AEB system safety efficacy quantitative evaluation method and system

Abstract

The invention provides an AEB system safety efficacy quantitative evaluation method and system, and relates to the technical field of road traffic safety. Comprising the following steps: acquiring driving reaction behavior characteristic data, and constructing a dangerous situation driver reaction behavior model; acquiring accident investigation data, constructing a typical accident reproduction scene, and reconstructing the accident reproduction scene; coupling a driver reaction behavior model and an AEB system algorithm to be applied to participating vehicles in the reconstructed accident reproduction scene, and performing simulation operation to obtain simulation data; and based on the simulation data, calculating and comparing a collision avoidance percentage, a reduction percentage of each damage severity and a minimum vehicle distance cumulative distribution curve, and comprehensively evaluating the safety performance of the AEB system. According to the invention, accurate and comprehensive evaluation of AEB benefits can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of road traffic safety, and in particular to a method and system for quantitatively evaluating the safety efficacy of an AEB system. Background Art

[0002] With the rapid development of the automobile industry and the increasing demand for traffic safety, the automatic emergency braking system (AEB) has become an important part of modern automobile active safety technology. The AEB system monitors the road conditions ahead in real time through sensors such as radar and cameras, and can automatically take braking measures when potential collision risks are detected, thereby reducing or avoiding collision accidents.

[0003] With the development of technologies such as big data and artificial intelligence, AEB technology has also developed rapidly. At present, the effectiveness of AEB systems is mainly based on historical accident statistics, simulation experiments, real vehicle tests and user feedback surveys, and is evaluated through indicators such as accident rate, casualty rate, system response time, and false alarm rate. However, based on historical accidents and user feedback, there are problems such as high evaluation costs and long time periods.

[0004] Although the evaluation test based on traffic simulation is low-cost and efficient, the simulation scenario cannot be combined with the real accident scenario, and there is a problem of poor reliability. With the development of big data and artificial intelligence technology, the accuracy of accident data collection continues to increase and the reliability of simulation software is gradually enhanced. Compared with previous safety assessment technologies, counterfactual simulation has the advantages of close integration with accident scenarios and high credibility. Counterfactual simulation usually includes mathematical models of drivers, vehicles and the environment, which have an important impact on the accurate evaluation of AEB benefits. Although the driver model is important, to date, the driver model has the lowest maturity among the models required for counterfactual simulation. In addition, the evaluation of the AEB system only stays on whether the collision is avoided, and no comprehensive quantitative evaluation of the safety efficacy of the AEB system is conducted in terms of the severity of the collision. The comprehensiveness and accuracy of the evaluation need to be considered. Summary of the invention

[0005] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method and system for quantitatively evaluating the safety efficacy of an AEB system. By constructing a more accurate driver reaction behavior model and combining it with a virtual simulation test scenario that takes into account the differences in road conditions and vehicle conditions, the driver, vehicle and environment are accurately modeled to achieve an accurate evaluation of the AEB benefits. The safety effectiveness of the AEB system is comprehensively evaluated from the collision avoidance percentage, the percentage reduction in severity of each injury and the cumulative distribution curve of the minimum vehicle spacing, thereby improving the comprehensiveness of the evaluation.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0007] A first aspect of the present invention provides a method for quantitatively evaluating the safety efficacy of an AEB system.

[0008] The quantitative evaluation method of the safety efficacy of the AEB system includes the following steps:

[0009] Obtain driving reaction behavior characteristic data and build a driver reaction behavior model in dangerous situations;

[0010] Obtain accident investigation data, construct typical accident reconstruction scenarios, and reconstruct accident reconstruction scenarios;

[0011] The driver reaction behavior model and the AEB system algorithm are coupled and applied to the participating vehicles in the reconstructed accident reconstruction scene to perform simulation operations and obtain simulation data;

[0012] Based on the simulation data, the collision avoidance percentage, the reduction percentage of each damage severity and the cumulative distribution curve of the minimum vehicle spacing are calculated to comprehensively evaluate the safety effectiveness of the AEB system.

[0013] A second aspect of the present invention provides a system for quantitatively evaluating the safety effectiveness of an AEB system.

[0014] AEB system safety efficacy quantitative evaluation system, including:

[0015] The driver reaction behavior model building module is configured to: obtain driving reaction behavior characteristic data and build a driver reaction behavior model for dangerous situations;

[0016] The accident reconstruction scene reconstruction module is configured to: obtain accident investigation data, construct a typical accident reconstruction scene, and reconstruct the accident reconstruction scene;

[0017] The simulation module is configured to: couple the driver reaction behavior model and the AEB system algorithm to the participating vehicles in the reconstructed accident reconstruction scene, perform simulation calculations, and obtain simulation data;

[0018] The evaluation module is configured to: calculate and compare the collision avoidance percentage, the reduction percentage of each damage severity and the cumulative distribution curve of the minimum vehicle spacing based on the simulation data, and comprehensively evaluate the safety effectiveness of the AEB system.

[0019] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the method for quantitatively evaluating the safety efficacy of an AEB system as described in the first aspect of the present invention.

[0020] The fourth aspect of the present invention provides an electronic 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 method for quantitatively evaluating the safety efficacy of the AEB system as described in the first aspect of the present invention are implemented.

[0021] One or more of the above technical solutions have the following beneficial effects:

[0022] The present invention provides a method and system for quantitatively evaluating the safety efficacy of an AEB system. The method constructs a driver behavior model for dangerous scenarios through natural driving data, and uses the inverse reasoning theory to combine the reconstructed scenario based on refined accident investigation data with simulation technology to reconstruct the accident reconstructed scenario. By replacing the driver reaction behavior model in the original accident scenario with the components of the AEB system, a virtual simulation test scenario that takes into account differences in road conditions is constructed.

[0023] The severity of the driver's injuries in the simulation environment is predicted based on the collision speed, and the accident avoidance percentage and the reduction percentage of each injury severity in the simulation event are extracted, ultimately achieving a quantitative evaluation of the safety effectiveness of the AEB system.

[0024] When constructing the driver reaction behavior model, the present invention takes into account the driving reaction behavior characteristic data including reaction time, deceleration change rate, maximum steering wheel angle, steering wheel speed and maximum braking deceleration. The constructed driver reaction behavior model is more mature, so that it can better fit the driver's real dangerous situation reaction in the real scene. When constructing a typical accident reproduction scene, the present invention determines the type of vehicle involved in the accident and the vehicle performance simulation model based on the accident investigation data; based on the road surface type, slope and road adhesion coefficient of the accident site, the road environment parameters of the accident site are determined, so as to construct a virtual simulation test scene that takes into account the differences in road conditions and vehicle conditions. Through the above-mentioned precise modeling of drivers, vehicles and environments, the present invention can achieve accurate evaluation of AEB benefits.

[0025] The present invention calculates and compares the collision avoidance percentage, the reduction percentage of each damage severity and the cumulative distribution curve of the minimum vehicle spacing based on simulation data, and comprehensively evaluates the safety effectiveness of the AEB system, which can improve the comprehensiveness of the evaluation.

[0026] 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

[0027] 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.

[0028] Figure 1 This is a flow chart of the method of the first embodiment.

[0029] Figure 2 It is a simulation framework diagram of the first embodiment. DETAILED DESCRIPTION

[0030] 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.

[0031] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0032] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0033] Embodiment 1

[0034] This embodiment discloses a method for quantitatively evaluating the safety efficacy of an AEB system.

[0035] like Figure 1 and Figure 2 As shown, the quantitative evaluation method of the safety efficacy of the AEB system includes the following steps:

[0036] Obtain driving reaction behavior characteristic data and build a driver reaction behavior model in dangerous situations;

[0037] Obtain accident investigation data, construct typical accident reconstruction scenarios, and reconstruct accident reconstruction scenarios;

[0038] The driver reaction behavior model and the AEB system algorithm are coupled and applied to the participating vehicles in the reconstructed accident reconstruction scene to perform simulation operations and obtain simulation data;

[0039] Based on the simulation data, the collision avoidance percentage, the reduction percentage of each damage severity and the cumulative distribution curve of the minimum vehicle spacing are calculated and compared to comprehensively evaluate the safety effectiveness of the AEB system.

[0040] The quantitative evaluation method for the safety efficacy of the AEB system proposed in this embodiment obtains a large amount of driving behavior characteristic data through natural driving, and constructs a driver reaction behavior model for dangerous scenarios; constructs a typical accident reproduction scenario based on detailed accident investigation data; builds an AEB system algorithm model; couples the driver reaction behavior model with the AEB system algorithm and applies it to vehicles involved in accidents in dangerous scenarios; performs batch simulation operations to count whether a collision occurs, and for collision accidents, constructs a damage severity prediction model, and quantitatively evaluates the safety efficacy of the AEB system based on evaluation indicators such as accident avoidance percentage and collision damage severity reduction percentage, so as to facilitate comparative analysis with the original accident results or other AEB system statistical results.

[0041] In some embodiments, obtaining driving reaction behavior characteristic data and constructing a driver reaction behavior model for dangerous situations specifically includes:

[0042] Obtain the driving behavior characteristic data of the driver's natural driving state to dangerous situations, including reaction time, deceleration change rate, maximum steering wheel angle, steering wheel speed and maximum braking deceleration;

[0043] Construct the distribution curve of each driver's reaction behavior characteristics, select the threshold for identifying bad driving reaction behavior characteristics, and construct the driver's risk behavior interval;

[0044] The driver's reaction behavior characteristic parameters are randomly selected to construct a driver's reaction behavior model including reaction time, deceleration change rate, maximum steering wheel angle, steering wheel speed and maximum braking deceleration.

[0045] Furthermore, the process of building the driver's reaction behavior model includes:

[0046] Under natural driving conditions, data acquisition equipment such as eye trackers, driving posture sensors HWT9073-CAN and steering wheel angle meters are used to accumulate the driver's reaction time, deceleration change rate, maximum steering wheel angle and steering wheel speed, and maximum braking deceleration characteristic parameters in dangerous situations during daily driving;

[0047] Based on the large sample statistical analysis method, the distribution curve of each driver's reaction behavior characteristics is constructed. The univariate POT model in extreme value statistical theory is used. The graphical method is used to select the discrimination threshold of bad driving behavior characteristics through the average life curve and threshold stability diagram, and the driver's risk behavior interval is constructed.

[0048] The Monte Carlo method is used to randomly select the characteristic parameters of the driver's reaction behavior, and a numerical model of the driver's reaction behavior including reaction time, deceleration change rate, maximum braking deceleration, maximum steering wheel angle and speed is constructed, wherein the reaction time includes braking reaction time and steering reaction time. The specific numerical model of the driver's reaction behavior is that for braking behavior, starting from the moment when the AEB system triggers the warning, the driver adopts a fixed deceleration change rate of braking after a certain braking reaction time, and maintains the deceleration until the maximum braking deceleration is reached until the end of the simulation. If there is a braking steering behavior, in addition to continuing the above braking process, starting from the moment when the AEB system triggers the warning, the driver adopts a fixed speed steering after a certain steering reaction time, and maintains this state until the maximum steering wheel angle is reached until the end of the simulation.

[0049] Based on the statistical analysis of the original accident data, the steering probability percentage is calculated and randomly assigned to the sample of the numerical model of the driver's reaction behavior. Therefore, the driver's risk avoidance behavior is divided into two categories: braking only and braking steering. In the dangerous scenario, if there is braking steering behavior, the driver needs to decelerate to the maximum braking deceleration at a constant deceleration rate after the AEB system alarm after the braking reaction time, and maintain the maximum braking deceleration until the end of the simulation; at the same time, after the steering reaction time, the driver reaches the maximum steering wheel angle at a constant speed, and maintains the steering wheel angle until the end of the simulation. Braking only does not involve steering operation.

[0050] In some embodiments, accident investigation data is obtained and a typical accident reconstruction scenario is constructed, specifically:

[0051] Select accident-frequently occurring scenes as typical accident reproduction scenes;

[0052] Determine the types of vehicles involved in the accident and the vehicle performance simulation model based on the accident investigation data;

[0053] Based on the road surface type, slope and road adhesion coefficient at the accident site, the road environment parameters at the accident site are determined to construct a typical accident reconstruction scenario.

[0054] Furthermore, the typical accident scene is reproduced, and the process includes:

[0055] In this embodiment, based on the statistical analysis of accident data, accident-frequently occurring scenes are selected, and the motion state of the accident participants in the first 5 seconds before the collision is reconstructed with the help of accident reconstruction technology or driving simulation technology. It includes the type of vehicles involved in the accident and the simulation model of vehicle performance, and the road environment parameters are calibrated based on the survey data of the road surface type, slope and road adhesion coefficient at the accident site.

[0056] The accident simulation scene is reconstructed using the inverse reasoning theory, keeping the temporal motion state of the front vehicle unchanged, deleting the original data on the reaction to the rear-end collision risk and the resulting change in the vehicle's motion state during the temporal motion of the rear vehicle in the rear-end collision accident, and maintaining the vehicle's horizontal position and speed when the driver begins the initial reaction action.

[0057] For the problem of prolonged movement time of the leading vehicle due to the change of simulation time:

[0058] The speed of the preceding vehicle is inferred from the average acceleration in the 0.6-0.1s time interval before the original collision:

[0059] If the acceleration of the leading vehicle is negative during this period, the speed of the leading vehicle after the original collision point until it stops is calculated by decelerating at a constant deceleration based on the speed at the original collision point;

[0060] If the acceleration is positive during this period, the front vehicle will maintain its original speed at the time of the accident collision and travel at a constant speed until the end of the simulation.

[0061] In some embodiments, the driver reaction behavior model and the AEB system algorithm are coupled and applied to the participating vehicles in the reconstructed accident reconstruction scene to perform simulation operations and obtain simulation data, specifically:

[0062] On the basis of maintaining the horizontal position and speed of the rear vehicle at the beginning of the driver's initial reaction action, the driver's reaction behavior model and the AEB system algorithm are coupled and applied to the rear vehicle for simulation calculation;

[0063] During the simulation, the data of the distance between the two vehicles, their speed, acceleration, and whether a collision occurs over time are collected as simulation data;

[0064] The simulation is terminated when the two vehicles collide, the simulation runs until the rear vehicle completely stops, or the simulation runs for a set time.

[0065] Furthermore, the above process includes:

[0066] The driver reaction behavior model is coupled with the AEB system to be tested and applied to the rear vehicle test vehicle to act on the motion state of the rear vehicle. Since there are hundreds of potential results for the simulation of a single original collision, the amount of calculation is too large, so the Monte Carlo method is used for sensitivity analysis to determine the minimum number of samples required for the simulation to obtain stable results. Then the simulation operation is performed based on the minimum number of samples. There are two situations in which the simulation is terminated for each event. Once the two vehicles collide, the simulation is terminated; if the two vehicles do not collide, the simulation runs until the rear vehicle is completely stopped or the simulation lasts for 30 seconds.

[0067] During the simulation, the vehicle spacing, speed, acceleration, and collision data of the two vehicles are collected over time. Then, according to whether a collision occurs, the collision avoidance percentage is analyzed, that is, the ratio of the number of collision events to the total number of simulation events. If no collision event occurs, the minimum vehicle spacing between the two vehicles is obtained and the cumulative distribution curve is drawn. The severity of driver injury is predicted based on the collision speed, and the dose-response model is used to evaluate the safety benefits of AEB.

[0068] In this embodiment, the number of rear vehicle drivers who suffered injuries of a certain severity (fatal, serious, or minor) is represented by E. s express:

[0069]

[0070] Where v is the speed of the vehicle at the time of collision, in km / h; f(v) is the collision frequency at the collision speed v; r s (v) is the risk of the driver suffering a certain injury severity s (death, serious, minor injury), which can be obtained based on historical rear-end collision accident data; L is the highest collision speed in all collision events.

[0071] Assuming that the percentage reduction in drivers with accident injury level s after the implementation of the tested AEB system is R, it is calculated by the following formula:

[0072]

[0073] Among them, E o is the number of people with injury level s when the AEB system is not applied, E w It is the number of people when the injury level is s when the vehicle is equipped with an AEB system; for accidents in which collisions have been avoided, the driver is considered to be uninjured.

[0074] The safety effectiveness of the AEB system is comprehensively evaluated by comprehensively comparing the collision avoidance percentage, the reduction percentage of each injury severity and the cumulative distribution curve of the minimum vehicle spacing.

[0075] Embodiment 2

[0076] This embodiment discloses a system for quantitatively evaluating the safety effectiveness of an AEB system.

[0077] AEB system safety efficacy quantitative evaluation system, including:

[0078] The driver reaction behavior model building module is configured to: obtain driving reaction behavior characteristic data and build a driver reaction behavior model for dangerous situations;

[0079] The accident reconstruction scene reconstruction module is configured to: obtain accident investigation data, construct a typical accident reconstruction scene, and reconstruct the accident reconstruction scene;

[0080] The simulation module is configured to: couple the driver reaction behavior model and the AEB system algorithm to the participating vehicles in the reconstructed accident reconstruction scene, perform simulation calculations, and obtain simulation data;

[0081] The evaluation module is configured to: calculate and compare the collision avoidance percentage, the reduction percentage of each damage severity and the cumulative distribution curve of the minimum vehicle spacing based on the simulation data, and comprehensively evaluate the safety effectiveness of the AEB system.

[0082] Embodiment 3

[0083] The purpose of this embodiment is to provide a computer-readable storage medium.

[0084] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for quantitatively evaluating the safety efficacy of an AEB system as described in Example 1 of the present disclosure.

[0085] Embodiment 4

[0086] The purpose of this embodiment is to provide an electronic device.

[0087] An electronic device comprising a memory, a processor and a program stored in the memory and executable on the processor, wherein the processor

[0088] When the program is executed, the steps in the method for quantitatively evaluating the safety efficacy of the AEB system as described in Example 1 of the present disclosure are implemented.

[0089] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0090] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0091] 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.

Claims

1. A quantitative evaluation method for the safety efficacy of an AEB system, characterized in that: The following steps are involved: Obtain driving reaction behavior characteristic data and build a driver reaction behavior model in dangerous situations; Obtain accident investigation data, construct typical accident reconstruction scenarios, and reconstruct accident reconstruction scenarios; The driver reaction behavior model and the AEB system algorithm are coupled and applied to the participating vehicles in the reconstructed accident reconstruction scene to perform simulation operations and obtain simulation data; Based on the simulation data, the collision avoidance percentage, the reduction percentage of each damage severity and the cumulative distribution curve of the minimum vehicle spacing are calculated and compared to comprehensively evaluate the safety effectiveness of the AEB system.

2. The AEB system safety efficacy quantitative evaluation method according to claim 1, characterized in that: Obtain driving reaction behavior characteristic data and build a driver reaction behavior model for dangerous situations, specifically: Obtain the driving behavior characteristic data of the driver's natural driving state to dangerous situations, including reaction time, deceleration change rate, maximum steering wheel angle, steering wheel speed and maximum braking deceleration; Construct the distribution curve of each driver's reaction behavior characteristics, select the threshold for identifying bad driving reaction behavior characteristics, and construct the driver's risk behavior interval; The driver's reaction behavior characteristic parameters are randomly selected to construct a driver's reaction behavior model including reaction time, deceleration change rate, maximum steering wheel angle, steering wheel speed and maximum braking deceleration.

3. The AEB system safety efficacy quantitative evaluation method according to claim 2, characterized in that: The reaction time includes braking reaction time and steering reaction time. The corresponding driver's risk avoidance behaviors in the simulation process are braking only and braking steering, where: If there is braking steering behavior, the driver needs to decelerate to the maximum braking deceleration at a constant deceleration rate after the braking reaction time, and maintain the maximum braking deceleration until the end of the simulation; At the same time, after the steering reaction time, the driver turns the steering wheel at a constant speed to the maximum steering wheel angle, and maintains the maximum steering wheel angle until the end of the simulation.

4. The AEB system safety efficacy quantitative evaluation method according to claim 1, characterized in that: Obtain accident investigation data and construct typical accident reconstruction scenarios, specifically: Select accident-frequently occurring scenes as typical accident reproduction scenes; Determine the types of vehicles involved in the accident and the vehicle performance simulation model based on the accident investigation data; Based on the road surface type, slope and road adhesion coefficient at the accident site, the road environment parameters at the accident site are determined to construct a typical accident reconstruction scenario.

5. The AEB system safety efficacy quantitative evaluation method according to claim 1, characterized in that: Reconstruct the accident reconstruction scene, specifically: Keep the sequential motion state of the preceding vehicle unchanged; Delete the original data of the reaction to the dangerous situation and the resulting change of vehicle motion state during the time sequence movement of the rear vehicle of the accident, and keep the horizontal position and speed of the rear vehicle when the driver starts the initial reaction action; The speed of the preceding vehicle is inferred based on the average acceleration of the preceding vehicle within a set time before the original collision.

6. The AEB system safety efficacy quantitative evaluation method according to claim 5, characterized in that: The driver reaction behavior model and the AEB system algorithm are coupled and applied to the participating vehicles in the reconstructed accident reconstruction scene to perform simulation operations and obtain simulation data, specifically: On the basis of maintaining the horizontal position and speed of the rear vehicle at the beginning of the driver's initial reaction action, the driver's reaction behavior model and the AEB system algorithm are coupled and applied to the rear vehicle for simulation calculation; During the simulation, the data of the distance between the two vehicles, their speed, acceleration, and whether a collision occurs over time are collected as simulation data; The simulation is terminated when the two vehicles collide, the simulation runs until the rear vehicle completely stops, or the simulation runs for a set time.

7. The AEB system safety efficacy quantitative evaluation method according to claim 1, characterized in that: The collision avoidance percentage, the reduction percentage of each injury severity and the cumulative distribution curve of the minimum vehicle spacing are calculated and compared, specifically: Based on the simulation data, count whether a collision occurs. If no collision occurs, obtain the minimum vehicle distance between the two vehicles and draw a cumulative distribution curve of the minimum vehicle distance; Calculate collision avoidance percentage; The severity of the driver's injuries is predicted based on the collision speed, and the percentage reduction of each injury severity is calculated, wherein the injury severity includes fatal, serious and minor.

8. AEB system safety efficacy quantitative evaluation system, characterized by: include: The driver reaction behavior model building module is configured to: obtain driving reaction behavior characteristic data and build a driver reaction behavior model for dangerous situations; The accident reconstruction scene reconstruction module is configured to: obtain accident investigation data, construct a typical accident reconstruction scene, and reconstruct the accident reconstruction scene; The simulation module is configured to: couple the driver reaction behavior model and the AEB system algorithm to the participating vehicles in the reconstructed accident reconstruction scene, perform simulation calculations, and obtain simulation data; The evaluation module is configured to: calculate and compare the collision avoidance percentage, the reduction percentage of each damage severity and the cumulative distribution curve of the minimum vehicle spacing based on the simulation data, and comprehensively evaluate the safety effectiveness of the AEB system.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the AEB system safety efficacy quantitative evaluation method as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the AEB system safety efficacy quantitative evaluation method as described in any one of claims 1 to 7 are implemented.

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