Maximum passing vehicle speed searching method for unmanned elk test

Abstract

The invention relates to the technical field of intelligent chassis testing, and discloses a maximum passing vehicle speed searching method for an unmanned elk test, and the method comprises the steps: continuously carrying out the unmanned elk test of a vehicle through an unmanned elk test system installed on the vehicle; when the unmanned elk test is carried out, the vehicle is controlled to reach the target test speed, the vehicle is controlled to complete target path tracking according to the updated vehicle target path and path tracking control parameters, and the vehicle collision penalty degree of the vehicle is calculated through the collected vehicle positioning point coordinate information. Judging whether the vehicle collision punishment degree meets a preset punishment threshold value or not; if not, the vehicle target path and the path tracking control parameters are updated, the unmanned elk test is carried out again until the penalty threshold value is met, it is regarded that the elk test passes, and the target test speed is increased. And if the preset number of times of unmanned elk tests are executed at a certain target test speed and the elk tests are not passed, taking the target test speed of the previous round as the maximum passing speed of the elk of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent chassis testing, and in particular to a method for finding the maximum passing speed of an unmanned elk test. Background Art

[0002] In automotive engineering, a vehicle's handling stability and emergency obstacle avoidance capabilities are crucial indicators of safety and overall performance. The maximum speed at which a vehicle passes the autonomous elk test is a key factor in assessing the vehicle's chassis electromechanical quality and driving safety. The higher the speed at which a vehicle passes the autonomous elk test, the more stable the vehicle's handling in an emergency, effectively avoiding collisions and protecting the lives of drivers and passengers.

[0003] Traditionally, determining a vehicle's maximum speed for autonomous driving has relied primarily on actual driving tests conducted by human drivers. However, this testing approach has significant limitations. Due to subjective factors such as driving habits and mental state while driving, as well as individual differences in abilities, including reaction speed and driving skills, it is difficult to maintain consistent test results from one test to the next. This often results in traditional methods being unable to determine a vehicle's true maximum speed for autonomous driving, and thus failing to truly and objectively reflect the true capabilities of the vehicle chassis. Such inaccurate test results not only affect the precise evaluation of vehicle performance during the automotive R&D process, but can also mislead consumers about the vehicle's safety performance.

[0004] Based on this, there is an urgent need for a method to find the maximum passing speed of an unmanned elk test, which can solve the interference problem of the driver's subjective factors and personal ability in the existing technology and realize the accurate measurement of the maximum passing speed of the vehicle in the unmanned elk test. Summary of the Invention

[0005] One of the purposes of the present invention is to provide a method for finding the maximum passing speed of an unmanned elk test, which can solve the interference problem of the driver's subjective factors and personal ability in the existing technology, and realize the accurate measurement of the maximum passing speed of the vehicle in the unmanned elk test.

[0006] In order to achieve the above object, a method for finding the maximum passing speed of an unmanned elk test is provided, comprising the following steps:

[0007] S1. Control the vehicle to conduct multiple rounds of unmanned elk tests continuously through the unmanned elk test control system installed on the vehicle;

[0008] S2. During the mth round of the unmanned elk test, add a sampling step size to the target test speed corresponding to the m-1th round, and use it as the target test speed corresponding to this round. After the vehicle reaches the target test speed based on the target test speed corresponding to this round, control the vehicle to perform a double lane change maneuver and collect the coordinates of the vehicle positioning point in real time;

[0009] If m=1, the target test speed corresponding to the vehicle, as well as the vehicle target path and path tracking control parameters are initialized; if m>1, the vehicle target path and path tracking control parameters corresponding to the m-1th round are retrieved;

[0010] S3. Calculate the vehicle collision penalty corresponding to the vehicle based on the collected vehicle positioning point coordinates and the corresponding vehicle target path and path tracking control parameters, based on a preset vehicle collision penalty calculation formula, and determine whether the calculated vehicle collision penalty satisfies a preset penalty threshold;

[0011] S4. If the judgment result is no, the vehicle target path and path tracking control parameters are updated, and S3 is re-executed to continue the next iteration of the unmanned elk test at the target test speed corresponding to the mth round of the unmanned elk test;

[0012] S5. Determine whether the mth round of unmanned elk test passes the unmanned elk test within the preset iteration threshold. If not, use the target test speed corresponding to the m-1th round of unmanned elk test as the maximum speed for the vehicle to pass the unmanned elk test.

[0013] S6. If the judgment result is yes, re-execute S2, and m=m+1 at this time.

[0014] Technical Principles and Results of This Solution: In this solution, a driving robot is directed by a vehicle-mounted unmanned elk test control system, enabling multiple rounds of unmanned elk testing. This provides the foundation for the automation and standardization of the entire testing process. The driving robot can precisely operate the vehicle according to system instructions, preventing human interference with test results.

[0015] During the mth round of unmanned elk testing, a sampling step is added to the target test speed corresponding to the m-1th round, which is used as the target test speed for this round. Based on the target test speed corresponding to this round, after the vehicle reaches the target test speed, it is controlled to perform a double lane change maneuver and the coordinates of the vehicle positioning point are collected in real time. By determining the target test speed corresponding to the corresponding round, as well as the vehicle's target path and path tracking control parameters, it is ensured that each round of testing begins with a clear and unified initial state, providing a stable starting point for subsequent testing and parameter adjustments. Of course, the initialization of the target test speed of this round is related to the target test speed of the previous round.

[0016] By collecting the coordinates of the vehicle's positioning points in real time, the actual driving trajectory of the vehicle during the test can be obtained, providing data support for subsequent evaluation of the vehicle's driving performance.

[0017] Based on the collected vehicle location coordinates, the corresponding target path, and the path tracking control parameters, a preset collision penalty formula is used to calculate the vehicle's collision penalty. This formula takes into account factors such as the degree of deviation from the target path and the proximity to possible obstacles. The calculated collision penalty is then compared with the preset penalty threshold to determine whether the vehicle's performance in this round of testing meets the requirements.

[0018] If the collision penalty does not meet the preset threshold, it indicates that the vehicle's driving performance is poor under the current parameter settings. The vehicle's target path and path tracking control parameters need to be updated, and the path tracking step needs to be re-executed for the next iteration. The purpose of continuous iteration is to gradually adjust the parameters so that the vehicle can pass the unmanned elk test at a higher speed without exceeding the collision penalty threshold. When the number of iterations reaches the preset threshold, if the collision penalty still does not meet the requirements, the current speed is considered to be close to the vehicle's limit, and the speed of the previous test is used as the maximum passing speed.

[0019] If the collision penalty meets the preset threshold, it means that the vehicle can pass the test well at the current speed. At this time, a sampling step is added based on the speed of this round of tests to form the speed of the next round of tests. The next round of tests is then carried out to explore a higher passing speed.

[0020] Compared with traditional unmanned elk tests, which often rely on manual driving operations, there are many uncertainties in human factors, such as differences in driving skills, reaction speed, and driving habits among different drivers. These factors will have a significant impact on the test results, making it difficult for the test results to accurately reflect the actual performance of the vehicle. This solution uses an unmanned elk test control system and driving robot, which can strictly follow preset instructions and procedures, eliminating the interference of uncontrollable factors such as human emotions and fatigue, making the operation of each test highly consistent, thereby effectively reducing the uncertainty and errors caused by human operation, making the test results more accurate and reliable, and being able to truly and objectively present the performance of the vehicle in the unmanned elk test. For vehicle manufacturers, this can more accurately understand vehicle performance and provide a solid data foundation for subsequent improvements and optimizations.

[0021] In the past, the process of exploring the maximum passing speed of vehicles in unmanned elk tests usually required repeated manual adjustments to the vehicle speed and testing, which not only consumed a lot of time and energy but was also inefficient. This solution automatically finds the vehicle's maximum passing speed through continuous iteration and parameter adjustment. Based on the results of each round of testing, the system intelligently adjusts the relevant parameters, gradually approaching the vehicle's maximum speed limit. This automated approach eliminates the need for manual repeated trials and adjustments, greatly shortening the testing cycle and improving testing efficiency. Taking the testing of large numbers of vehicles as an example, it can significantly save time and costs, while also reducing manpower input and the labor cost of testing, making testing more efficient and economical.

[0022] The evaluation of the vehicle's safety and handling performance in the unmanned elk test is crucial. Previous evaluation methods may have been relatively simple and unable to comprehensively consider multiple factors. This solution uses collision penalty to evaluate the vehicle's driving performance, comprehensively considering multiple factors such as the degree to which the vehicle deviates from the target path and the proximity to possible obstacles. This comprehensive evaluation method can more comprehensively reflect the actual situation of the vehicle in the unmanned elk test and consider the vehicle's performance from multiple dimensions. For example, even if the vehicle does not directly collide with an obstacle, if it deviates too much from the target path, it will be reflected in the calculation of the collision penalty. This provides a more valuable and targeted reference for vehicle research and development and improvement, helping manufacturers to improve vehicle design and performance, and enhance vehicle safety and handling stability.

[0023] The entire unmanned elk testing process has clear and detailed steps and standards, from the initialization settings before the test, including the setting of the current target test speed, the vehicle's target path, and the path tracking control parameters, to the parameter adjustment during the test and the result judgment after the test is completed, each link has clear regulations. This standardized testing process ensures the consistency and repeatability of the test. Regardless of when and where the test is conducted, as long as this standard process is followed, similar test environments and conditions can be obtained. This makes the performance comparison and analysis between different vehicles more scientific, fair, and reasonable, and facilitates vehicle manufacturers, scientific research institutions, etc. to conduct horizontal comparisons of the performance of different models, thereby promoting technological progress and development of the entire automotive industry.

[0024] Furthermore, the S3 includes:

[0025] S31. Generate the x and y coordinates of the vehicle target path corresponding to the unmanned elk test based on the vehicle target path parameters;

[0026] S32, controlling the vehicle to track the target vehicle path using the corresponding path tracking control parameters according to the x and y coordinates of the target vehicle path corresponding to the unmanned elk test;

[0027] S33. When the vehicle passes through the target vehicle path, the coordinates of the vehicle positioning point corresponding to the vehicle are collected in real time. According to the collected vehicle positioning point coordinates, the corresponding steering wheel target angle is calculated based on the preset path tracking steering wheel target angle calculation formula, and the vehicle movement is controlled based on the corresponding steering wheel target angle.

[0028] Beneficial effects: In this solution, by determining the first horizontal coordinate and the first vertical coordinate of the vehicle's target path in the test coordinate system based on the vehicle's target path and the path tracking control parameters, the target path that the vehicle needs to track can be accurately constructed. The rational use of these parameters ensures that the generated target path meets the requirements and specifications of the unmanned elk test, and provides clear guidance for the accurate tracking of subsequent vehicles. The construction of a clear and precise target path helps to maintain consistency in test conditions across multiple tests. No matter how many rounds of testing are conducted, as long as the same parameter settings are used, the same target path can be generated, thereby ensuring the comparability and reliability of the test results and facilitating accurate evaluation of the vehicle's performance under different conditions.

[0029] As the vehicle traverses the target path, the coordinates of the vehicle's positioning points are collected in real time. The target steering angle is calculated based on the preset path tracking formula. The vehicle's steering target angle is controlled to ensure the vehicle tracks the target path as accurately as possible, improving the accuracy and effectiveness of the test. By collecting and analyzing the vehicle's positioning point coordinates and path tracking results in real time, a more comprehensive and accurate assessment of the vehicle's path tracking capability, handling stability, and other performance indicators can be made. These evaluation results are of great reference value for vehicle design improvements, the development of autonomous driving technology, and the improvement of safety performance.

[0030] Further, the S31 includes:

[0031] The first horizontal coordinate vector x corresponding to the vehicle target path in the test coordinate system ref Determine the sampling range and set the corresponding sampling interval;

[0032] According to the first horizontal coordinate vector x ref The first ordinate vector y corresponding to the target path of the vehicle in the test coordinate system is calculated based on the preset first ordinate vector calculation formula after the sampling range and sampling interval are determined. ref , forming a corresponding vehicle target path through the first abscissa vector and the first ordinate vector;

[0033] Preset calculation formula for the first ordinate vector;

[0034]

[0035] Where z1 and z2 are intermediate variables, shape, Xs1, Xs2, dx1, dx2, dy1, and dy2 are seven parameters in the vehicle target path parameters, shape is the path shape influencing factor, Xs1 and Xs2 are the corresponding lateral key position influencing factors, dx1 and dx2 are the corresponding lateral rate influencing factors, and dy1 and dy2 are the corresponding longitudinal rate influencing factors.

[0036] Beneficial effects: By determining the sampling range of the first horizontal coordinate vector corresponding to the vehicle's target path in the test coordinate system and setting the corresponding sampling interval, the horizontal coordinate coverage of the target path can be flexibly defined according to the actual test requirements and the expected range of vehicle travel. For example, the sampling range can be reasonably set and the appropriate sampling interval can be set based on factors such as the maximum distance the vehicle may travel in the unmanned elk test. This ensures that the generated target path can fully cover the area required for the test, and the accuracy and efficiency of the test will not be affected by overly dense or sparse sampling points.

[0037] By comprehensively considering multiple parameters that influence the vehicle's target curve shape, the system accurately describes the changes in the target path along the ordinate. By precisely calculating the first ordinate vector, an accurate and predictable target path is generated, providing a precise reference for subsequent vehicle path tracking.

[0038] Seven parameters that influence the shape of the vehicle's target curve make the target path highly adjustable. These parameters can be adjusted to alter the shape and characteristics of the target path in different test scenarios or vehicle types to suit various testing requirements. For example, for vehicles with different performance capabilities, parameters can be adjusted to simulate autonomous elk test scenarios of varying difficulty, allowing for a more comprehensive assessment of the vehicle's performance and response capabilities.

[0039] Furthermore, the calculation formula for the target steering wheel angle of the path tracking is:

[0040]

[0041] Where θ is the preview error angle, x p 、y p and are the longitudinal position, lateral position and heading angle of the vehicle positioning point in the experimental coordinate system, v is the real-time speed of the vehicle, Δt is the preview time, and L is the vehicle wheelbase. Δt is an important control parameter for pure tracking and is strongly correlated to the accuracy of path tracking. δ is the steering wheel angle.

[0042] Beneficial Effect: This method accurately measures the vehicle's deviation from the target path at the preview moment. For example, when the vehicle deviates from the target path, the formula can intuitively calculate the deviation angle by calculating various parameters, providing a precise basis for subsequent adjustments and helping to improve path tracking accuracy.

[0043] It accurately determines the steering wheel angle required to return the vehicle to its target path. This enables the vehicle to follow the target path and achieve precise path-following control. For example, when the vehicle deviates slightly, the formula can calculate a smaller steering wheel angle to allow the vehicle to make fine adjustments; while when the vehicle deviates significantly, it can calculate a larger angle to allow the vehicle to quickly correct its course.

[0044] Furthermore, the vehicle collision penalty calculation formula is:

[0045]

[0046] Where CostFcn is the vehicle collision penalty, V i is the collision degree of the i-th collision risk zone.

[0047] Beneficial Effects: By considering multiple collision risk zones (including high-risk collision points and high-risk collision boundaries), the system can more comprehensively simulate the collision scenarios a vehicle may encounter during actual driving. During unmanned elk testing, the vehicle may come into contact with obstacles or dangerous areas in various locations. By cumulatively calculating the collision severity of multiple collision risk zones, the system can more realistically reflect the vehicle's safety and response capabilities in complex environments, making the test results more practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a method for finding the maximum passing speed of an unmanned elk test in Example 1 of the present invention;

[0049] Figure 2 This is a diagram showing the obstacle avoidance test lane, its anchor points, and the high-risk collision zone in Example 1 of the present invention;

[0050] Figure 3 This is a schematic diagram of determining whether a high-risk collision zone collides with a vehicle in the first embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following is further described in detail through specific implementation methods:

[0052] Example 1

[0053] A method for finding the maximum passing speed of an unmanned elk test is basically as follows Figure 1 As shown, the following steps are included:

[0054] S1. Control the vehicle to conduct multiple rounds of unmanned elk tests continuously through the unmanned elk test control system installed on the vehicle;

[0055] S2. During the mth round of the unmanned elk test, add a sampling step size to the target test speed corresponding to the m-1th round, and use it as the target test speed corresponding to this round. After the vehicle reaches the target test speed based on the target test speed corresponding to this round, control the vehicle to perform a double lane change maneuver and collect the coordinates of the vehicle positioning point in real time;

[0056] If m=1, the target test speed corresponding to the vehicle, as well as the vehicle target path and path tracking control parameters are initialized; if m>1, the vehicle target path and path tracking control parameters corresponding to the m-1th round are retrieved;

[0057] S3. Calculate the vehicle collision penalty corresponding to the vehicle based on the collected vehicle positioning point coordinates and the corresponding vehicle target path and path tracking control parameters, based on a preset vehicle collision penalty calculation formula, and determine whether the calculated vehicle collision penalty satisfies a preset penalty threshold;

[0058] The S3 includes:

[0059] S31. Generate the x and y coordinates of the vehicle target path corresponding to the unmanned elk test based on the vehicle target path parameters;

[0060] The S31 includes:

[0061] The first horizontal coordinate vector x corresponding to the vehicle target path in the test coordinate system ref Determine the sampling range and set the corresponding sampling interval;

[0062] According to the first horizontal coordinate vector x ref The first ordinate vector y corresponding to the target path of the vehicle in the test coordinate system is calculated based on the preset first ordinate vector calculation formula after the sampling range and sampling interval are determined. ref , forming a corresponding vehicle target path through the first abscissa vector and the first ordinate vector;

[0063] Preset calculation formula for the first ordinate vector;

[0064]

[0065]

[0066] Where z1 and z2 are intermediate variables, shape, Xs1, Xs2, dx1, dx2, dy1, and dy2 are seven parameters in the vehicle target path parameters, shape is the path shape influencing factor, Xs1 and Xs2 are the corresponding lateral key position influencing factors, dx1 and dx2 are the corresponding lateral rate influencing factors, and dy1 and dy2 are the corresponding longitudinal rate influencing factors.

[0067] S32, controlling the vehicle to track the target vehicle path using the corresponding path tracking control parameters according to the x and y coordinates of the target vehicle path corresponding to the unmanned elk test;

[0068] S33. When the vehicle passes through the target vehicle path, the coordinates of the vehicle positioning point corresponding to the vehicle are collected in real time. According to the collected vehicle positioning point coordinates, the corresponding steering wheel target angle is calculated based on the preset path tracking steering wheel target angle calculation formula, and the vehicle movement is controlled based on the corresponding steering wheel target angle.

[0069] The calculation formula for the steering wheel target angle of the path tracking is:

[0070]

[0071] Where θ is the preview error angle, x p 、y p and are the longitudinal position, lateral position and heading angle of the vehicle positioning point in the experimental coordinate system, v is the real-time speed of the vehicle, Δt is the preview time, and L is the vehicle wheelbase. Δt is an important control parameter for pure tracking and is strongly correlated to the accuracy of path tracking. δ is the steering wheel angle.

[0072] The vehicle collision penalty calculation formula is:

[0073]

[0074] Where CostFcn is the vehicle collision penalty, V i is the collision degree of the i-th collision risk zone.

[0075] In this embodiment, there are a total of 7 collision risk areas, namely 4 high-risk collision points and 3 high-risk collision boundaries. According to the position of the emergency test lane anchor point in the test coordinate system, the spatial positions of the 4 high-risk collision points and the 3 high-risk collision boundaries can be calculated. a is the lower left corner point of the emergency lane change test lane. Figure 2 As shown, the emergency lane change test lane anchor point P a Located on the y-axis of the test coordinate system xoy, the y value is -(W veh / 2+0.2).

[0076] In this embodiment, the vehicle corner points in the test coordinate system of the unmanned elk test are simplified as a rectangle, and the left front P fl , Left rear P rl , right front P fr , right rear P rr 4 points, their coordinates in the experimental coordinate system can be determined based on the width W of the vehicle being tested. veh 、L veh , the distance L between the main positioning antenna and the front envelope of the vehicle pf , the distance L between the main positioning antenna and the left envelope of the vehicle pl , and the coordinates of the vehicle positioning point in the experimental coordinate system (x p ,y p ) and heading Calculated.

[0077]

[0078] like Figure 3 As shown in the figure, the high-risk area is judged whether it collides with the vehicle. The vehicle collides with the high-risk collision point R1 (the point has coordinates (x r1 ,y r1 The collision degree of )) is calculated as follows:

[0079]

[0080] Where y jud1 is the x value where the vehicle appears at the high-risk collision point R1 r1 The maximum y value at .

[0081] Then, the maximum collision degree V1 of the vehicle at the high-risk collision point R1 is

[0082] V1=max(y jud1 )-y r1

[0083] Then, by the same logic, we can know that the vehicle and the high collision risk point R2 (the point with coordinates (x r2 ,y r2 ))'s collision occurrence determination method.

[0084]

[0085] Where y jud2 is the maximum y value of the cross section of the vehicle at the x position indicated by the high-risk collision point R2 at the left boundary of the vehicle. Then, the maximum collision degree V2 of the vehicle at the high-risk collision point R2 is

[0086] V2=y r2 -max(y jud2 )

[0087] The vehicle and the high collision risk point R3 (the coordinate of this point in the experimental coordinate system (x r3 ,y r3 ))'s collision occurrence determination method.

[0088]

[0089] Where y jud3 is the maximum y value of the cross section of the vehicle at the x position indicated by the high-risk collision point R3 when the left boundary of the vehicle appears. Then, the maximum collision degree V3 of the vehicle at the high-risk collision point R3 is

[0090] V3=y r3 -max(y jud3 )

[0091] The vehicle and the high collision risk point R4 (the coordinate of this point in the experimental coordinate system (x r4 ,y r4 ))'s collision occurrence determination method.

[0092]

[0093] Where y jud4is the maximum y value of the cross section of the vehicle at the x position indicated by the high-risk collision point R4 at the left boundary of the vehicle. Then, the maximum collision degree V4 of the vehicle at the high-risk collision point R4 is

[0094] V4=max(y jud4 )-y r4

[0095] When judging the collision degree between the vehicle and the high-risk collision boundary: calculate the maximum and minimum values ​​in the y direction of the four corner points of the vehicle in the complete time series, and subtract them from the high-risk collision boundaries L2 and L3. The difference is the collision degree V6 and V7;

[0096]

[0097]

[0098] Where, The y values ​​of the left front, left rear, right front, and right rear corner points of the vehicle in the full time series of the unmanned elk experiment. L2 and y L3 It is the y-position of the high-risk collision boundaries L2 and L3 in the test coordinate system.

[0099]

[0100] Where, The left front, left rear, right front, and right rear corner points of the vehicle are in the test coordinate system x=[0,x r1 ] corresponding to the set of y values. L1 is the y-position of the high-risk collision boundary L1 in the experimental coordinate system.

[0101] S4. If the judgment result is no, the vehicle target path and path tracking control parameters are updated, and S3 is re-executed to continue the next iteration of the unmanned elk test at the target test speed corresponding to the mth round of the unmanned elk test;

[0102] S5. Determine whether the vehicle passes the unmanned elk test within a preset iteration threshold in the mth round. If not, use the target test speed corresponding to the m-1th round as the maximum speed required to pass the unmanned elk test. In this embodiment, the iteration threshold is 90.

[0103] S6. If the judgment result is yes, re-execute S2, and at this time m=m+1.

[0104] The above is only an embodiment of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is excessively described here. Ordinary technicians in the relevant field are aware of all common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for finding the maximum passing speed of an unmanned elk test, characterized in that: The following steps are involved: S1. Control the vehicle to continuously perform multiple rounds of unmanned elk tests through the unmanned elk test control system installed on the vehicle; S2. In the mth round of unmanned elk test, a sampling step is added to the target test speed corresponding to the m-1th round as the target test speed corresponding to this round. After the vehicle reaches the target test speed based on the target test speed corresponding to this round, the vehicle is controlled to perform a double lane shift maneuver and the coordinates of the vehicle positioning point are collected in real time; If m=1, the target test speed corresponding to the vehicle, as well as the vehicle target path and path tracking control parameters are initialized; if m>1, the vehicle target path and path tracking control parameters corresponding to the m-1th round are retrieved; S3, according to the collected vehicle positioning point coordinates and the corresponding vehicle target path and path tracking control parameters, based on the preset vehicle collision penalty calculation formula, calculate the vehicle collision penalty corresponding to the vehicle, and determine whether the calculated vehicle collision penalty meets the preset penalty threshold; S4. If the judgment result is no, the vehicle target path and path tracking control parameters are updated, and S3 is re-executed to continue the next iteration of the unmanned elk test at the target test vehicle speed corresponding to the mth round of the unmanned elk test; S5. Determine whether the mth round of unmanned elk test passes the unmanned elk test within a preset iteration threshold. If not, use the target test speed corresponding to the m-1th round of unmanned elk test as the maximum speed corresponding to the unmanned elk test for the vehicle. S6. If the judgment result is yes, re-execute S2, and at this time m=m+1.

2. The method for finding the maximum passing speed of an unmanned elk test according to claim 1, characterized in that: The S3 includes: S31. Generate x and y coordinates of the vehicle target path corresponding to the unmanned elk test according to the vehicle target path parameters; S32, according to the x and y coordinates of the vehicle target path corresponding to the unmanned elk test, controlling the vehicle to track the vehicle target path with corresponding path tracking control parameters; S33. When the vehicle passes through the target vehicle path, the coordinates of the vehicle positioning points corresponding to the vehicle are collected in real time. According to the collected vehicle positioning point coordinates and based on the preset path tracking steering wheel target angle calculation formula, the corresponding steering wheel target angle is calculated, and the vehicle movement is controlled based on the corresponding steering wheel target angle.

3. The method for finding the maximum passing speed of an unmanned elk test according to claim 2, characterized in that: The S31 includes: The first horizontal coordinate vector x corresponding to the vehicle target path in the test coordinate system ref Determine the sampling range and set the corresponding sampling interval; According to the first horizontal coordinate vector x ref The sampling range and sampling interval are determined, and the first ordinate vector y corresponding to the target path of the vehicle in the test coordinate system is calculated based on the preset first ordinate vector calculation formula. ref , forming a corresponding vehicle target path through the first abscissa vector and the first ordinate vector; Preset calculation formula for the first ordinate vector; Wherein, z1 and z2 are intermediate variables, shape, Xs1, Xs2, dx1, dx2, dy1, and dy2 are seven parameters of the vehicle target path parameters, shape is the path shape influencing factor, Xs1 and Xs2 are the corresponding lateral key position influencing factors, dx1 and dx2 are the corresponding lateral rate influencing factors, and dy1 and dy2 are the corresponding longitudinal rate influencing factors.

4. The method for finding the maximum passing speed of an unmanned elk test according to claim 3, characterized in that: The calculation formula of the steering wheel target angle of the path tracking is: Where θ is the preview error angle, x p ,y p and are the longitudinal position, lateral position and heading angle of the vehicle positioning point in the experimental coordinate system, v is the real-time speed of the vehicle, Δt is the preview time, and L is the wheelbase of the vehicle; among them, Δt is an important control parameter of pure tracking and is strongly correlated to the accuracy of path tracking, and δ is the steering wheel angle.

5. The method for finding the maximum passing speed of an unmanned elk test according to claim 4, characterized in that: The vehicle collision penalty calculation formula is: Where CostFcn is the vehicle collision penalty, V i is the collision degree of the i-th collision risk area.

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