send the collected operating parameters to the central gateway.

[0026] The central gateway is used to receive operating parameters and interact with the autonomous driving domain controller.

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

[0028] Communication equipment is used to enable the communication functions of a system.

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

[0030] The information acquisition module is configured to acquire information about the vehicle's surrounding environment and the historical operating information of each component.

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

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

[0033] The safety assessment module is configured to estimate the remaining lifespan of each component based on its real-time operating parameters, and to obtain the safety assessment prediction results for each component's trip.

[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 of the vehicle component management method based on autonomous driving as described in the first aspect of the present invention.

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

[0036] The above one or more technical solutions have the following beneficial effects:

[0037] This invention discloses a method, system, medium, and device for managing vehicle components based on autonomous driving. On one hand, it manages the operating conditions of various components according to the driver's driving habits, reducing energy output and improving the travel experience. On the other hand, it monitors the service life of each component, enabling more precise replacement of safety components in intelligent connected vehicles with autonomous driving systems, and providing more accurate information for replacing vehicle braking components and tires. Simultaneously, based on driver habits and navigation positioning information, it precisely controls the output of various integrated control devices, saving vehicle energy and achieving greater driving range.

[0038] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0039] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

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

[0041] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0042] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0043] Example 1:

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

[0045] Step 1: Obtain information about the vehicle's surrounding environment and the historical operating information of each component.

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

[0047] The system collects information on the size and shape of obstacles around the vehicle using data acquisition equipment and retrieves traffic sign information from the cloud. Historical operational information for each component is obtained through statistical analysis of historical travel data.

[0048] The components in this embodiment include an integrated control device and tires. 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: Process the historical operating information of each component using the driving habit model to obtain driver habit information.

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

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

[0052] Step 2.1: Construct a driving habit model using a convolutional neural network.

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

[0054] The collected information includes historical operational information of vehicle components, such as historical steering amplitude, historical vehicle speed, and historical braking frequency and amplitude. A convolutional neural network is used to learn and classify this historical operational information to obtain the driver's commonly used steering amplitude, commonly used speed for straight roads, commonly used speed for curved roads, braking amplitude, and commonly used braking frequency for curved roads. Since the braking frequency for straight roads is randomly determined based on traffic flow and conditions, this embodiment only learns braking frequency for curved roads.

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

[0056] Step 3: Generate the optimal route based on the preset navigation information and the surrounding environment information, drive according to the optimal route, and adjust the real-time operating parameters of each component according to the driver's habits 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: Surrounding environment information includes information on obstacles around the vehicle and traffic signs. Based on the traffic signs of the preset navigation destination, determine the driving route.

[0059] Step 3.1.2: Based on the real-time updates of various obstacles on the driving route, perform obstacle avoidance planning 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: Based on the optimal route and with the goal of minimizing the total output, and with driver habit information as the constraint, calculate the output of each component.

[0062] In one specific implementation, the objective function is constructed as follows:

[0063] F = min∑P HCU +P IPB +P EPS .

[0064] Where F is the total output, P HCU P IPB P EPS These are the outputs of HCU, IPB, and EPS, respectively.

[0065] Its 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 These represent the minimum and maximum values ​​within the habitual speed range, H. min H max These are the minimum and maximum values ​​of the braking habit range, E. min E max These represent the minimum and maximum values ​​within the specific habit range.

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

[0071] In one specific implementation, the autonomous driving domain controller adjusts the outputs of HCU, IPB, and EPS based on the above calculation results, and records a comparison of the vehicle's operating status before and after the adjustment.

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

[0073] Step 4.1: Real-time assessment of the current journey: Obtain the cumulative usage time and control range of each component, and compare it with the known life parameters of the components to determine whether the service life has been reached.

[0074] In one specific implementation, when the autonomous driving domain controller starts working, it records the usage duration and control amplitude of each control operation of the HCU, IPB, and EPS, such as the braking force of the IPB, the steering wheel angle of the EPS, and the acceleration provided by the HCU to the vehicle. Specifically, the HCU, IPB, and EPS transmit this information—including the driver's usage duration and control amplitude—to the autonomous driving domain controller via Ethernet through a gateway. The autonomous driving domain controller collects the cumulative counts when the autonomous driving function is activated and the statistical information transmitted by the HCU, IPB, and EPS. It then uses data methods to determine whether the HCU, IPB, and EPS have reached the end of their service life. If they have, it sends a reminder to the DMC via Ethernet through the gateway to have the driver replace or repair the component.

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

[0076] Normal wear statistics under operating condition A: When the vehicle speed V≠0, the actual braking time received by the automatic driving controller from the IPB is accumulated, and all braking times are counted as effective braking.

[0077] Condition B includes wear statistics after the vehicle speed drops to 0: When the vehicle speed V drops to 0, the time it takes for the autopilot controller to send a braking command and the actual braking time of the IPB are statistically analyzed. Only the time from the start of braking until the vehicle speed reaches 0 is counted; the time after braking to 0 is not included in the effective duration. The sum of the two statistical results for Conditions A and B is compared with the brake pad lifespan. When the sum of the braking duration statistics for Conditions A and B approaches the brake pad lifespan, the autopilot controller sends an alarm to the DMC via Ethernet to remind the driver to replace the brake pads promptly. Simultaneously, the nearest 4S shop can be recommended for repair, and the customer's brake pad replacement information is synchronized to the corresponding 4S shop to prepare spare parts.

[0078] To estimate the lifespan of vehicle tires, the acceleration and deceleration of the tires can be calculated by statistically analyzing the usage of IPB and HCU. Based on the dynamic model, the tire lifespan can be predicted. The wear caused by braking can be calculated based on the braking duration statistics provided by IPB. This is then added to the wear caused by acceleration provided by HCU, and finally to the natural wear of the tires. The sum of these factors is compared with the tire lifespan, and the DMC reminds the driver to replace the tires in a timely manner, providing a safer guarantee for the driver.

[0079] The calculation processes for the aforementioned wear, acceleration / deceleration conditions, and dynamic models are all common knowledge in this field and will not be elaborated upon here.

[0080] Step 4.2: Complete Prediction and Evaluation of Current Journey: Obtain the predicted usage time and control range of each component for this journey, and compare it with the known life parameters of the components to determine whether the service life has been reached.

[0081] Step 4.2.1: Based on the planned optimal route and driver habit information, predict the component usage time and control amplitude after completing the entire route. The prediction model can be obtained by training the existing machine learning model using the driver's historical data.

[0082] Step 4.2.2: Overlay the predicted results onto the accumulated values ​​before departure, compare the accumulated results with the lifespan parameters, and determine whether the lifespan has been reached.

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

[0084] This invention maximizes energy utilization and improves driver comfort by controlling the minimum output of various vehicle components within the driver's habitual range. Furthermore, it enables travel prediction and assessment of the lifespan of various vehicle components, preventing situations where the vehicle is out of service and unable to be replaced even when its lifespan has reached a critical point, resulting in wasted resources and manpower if roadside assistance is required. This invention combines component travel prediction and assessment with real-time evaluation, further improving the utilization rate of vehicle components.

[0085] Example 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. This system is safe, reliable, and highly practical, and is applicable to any intelligent vehicle equipped with an autonomous driving system. It includes:

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

[0088] In this embodiment, the data acquisition equipment includes an infrared night vision device, millimeter-wave radar, a panoramic camera, a surround-view camera, a multi-functional forward-looking camera, and a lidar, all installed on the vehicle. The infrared night vision device, panoramic camera, surround-view camera, and multi-functional forward-looking camera transmit information via LVDS, the lidar transmits information via Ethernet, and the millimeter-wave radar transmits information via CAN FD.

[0089] Surrounding environment information includes information on obstacles around the vehicle and traffic signs. In this embodiment, larger objects on the road that affect traffic, such as vehicles, pedestrians, and curbs, are considered obstacles. Data acquisition equipment is used to collect information on the size and shape of obstacles around the vehicle.

[0090] An integrated control device is used to control each component and collect operating parameters, and then send the collected operating parameters to a central gateway.

[0091] The integrated control equipment includes: HCU (Hybrid Control Unit), IPB (Integrated Brake Controller Assembly), EPS (Electric Power Steering Controller), and DMC (Infotainment Domain Controller).

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

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

[0094] During the information exchange with the central gateway and the safety assessment of the vehicle based on the exchanged information, the HCU provides vehicle power, the IPB provides vehicle braking force, the EPS controls vehicle steering, and the DMC provides reminders to the driver. The autonomous driving domain controller collects the usage and design life of the HCU, IPB, and EPS and compares them to provide early reminders to the driver so that the driver can replace the parts in advance, providing safety assurance and travel reminders for the driver. The data is uploaded to the cloud via T-BOX for reference by other vehicles and for after-sales 4S stores to ensure sufficient stock.

[0095] Communication equipment is used to implement the system's communication functions. In this embodiment, the communication equipment is a T-BOX (TelematicBOX, Remote Information Processor). The navigation information is acquired as follows: the T-BOX transmits the vehicle's location information to the autonomous driving domain controller via Ethernet using the ADAS map function. The autonomous driving domain controller, through the collected surrounding environment information and the navigation information from the T-BOX, plans the vehicle's driving route and sends control signals to the integrated control device via the central gateway. The HCU controls the vehicle's energy output, the IPB controls the vehicle's braking system, the EPS controls the vehicle's steering, and the DMC controls the vehicle's display screen and warnings in case of danger.

[0096] In one specific implementation, the process of conducting a vehicle safety assessment based on interactive information includes:

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

[0098] (2) Current journey complete prediction and evaluation: Obtain the predicted usage time and control range of each component in this journey, and compare it 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, it records the usage duration and control amplitude of each control operation of the HCU, IPB, and EPS, such as the braking force of the IPB, the steering wheel angle of the EPS, and the acceleration provided by the HCU to the vehicle. Specifically, the HCU, IPB, and EPS transmit this information about the driver's usage duration and control amplitude to the autonomous driving domain controller via Ethernet through a gateway. The autonomous driving domain controller collects the cumulative counts when the autonomous driving function is activated and the information transmitted by the HCU, IPB, and EPS, and uses data methods to determine whether the HCU, IPB, and EPS have reached the end of their service life. If they have, it sends a reminder to the DMC via Ethernet through the gateway to prompt the driver to replace or repair the component.

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

[0101] Normal wear statistics under operating condition A: When the vehicle speed V≠0, the actual braking time received by the automatic driving controller from the IPB is accumulated, and all braking times are counted as effective braking.

[0102] Condition B includes wear statistics after the vehicle speed drops to 0: When the vehicle speed V drops to 0, the time it takes for the autopilot controller to send a braking command and the actual braking time of the IPB are statistically analyzed. Only the time from the start of braking until the vehicle speed reaches 0 is counted; the time after braking to 0 is not included in the effective duration. The sum of the two statistical results for Conditions A and B is compared with the brake pad lifespan. When the sum of the braking duration statistics for Conditions A and B approaches the brake pad lifespan, the autopilot controller sends an alarm to the DMC via Ethernet to remind the driver to replace the brake pads promptly. Simultaneously, the nearest 4S shop can be recommended for repair, and the customer's brake pad replacement information is synchronized to the corresponding 4S shop to prepare spare parts.

[0103] To estimate the lifespan of vehicle tires, the acceleration and deceleration of the tires can be calculated by statistically analyzing the usage of IPB and HCU. Based on the dynamic model, the tire lifespan can be predicted. The wear caused by braking can be calculated based on the braking duration statistics provided by IPB. This is then added to the wear caused by acceleration provided by HCU, and finally to the natural wear of the tires. The sum of these factors is compared with the tire lifespan, and the DMC reminds the driver to replace the tires in a timely manner, providing a safer guarantee for the driver.

[0104] like Figure 1 As shown, components such as HCU, IPB, and EPS transmit signals to the gateway via Ethernet. The gateway forwards the signals from each controller to the autonomous driving domain controller for collection. Using a driving habit model, it learns the driver's driving habits. When the user activates autonomous driving mode, the autonomous driving domain controller plans a route based on surrounding environmental information and preset navigation information. Simultaneously, based on the planned route and driving habits, it controls the output of HCU, IPB, and EPS under different operating conditions, and provides the driver with commonly used vehicle speeds, braking amplitudes, braking frequencies, and braking amplitudes. This achieves the goal of meeting the driver's habits while also saving vehicle energy, reducing IPB and tire wear, and increasing their service life.

[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 receiving this data, after-sales service can also prepare spare parts in advance to provide vehicles, thus more accurately meeting the needs of drivers.

[0106] Example 3:

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

[0108] The information acquisition module is configured to acquire information about the vehicle's surrounding environment and the historical operating information of each component.

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

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

[0111] The safety assessment module is configured to estimate the remaining lifespan of each component based on its real-time operating parameters, and to obtain the safety assessment prediction results for each component's trip.

[0112] Example 4:

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

[0114] Example 5:

[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, it implements the steps in the vehicle component management method based on autonomous driving as described in Embodiment 1 of the present invention.

[0116] The steps and methods involved in Examples 2, 3, 4 and 5 above correspond to those in Example 1. For specific implementation methods, please refer to the relevant description section of Example 1.

[0117] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this 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 without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

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

[0119] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A vehicle component management method based on autonomous driving, characterized in that, Includes the following steps: Acquire information about the vehicle's surrounding environment and the historical operating information of each component; By processing the historical operating information of each component using a driving habit model, driver habit information is obtained. The system generates an optimal route based on preset navigation information and surrounding environmental information, drives according to the optimal route, and adjusts the real-time operating parameters of each component according to driver habits and with the goal of minimizing output. The specific steps are as follows: calculate the output of each component based on the optimal route and with the goal of minimizing total output, and with driver habits as a constraint; adjust the real-time operating parameters of each component based on the calculation results. Based on the real-time operating parameters of each component, the remaining lifespan of the corresponding component is estimated, and the safety assessment prediction results of each component for each trip are obtained. The specific steps are: obtain the current cumulative usage time and control range of each component, and compare them with the known lifespan parameters of the component to determine whether the service life has been reached. The predicted usage time and control range of each component for this stroke are obtained and compared with the known life parameters of the components to determine whether the service life has been reached.

2. The vehicle component management method based on autonomous driving as described in claim 1, characterized in that, Surrounding environment information includes information on obstacles around the vehicle and traffic signs.

3. The vehicle component management method based on autonomous driving as described in claim 1, characterized in that, Driving habit information includes steering habits, braking habits, and speed habits.

4. The vehicle component management method based on autonomous driving as described in claim 3, characterized in that, The specific steps for processing historical operational information of each component using a driving habit model to obtain driver habit information are as follows: A driving habit model is constructed using a convolutional neural network; The driving habit model is trained using historical operating information of each component to obtain the driver's steering habit range, braking habit range, and vehicle speed habit range as driver habit information.

5. A management system for the vehicle component management method based on autonomous driving as described in any one of claims 1-4, characterized in that, include: Data acquisition equipment is used to collect information about the vehicle's surrounding environment and send that information to the autonomous driving domain controller. An integrated control device is used to control each component and collect operating parameters, and then send the collected operating parameters to the central gateway. The central gateway is used to receive operating parameters and interact with the autonomous driving domain controller. The autonomous driving domain controller is used to receive collected information about the surrounding environment, plan the driving trajectory based on the surrounding environment information and navigation information, and interact with the central gateway to conduct a safety assessment of the vehicle based on the interaction information. Communication equipment is used to enable the communication functions of a system.

6. A vehicle component management system based on autonomous driving, characterized in that, include: The information acquisition module is configured to acquire information about the vehicle's surrounding environment and the historical operating information of each component. The model processing module is configured to process the historical operating information of each component using a driving habit model to obtain driver habit information; The route planning module is configured to generate the optimal route based on preset navigation information and surrounding environmental information, drive according to the optimal route, and adjust the real-time operating parameters of each component according to driver habit information and with the goal of minimizing output. The specific steps are as follows: calculate the output of each component based on the optimal route with the goal of minimizing the total output and with driver habit information as a constraint; adjust the real-time operating parameters of each component based on the calculation results. The safety assessment module is configured to estimate the remaining lifespan of each component based on its real-time operating parameters, and to obtain the safety assessment prediction results for each component's trip. The specific steps are as follows: obtain the current cumulative usage time and control range of each component, and compare them with the known lifespan 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 lifespan parameters of the component to determine whether the service life has been reached.

7. A computer-readable storage medium, characterized in that, It stores multiple instructions, which are adapted to be loaded and executed by the processor of the terminal device, according to any one of claims 1-4, the vehicle component management method based on autonomous driving.

8. A terminal device, characterized in that, The method includes a processor and a computer-readable storage medium, wherein the processor implements various instructions; and the computer-readable storage medium stores multiple instructions adapted to be loaded by the processor and executed by the processor in any one of claims 1-4.

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