Vehicle avoidance method and system and storage medium

Abstract

The invention provides a vehicle avoidance method and system and a storage medium, and the method comprises the steps: pre-defining an avoidance strategy library, the avoidance strategy library comprises a plurality of avoidance strategies, and the vehicle avoidance method comprises the steps: obtaining emergency braking early warning information sent by a target vehicle in a preset range of a current vehicle; performing risk assessment prediction based on the emergency braking early warning information to obtain a prediction result; and acquiring an optimal avoidance strategy according to the prediction result and an avoidance strategy library, and controlling the current vehicle to perform avoidance based on the optimal avoidance strategy. According to the method provided by the invention, the emergency braking early warning information transmitted by the target vehicle is acquired in time, and the current vehicle decides the optimal avoidance strategy in time according to the emergency braking early warning information and performs avoidance, so that rear-end collision accidents caused by emergency braking are reduced, and the safety of road traffic is improved.

Description

Technical Field

[0001] The present application belongs to the field of automobile safe driving technology, and specifically relates to a vehicle avoidance method, system and storage medium. Background Art

[0002] With the continuous increase in the number of cars, road traffic safety issues have become increasingly prominent, especially the frequent rear-end collisions caused by emergency braking, which pose a serious threat to the safety of life and property of drivers and passengers. In the existing technology, vehicles mainly use brake lights, emergency brake assistance systems (such as AEB) and other methods to warn the rear vehicles, but these methods are limited in effect in complex traffic environments or under poor visibility conditions, and cannot ensure that all rear vehicles can receive emergency braking warning information in time. In addition, although some vehicles have been equipped with vehicle networking technology, the instant sharing of emergency braking information has not yet been widely realized, and collision accidents caused by information asymmetry cannot be effectively prevented. In the emergency braking situation, key information (such as vehicle location, speed, and driving direction) cannot be quickly transmitted to surrounding vehicles. After receiving the emergency braking signal, the rear vehicle is often unable to make effective pre-braking preparations or emergency handling due to the lack of sufficient warning time and detailed information. Summary of the invention

[0003] In order to solve the above technical problems, the present application proposes a vehicle avoidance method that can timely obtain emergency braking warning information of a target vehicle and perform emergency processing in a timely manner according to the emergency braking warning information.

[0004] Specifically, the present application proposes a vehicle avoidance method, wherein an avoidance strategy library is predefined, the avoidance strategy library includes multiple avoidance strategies, and the method includes: Obtain emergency braking warning information from a target vehicle within a preset range of the current vehicle; perform risk assessment prediction based on the emergency braking warning information to obtain prediction results; wherein the prediction results at least include the target vehicle status and collision risk within a preset time; and, obtain an optimal avoidance strategy based on the prediction results and an avoidance strategy library, and control the current vehicle to avoid the obstacle based on the optimal avoidance strategy.

[0005] In the above technical solution, by obtaining the emergency braking warning information issued by the target vehicle, it is possible to ensure that the current vehicle can receive the emergency braking warning information of the target vehicle in a timely manner in a complex traffic environment or when the visibility is poor. By performing risk assessment and prediction on the received emergency braking warning information, and obtaining the optimal avoidance strategy based on the prediction results and the pre-defined avoidance strategy library, the current vehicle can make an avoidance response in a timely manner after receiving the emergency braking warning information of the target vehicle, effectively avoiding or reducing rear-end collisions caused by emergency braking, and improving the safety of car driving.

[0006] As an implementation manner, before obtaining the emergency brake warning information issued by the target vehicle within the preset range of the current vehicle, the following may also be included: The AI ​​algorithm model is constructed based on historical scenario data information, historical risk assessment prediction results, and historical optimal avoidance strategies. Among them, the AI ​​algorithm model is constructed through historical data and risk assessment, and the best response strategy given by analyzing historical situations can make more accurate judgments and responses in similar situations, thereby improving the vehicle's avoidance ability in emergency situations and improving the accuracy and safety of decision-making.

[0007] Furthermore, the step of obtaining emergency braking warning information issued by a target vehicle within a preset range of the current vehicle includes: Acquiring emergency braking warning information issued by the target vehicle through wireless communication technology; The emergency braking warning information includes at least the emergency braking signal of the target vehicle and the first driving information of the target vehicle collected in real time using data desensitization technology.

[0008] Among them, the real-time collection of the first driving information through the data desensitization technology can desensitize personal privacy data in the data collection process to prevent personal information leakage. The wireless communication technology can be 5G communication technology, Bluetooth and WiFi, ensuring ultra-high-speed real-time transmission of emergency brake warning information and reducing information transmission delays. In addition, the wireless communication technology can be a variety of different communication technologies to ensure compatibility with most models on the market, thereby improving the popularity and compatibility of the technology.

[0009] Furthermore, the risk assessment prediction based on the emergency brake warning information includes: The second driving information of the current vehicle is collected in real time, and the emergency brake warning information is parsed to obtain the emergency brake signal and the first driving information. The second driving information and the first driving information are preprocessed to obtain current scene data. The AI ​​algorithm model is used to perform risk assessment prediction based on the current scene data to obtain a prediction result.

[0010] The accuracy and consistency of the data are ensured by preprocessing the second driving information and the first driving information. The risk assessment and prediction are performed by the AI ​​algorithm model, which improves the decision-making ability of the current vehicle to avoid collision risks and enhances the prediction accuracy of collision risks.

[0011] Further, the preprocessing of the second driving information and the first driving information includes: The second driving information and the first driving information are cleaned to obtain first scene data; avoidance decision data are filtered out based on the first scene data; each avoidance decision data is converted into a preset format to obtain second scene data; avoidance decision features are extracted according to the second scene data, and each avoidance decision feature is fused to obtain current scene data.

[0012] The data cleaning removes noise and irrelevant information from the second driving information and the first driving information, thereby ensuring the accuracy and consistency of the data. The data screening improves the pertinence and effectiveness of the avoidance decision data. The format conversion of each avoidance decision data enables the data to meet the needs of subsequent processing and analysis, thereby ensuring the consistency and compatibility of the information.

[0013] Furthermore, obtaining the optimal avoidance strategy according to the prediction result and the avoidance strategy library includes: An initial avoidance strategy is obtained from the avoidance strategy library according to the target vehicle state and collision risk in the prediction result, the vehicle motion trajectory corresponding to the initial avoidance strategy is simulated using an AI algorithm model, and the initial avoidance strategy is optimized based on the simulation result to obtain the optimal avoidance strategy.

[0014] By simulating the obtained initial avoidance strategy and optimizing the initial avoidance strategy based on the simulation results, the effect of the avoidance decision is improved, ensuring that the optimal avoidance strategy can better cope with the risk of collision, and improving the safety and driving reliability of the vehicle.

[0015] Furthermore, controlling the current vehicle to avoid based on the optimal avoidance strategy includes: Generate a control instruction according to the optimal avoidance strategy; verify the control instruction to determine whether the control instruction meets the vehicle control requirements, if yes, control the current vehicle to avoid according to the control instruction; otherwise, regenerate the control instruction for verification until it meets the vehicle control requirements.

[0016] The control instructions are generated according to the optimal avoidance strategy, which ensures that the control instructions are based on the optimal avoidance strategy, thereby improving the accuracy and effectiveness of the control instructions. The correctness of the control instructions and the safety of the avoidance process are ensured by verifying the control instructions, thereby improving the reliability and safety of the vehicle in avoiding collision risks.

[0017] Furthermore, in the process of controlling the current vehicle to avoid the obstacle, the following steps are also included: Collect feedback information of the current vehicle, evaluate the effect of the current avoidance process based on the feedback information, and obtain an evaluation result. Adjust and optimize the AI ​​algorithm model according to the evaluation result; wherein the feedback information at least includes the vehicle status, avoidance trajectory, avoidance effect and environmental information.

[0018] By evaluating the effect of the avoidance process, it is ensured that the actual effect of the avoidance strategy meets expectations. The AI ​​algorithm model is optimized according to the evaluation results to improve the accuracy of the AI ​​algorithm model and make the decision of the AI ​​algorithm model more accurate. Through continuous evaluation and optimization, the AI ​​algorithm model can improve itself and adapt to changing environments and different driving conditions, so that the vehicle can make better decisions in the face of emergencies, reduce potential risks, and enhance the safety and reliability of vehicle avoidance.

[0019] Based on the same inventive concept, the present application also proposes a vehicle avoidance system, the system comprising: The information acquisition module is used to obtain emergency brake warning information issued by the target vehicle within the preset range of the current vehicle.

[0020] A prediction module is used to perform risk assessment prediction based on the emergency braking warning information and obtain a prediction result; wherein the prediction result at least includes the target vehicle state and collision risk within a preset time.

[0021] A decision module is used to obtain an optimal avoidance strategy based on the prediction result and a predefined avoidance strategy library; the avoidance strategy library includes multiple avoidance strategies.

[0022] And, a control module is used to control the current vehicle to avoid based on the optimal avoidance strategy.

[0023] Based on the same inventive concept, the present application also proposes a computer-readable storage medium, which stores computer-executable instructions. The computer-executable instructions can be read by a domain controller and execute the vehicle avoidance method.

[0024] Compared with the prior art, this application has at least the following beneficial effects: This application obtains emergency braking warning information from target vehicles within the preset range of the current vehicle, performs risk assessment and prediction based on the emergency braking information, obtains the optimal avoidance strategy based on the obtained prediction results, and controls the current vehicle to avoid. It realizes the timely sharing of emergency braking information, effectively preventing rear-end collisions caused by untimely information transmission and the inability of the current vehicle to make effective emergency response after receiving the emergency braking warning information. Through risk assessment and prediction and controlling the current vehicle to avoid according to the optimal avoidance strategy, it is possible to effectively control the current vehicle to avoid in an emergency, thereby improving the safety and reliability of car driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a vehicle avoidance method shown in an embodiment of the present application.

[0026] Figure 2 It is a schematic diagram of a vehicle avoidance system shown in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] Embodiment 1: Please refer to Figure 1 , the vehicle avoidance method mainly includes: Acquire emergency braking warning information issued by a target vehicle within a preset range of the current vehicle. The target vehicle may be a vehicle that is in emergency braking in front of the current vehicle, and the preset range may be within 100 meters. For example, when a vehicle in front of the current vehicle within a range of 100 meters performs emergency braking, the vehicle in front issues emergency braking warning information, and the current vehicle acquires the emergency braking warning information in a timely manner.

[0029] Based on the emergency brake warning information, a risk assessment prediction is performed to obtain a prediction result; wherein the prediction result at least includes the target vehicle state and collision risk within a preset time. The target vehicle state may include the vehicle's motion trajectory and the vehicle's speed change, and the collision risk may include the collision probability, the predicted collision time, and the collision position.

[0030] And, obtaining an optimal avoidance strategy according to the prediction result and the avoidance strategy library, and controlling the current vehicle to avoid the vehicle based on the optimal avoidance strategy. The avoidance strategy library is mainly pre-set, and the avoidance strategy library mainly includes multiple avoidance strategies.

[0031] In some embodiments, before obtaining emergency brake warning information issued by a target vehicle within a preset range of the current vehicle, the method further includes: An AI algorithm model is constructed based on historical scenario data information, historical risk assessment prediction results, and historical optimal avoidance strategies.

[0032] Among them, the historical scene data information may include at least the current vehicle speed, the target vehicle speed, the current vehicle position, the target vehicle position and the road scene. The historical risk assessment prediction results may include at least the predicted collision risks and target vehicle states under different scenarios. The historical optimal avoidance strategy may include the most effective avoidance strategy adopted when the current vehicle previously faced similar risks, such as timely braking, acceleration avoidance and steering avoidance operations. The AI ​​algorithm model may adopt a machine learning model or a deep learning module. For example, the AI ​​algorithm model may adopt a decision tree, a random forest, a support vector machine and a neural network.

[0033] Optionally, the step of obtaining emergency braking warning information issued by a target vehicle within a preset range of the current vehicle includes: Acquiring emergency braking warning information issued by the target vehicle through wireless communication technology; The emergency braking warning information includes at least the emergency braking signal of the target vehicle and the first driving information of the target vehicle collected in real time using data desensitization technology.

[0034] Among them, the data desensitization technology avoids the leakage of personal privacy data during the data collection process, and the first driving information may at least include the position, driving direction and speed of the target vehicle. The wireless communication technology may mainly be 5G communication technology, Bluetooth and WiFi. For example, the current vehicle receives emergency braking warning information from the target vehicle in front through 5G communication technology, and takes corresponding avoidance measures according to the emergency braking warning information.

[0035] Optionally, the risk assessment prediction based on the emergency braking warning information may include: The second driving information of the current vehicle is collected in real time, and the emergency braking warning information is analyzed to obtain the emergency braking signal and the first driving information; the second driving information and the first driving information are preprocessed to obtain current scene data; and the AI ​​algorithm model is used to perform risk assessment and prediction based on the current scene data to obtain a prediction result.

[0036] Among them, the second driving information may mainly include the speed, acceleration, driving direction and current position of the current vehicle. After preprocessing the first driving information and the second driving information, the current scene data obtained may mainly include information such as the current target vehicle position, the current vehicle position, the current vehicle speed, acceleration, distance and weather. The current scene data is input into the AI ​​algorithm model, and the AI ​​algorithm model analyzes the current scene data to evaluate the collision risk under the current circumstances. For example, evaluate the current collision probability, predict the collision time and the location of the collision site. Then, based on the current vehicle status and risk assessment results, predict changes in the target vehicle status and collision risk.

[0037] Optionally, the preprocessing the second driving information and the first driving information may include: The second driving information and the first driving information are cleaned to obtain first scene data; avoidance decision data are filtered out based on the first scene data; each avoidance decision data is converted into a preset format to obtain second scene data; avoidance decision features are extracted according to the second scene data, and each avoidance decision feature is fused to obtain current scene data.

[0038] The data cleaning at least includes removing abnormal data caused by sensor failure or signal interference in the first driving information and the second driving information. For example, check whether the acceleration and speed data exceed the physical limit or whether the position information is reasonable, such as checking whether the vehicle position is in the road grid. The data screening at least includes screening data for emergency braking and avoidance decisions according to emergency braking characteristics, such as the current speed of the vehicle, the rate of change of acceleration, the braking force and the braking time. The conversion of each avoidance decision data into a preset format can mainly be to convert data from different sources and different formats into a unified format. The avoidance decision features can at least include the vehicle's instantaneous speed, the rate of change of acceleration, the distance between the current vehicle and the target vehicle, the road type and the road traffic conditions.

[0039] Optionally, obtaining an optimal avoidance strategy according to the prediction result and an avoidance strategy library includes: An initial avoidance strategy is obtained from the avoidance strategy library according to the target vehicle state and collision risk in the prediction result; the vehicle motion trajectory corresponding to the initial avoidance strategy is simulated using an AI algorithm model, and the initial avoidance strategy is optimized based on the simulation result to obtain the optimal avoidance strategy.

[0040] The avoidance strategy library is pre-set and includes avoidance strategies for multiple different scenarios. For example, multiple avoidance strategies for deceleration, lane change, and emergency braking. By simulating the selected initial avoidance strategy through the AI ​​algorithm model, the movement trajectory and collision risk of the vehicle under the selected initial avoidance strategy can be observed through simulation. The initial avoidance strategy is optimized according to the simulation results to reduce the collision risk of the selected avoidance decision and improve the safety and reliability of the avoidance strategy.

[0041] Optionally, controlling the current vehicle to avoid based on the optimal avoidance strategy may include: Generate a control instruction according to the optimal avoidance strategy. Verify the control instruction to determine whether the control instruction meets the vehicle control requirements. If yes, control the current vehicle to avoid according to the control instruction; otherwise, regenerate the control instruction for verification until it meets the vehicle control requirements.

[0042] The control instruction may be a deceleration instruction, a lane change instruction or an emergency braking instruction. The control instruction is verified to ensure that the control instruction meets the control requirements of the vehicle. The control requirements may mainly include the vehicle's movement ability, control accuracy and safety restrictions. After passing the inspection, the control instruction may be sent to the vehicle system control unit, and after receiving the control instruction, the vehicle system control unit performs corresponding control operations to control the current vehicle to avoid effectively. For example, an emergency braking instruction is generated based on the obtained optimal avoidance strategy, and after verifying the emergency braking instruction, it is determined that the emergency braking instruction meets the vehicle control requirements, and then the current vehicle is controlled to perform emergency braking to avoid collision according to the emergency braking instruction.

[0043] Optionally, the process of controlling the current vehicle to avoid the obstacle may also include: Collect feedback information of the current vehicle, evaluate the effect of the current avoidance process based on the feedback information, and obtain an evaluation result; adjust and optimize the AI ​​algorithm model according to the evaluation result; wherein the feedback information at least includes the vehicle status, avoidance trajectory, avoidance effect and environmental information.

[0044] In the process of controlling the current vehicle to avoid, the vehicle status in the feedback information includes at least the vehicle speed, acceleration, driving direction and braking status, and the avoidance effect includes at least the avoidance success rate; the environmental information includes at least the road conditions, vehicle position and weather conditions. By evaluating the effect of the current avoidance process through the collected feedback information, the success rate and improvement points of the current avoidance measures can be analyzed; the AI ​​algorithm model can be optimized according to the improvement points to make the avoidance decision more accurate.

[0045] Taking the case where the road is slippery due to rain and the front vehicle has emergency braking as an example, the front vehicle broadcasts the emergency braking warning information through 5G communication technology. After the current vehicle receives the emergency braking warning information, it parses the emergency braking warning information to obtain the speed, acceleration and distance of the front vehicle from the current vehicle, and pre-processes the front vehicle information and the current vehicle driving information obtained by the analysis to obtain the current scene data. The current scene data is input into the pre-built AI algorithm model, and the risk assessment prediction is performed through the AI ​​algorithm model. The prediction result obtained is that the collision risk is 80%. According to the prediction result, the initial avoidance strategy is selected as the emergency braking strategy in the preset strategy library, and the emergency braking strategy is simulated and optimized through the AI ​​algorithm model to obtain the optimal avoidance strategy. The optimal avoidance strategy mainly includes the minimum acceleration, braking distance and braking time of emergency braking. The control instruction is generated through the optimal avoidance strategy. The control instruction is an emergency braking instruction. According to the emergency braking instruction, the current vehicle is controlled to perform emergency braking to avoid collision. The feedback information of the braking process is collected and the effect is evaluated. According to the evaluation result, the AI ​​algorithm model is optimized to improve the accuracy of avoidance.

[0046] Embodiment 2: Please refer to Figure 2 , the present application also proposes a vehicle avoidance system, which mainly includes: an information acquisition module, a prediction module, a decision module and a control module.

[0047] Among them, the information acquisition module is used to obtain the emergency braking warning information issued by the target vehicle within the preset range of the current vehicle. The target vehicle detects the emergency braking signal of the vehicle through the on-board emergency braking signal detection module, obtains the vehicle position, driving direction and speed information in real time through the GPS positioning module, and integrates the emergency braking signal, vehicle position, driving direction and speed information through the data processing module to obtain the emergency braking warning information. The emergency braking warning information is then broadcast and sent through the wireless communication module. Among them, the wireless communication module adopts at least 5G communication technology, Bluetooth and WiFi wireless communication technology.

[0048] A prediction module is used to perform risk assessment prediction based on the emergency braking warning information and obtain prediction results; wherein the prediction results at least include the target vehicle state and collision risk within a preset time. After receiving the emergency braking information, the current vehicle parses the emergency braking information, pre-processes the target vehicle driving information obtained by the analysis and the current vehicle driving information, and inputs the scene data obtained after the pre-processing into the AI ​​algorithm model for risk assessment prediction. The collision risk may at least include the collision probability, the predicted collision time and the collision location.

[0049] A decision module is used to obtain the optimal avoidance strategy according to the prediction results and a predefined avoidance strategy library; the avoidance strategy library includes multiple avoidance strategies. After the initial avoidance strategy is selected according to the prediction results and the avoidance strategy library, the initial avoidance strategy is simulated by the AI ​​algorithm model, and the initial avoidance strategy is optimized according to the simulation results to obtain the optimal avoidance strategy. For example, according to the simulation of the AI ​​algorithm model, it is found that the acceleration of the vehicle in the selected initial avoidance strategy can be increased, which can improve the avoidance efficiency of the vehicle. Therefore, the acceleration of the vehicle in the initial avoidance strategy is adjusted according to the simulation results to obtain the optimal avoidance strategy.

[0050] and a control module, for controlling the current vehicle to avoid the target vehicle based on the optimal avoidance strategy. A control instruction is generated by the optimal avoidance strategy, and the current vehicle is controlled to avoid the target vehicle by the control instruction. For example, if the optimal avoidance strategy is to decelerate and change lanes to the right, the current vehicle is controlled to decelerate and change lanes to the right according to the generated control instruction to avoid collision with the target vehicle.

[0051] Embodiment three: The present application also proposes a computer-readable storage medium, the computer-readable storage medium comprising: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions implement the vehicle avoidance method when executed by a control processor.

[0052] In the computer-readable storage medium, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidState Disk (SSD)), etc.

[0053] In summary, the present application timely receives the emergency braking warning information from the target vehicle and performs corresponding vehicle avoidance control according to the emergency braking information. It realizes the timely sharing of emergency braking information and effectively prevents rear-end collisions caused by untimely information transmission and the inability of the current vehicle to make effective emergency response after receiving the emergency braking warning information. Through risk assessment prediction and controlling the current vehicle to avoid according to the optimal avoidance strategy, it realizes the effective control of the current vehicle to avoid in an emergency, thereby improving the safety and reliability of car driving.

[0054] In several embodiments provided in the present application, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0055] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially or in other words, the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for an electronic device to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0056] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A vehicle avoidance method, characterized in that: An avoidance strategy library is predefined, the avoidance strategy library includes multiple avoidance strategies, and the vehicle avoidance method includes: Obtain emergency brake warning information from target vehicles within the preset range of the current vehicle; Perform risk assessment prediction based on the emergency braking warning information to obtain prediction results; wherein the prediction results at least include the target vehicle state and collision risk within a preset time; And, obtaining an optimal avoidance strategy according to the prediction result and the avoidance strategy library, and controlling the current vehicle to avoid the obstacle based on the optimal avoidance strategy.

2. The vehicle avoidance method according to claim 1, characterized in that: Before obtaining the emergency brake warning information issued by the target vehicle within the preset range of the current vehicle, it also includes: An AI algorithm model is constructed based on historical scenario data information, historical risk assessment prediction results, and historical optimal avoidance strategies.

3. The vehicle avoidance method according to claim 1, characterized in that: The step of obtaining emergency brake warning information issued by a target vehicle within a preset range of the current vehicle includes: Acquiring emergency braking warning information issued by the target vehicle through wireless communication technology; The emergency braking warning information includes at least the emergency braking signal of the target vehicle and the first driving information of the target vehicle collected in real time using data desensitization technology.

4. The vehicle avoidance method according to claim 2, characterized in that: The risk assessment prediction based on the emergency brake warning information includes: collecting the second driving information of the current vehicle in real time, and analyzing the emergency braking warning information to obtain the emergency braking signal and the first driving information; preprocessing the second driving information and the first driving information to obtain current scene data; The AI ​​algorithm model is used to perform risk assessment prediction based on the current scenario data to obtain a prediction result.

5. The vehicle avoidance method according to claim 4, characterized in that: The preprocessing of the second driving information and the first driving information includes: performing data cleaning on the second driving information and the first driving information to obtain first scene data; Filtering avoidance decision data based on the first scenario data; Convert each avoidance decision data into a preset format to obtain second scenario data; Avoidance decision features are extracted according to the second scene data, and the avoidance decision features are fused to obtain current scene data.

6. The vehicle avoidance method according to claim 1, characterized in that: The obtaining of the optimal avoidance strategy according to the prediction result and the avoidance strategy library includes: Acquiring an initial avoidance strategy from the avoidance strategy library according to the target vehicle state and collision risk in the prediction result; The AI ​​algorithm model is used to simulate the vehicle motion trajectory corresponding to the initial avoidance strategy, and the initial avoidance strategy is optimized based on the simulation results to obtain the optimal avoidance strategy.

7. The vehicle avoidance method according to claim 1, characterized in that: The controlling the current vehicle to avoid based on the optimal avoidance strategy includes: generating a control instruction according to the optimal avoidance strategy; The control instruction is verified to determine whether the control instruction meets the vehicle control requirements. If so, the current vehicle is controlled to avoid the obstacle according to the control instruction; otherwise, the control instruction is regenerated for verification until it meets the vehicle control requirements.

8. The vehicle avoidance method according to claim 1, characterized in that: The process of controlling the current vehicle to avoid a maneuver also includes: Collect feedback information of current vehicles; Based on the feedback information, an effect evaluation is performed on the current avoidance process to obtain an evaluation result; Adjust and optimize the AI ​​algorithm model according to the evaluation results; The feedback information at least includes the vehicle status, avoidance trajectory, avoidance effect and environmental information.

9. A system based on the vehicle avoidance method according to any one of claims 1 to 8, characterized in that: The system comprises: An information acquisition module is used to obtain emergency brake warning information issued by a target vehicle within a preset range of the current vehicle; A prediction module, configured to perform risk assessment prediction based on the emergency braking warning information and obtain a prediction result; wherein the prediction result at least includes the target vehicle state and collision risk within a preset time; A decision module, used for obtaining an optimal avoidance strategy according to the prediction result and a predefined avoidance strategy library; the avoidance strategy library includes a plurality of avoidance strategies; And, a control module is used to control the current vehicle to avoid based on the optimal avoidance strategy.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that: When the computer executable instructions are executed by the control processor, the vehicle avoidance method as described in any one of claims 1-8 is implemented.

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