Unmanned elk test trafficability offline judgment method based on positioning signal

Abstract

The invention relates to the technical field of automobile intelligent chassis testing, and discloses an unmanned elk test trafficability offline judgment method based on a positioning signal, which comprises the following steps: establishing a test coordinate system based on a vehicle positioning antenna coordinate and a vehicle course at the moment when an unmanned elk test enters a test state; coordinate transformation and vehicle corner point coordinate calculation are carried out; screening high-risk collision areas of unmanned elk test emergency line change test lanes; and calculating a collision factor of the unmanned elk test based on the high-risk collision region, and further judging whether the elk test passes or not. According to the invention, the test precision and the automation level are improved, and the ability of evaluating the safety performance of the vehicle is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of automotive intelligent chassis testing, and particularly relates to an offline judgment method for the passability of unmanned moose tests based on positioning signals. Background Art

[0002] The moose test is a standard test for verifying the roll stability of vehicles, which examines the vehicle handling stability performance during two emergency lane changes. The moose test was initially to simulate the emergency operation of a driver when encountering a large animal such as a moose rushing out from the roadside during driving, and was formulated as the "Moose Test" standard of VDA, and later was applied to the testing of sedans and became well-known to the public.

[0003] The automotive industry conducts tests and releases moose test results according to the obstacle avoidance test lane dimensions, obstacle avoidance test lane markings, and obstacle avoidance emergency lane change operations required by the standards. The existing test method uses whether the test vehicle collides with the obstacle avoidance test lane markers as the basis for passing the moose test. However, in unmanned moose tests, lane markers are no longer needed to guide the driver for obstacle avoidance operations. Unmanned moose tests require precise analysis of vehicle behavior through automated means, and traditional test methods can no longer meet the requirements of high precision and automation. Therefore, there is an urgent need for an offline judgment method for the passability of unmanned moose tests based on positioning signals to judge the passability of unmanned moose tests. Summary of the Invention

[0004] The present invention aims to provide an offline judgment method for the passability of unmanned moose tests based on positioning signals. By combining positioning signals with the test coordinate system and using the corner point coordinate model and high-risk collision area judgment, it realizes the precise assessment of the collision risk in unmanned moose tests, and obtains the final test result through multi-dimensional passability judgment factors, improving the test accuracy and automation level, and enhancing the assessment ability of vehicle safety performance.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] An offline judgment method for the passability of unmanned moose tests based on positioning signals, comprising:

[0007] S1, at the moment when the unmanned moose test enters the test state, establish a test coordinate system based on the vehicle positioning antenna coordinates and the vehicle heading;

[0008] S2, perform coordinate transformation and calculate the vehicle corner point coordinates;

[0009] S3, screen the high-risk collision areas of the emergency lane change test lane in the unmanned moose test;

[0010] S4, judge the occurrence of collisions in the high-risk collision areas of the unmanned moose test based on the collision factors in the high-risk collision areas;

[0011] S5. Determine whether the moose test is passed based on the moose test passability judgment factor.

[0012] The principle and advantages of this solution are as follows: In actual application, at the moment when the unmanned moose test enters the test state, an experimental coordinate system is established based on the vehicle positioning antenna coordinates and the vehicle heading, which is convenient for off-line judgment; the vehicle position coordinates are converted from the vehicle positioning signal to the experimental coordinate system, which is convenient for ensuring that all data during the test are represented in a unified reference system, eliminating the deviation between different sensor data, improving the reliability of the test data, and providing an accurate spatial reference for subsequent calculations; at the same time, it is convenient to use the longitude, latitude, heading data, etc. of the vehicle under test to judge whether the moose test is passed.

[0013] Obtaining the vehicle corner coordinates is convenient for determining the specific shape and spatial distribution of the vehicle during the test, providing basic data for collision detection and trajectory analysis. The application of corner coordinates further refines the spatial distribution of the vehicle and enhances the ability to describe the dynamic behavior of the vehicle.

[0014] According to the emergency lane-changing test lane of the unmanned moose test, delimit the high-risk areas that may cause collisions. These areas are usually the key points on the lane-changing path or the locations of obstacles, which is convenient for clarifying potential dangerous areas and subsequent collision judgment.

[0015] Based on the collision factor in the high-risk collision area, judge whether a collision occurs in the high-risk collision area of the unmanned moose test to real-time evaluate whether there is a collision risk during the test and provide a basis for the final result. Finally, comprehensively consider the moose test passability judgment factor to judge whether the moose test is passed, quantitatively analyze the test results from multiple dimensions, ensure the scientificity and accuracy of the judgment, effectively assist the popularization and application of the unmanned moose test method, and provide strong support for the verification of intelligent chassis innovation technologies.

[0016] Technical effects: (1) Do not rely on whether the obstacle avoidance test lane markings are collided as the basis for judging the moose test passability, avoiding the ambiguity of passability judgment caused by the obstacle avoidance test lane markings being blown away by the airflow generated by vehicle movement;

[0017] (2) The digital judgment method can avoid the safety risk caused by the wheels being caught in the obstacle avoidance test lane marking cones;

[0018] (3) The digital judgment method makes a collision judgment according to the automatically generated obstacle avoidance test lane when entering the unmanned moose test state, which is convenient for ensuring that when entering the test state each time, the heading and position of the vehicle are fixed relative to the obstacle avoidance test lane, effectively avoiding the problem that the initial conditions for entering the test state change due to the movement error of the vehicle before entering the test state, resulting in the inability to effectively reproduce the previously passable moose test results.

[0019] Preferably, as an improvement, the vehicle corner coordinates include the corners of the vehicle shape simplified as a rectangle, and the calculation model of the corner coordinates includes:

[0020]

[0021]

[0022] Wherein, is the coordinate of the left front corner point, is the coordinate of the left rear corner point, is the coordinate of the right front corner point, is the coordinate of the right rear corner point, W veh is the vehicle width, L veh is the vehicle length, L pf is the distance between the positioning main antenna and the front envelope line of the vehicle, L pl is the distance between the positioning main antenna and the left envelope line of the vehicle, (x p , y p ) is the coordinate of the vehicle positioning point in the test coordinate system, is the heading.

[0023] Technical effect: It is convenient to judge whether the moose test passes based on the vehicle width, vehicle length, and positioning main antenna installation dimension information of the vehicle under test, according to the longitude and latitude, heading, moose test state, and the anchor point coordinates and heading of the emergency lane boundary during the unmanned moose test.

[0024] Preferably, as an improvement, the high-risk collision area includes: aligning the position of the emergency lane anchor point in the test coordinate system and calculating the spatial positions of 4 high-risk collision points and 3 high-risk collision boundaries.

[0025] Technical effect: Compared with the traditional method of visual inspection and then image processing, it is more intuitive and convenient to achieve quantitative analysis through collision points and collision boundaries; at the same time, 4 high-risk collision points and 3 high-risk collision boundaries can obtain the required information with the least amount of calculation, saving the amount of calculation.

[0026] Preferably, as an improvement, the emergency lane anchor point is the lower left corner point of the emergency lane, and the ordinate value of the anchor point is -(W veh / 2 + 0.2).

[0027] Technical effect: It is convenient to clarify the potential danger area.

[0028] Preferably, as an improvement, the collision factor includes the collision factor of the high-risk collision point, and the collision factor of the high-risk collision point includes:

[0029]

[0030]

[0031] Among them, C R1 、C R2 、C R3 and C R4 are respectively the collision factors of high-risk collision points R 1 、R 2 、R 3 、R 4 ; y jud1 、y jud2 、y jud3 、y jud4 are respectively the maximum ordinate values of the cross-section of the vehicle at the abscissa positions where the left boundary of the vehicle appears at high-risk collision points R 1 、R 2 、R 3 、R 4 ; y r1 、y r2 、y r3 and y r4 are respectively the ordinate values of high-risk collision points R 1 、R 2 、R 3 、R 4 .

[0032] Technical effect: By the above-mentioned judgment method based on geometric intersection, the false alarm or missed alarm problems existing in traditional sensor detection are avoided; by using the calculation in the test coordinate system, the result can be quickly obtained directly from the vehicle positioning signal and model parameters without complex physical sensor feedback, which is suitable for offline or online analysis, improves the accuracy of the test, and also enhances the adaptability to complex working conditions.

[0033] Preferably, as an improvement, the calculation model of the maximum ordinate value of the cross-section of the vehicle at the abscissa position where the left boundary of the vehicle appears at the high-risk collision point is:

[0034]

[0035] In the formula, is the coordinate of the left front corner point, is the coordinate of the left rear corner point, x ri is the coordinate of the high-risk collision point R i in the test coordinate system.

[0036] Technical effect: By comparing the coordinates of the high-risk collision point with the coordinates of the vehicle envelope, it can accurately judge whether the vehicle enters or approaches the dangerous area.

[0037] Preferably, as an improvement, the collision factor further includes the collision factor of the high-risk collision boundary, and the collision factor of the high-risk collision boundary includes:

[0038]

[0039] wherein, C L1 , C L2 , C L3 are the collision factors of the high-risk collision boundaries L 1 , L 2 , L 3 respectively; y Pfl , y Pr1 , y Pfr , y Prr are the sets of ordinate 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 moose test; y L1 , y L2 , y L3 are the ordinate values of L 1 , L 2 , L 3 in the test coordinate system respectively.

[0040] Technical effect: Through the above calculation combining the longitudinal maximum and minimum values of the vehicle corner points in the complete time series, a comprehensive assessment of the collision risk in the unmanned moose test is achieved, which not only improves the accuracy and reliability of judgment, but also enhances the adaptability to complex working conditions, providing important technical support for the safety performance evaluation of autonomous vehicles.

[0041] Preferably, as an improvement, the calculation model of the moose test passability judgment factor is:

[0042]

[0043] When P moose = 1, the moose test is passed; when P moose = 0, the moose test is not passed;

[0044] wherein, C R1 , C R2 , C R3 , C R4 , C L1 , C L2 , C L3 are the collision judgment factors of the high-risk collision points and the high-risk collision boundaries respectively.

[0045] Technical effect: It is convenient to quantify the test results from multiple dimensions and improve the result accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic flow chart of an offline judgment method for the passing performance of an unmanned elk test based on positioning signals;

[0047] Figure 2 It is a schematic diagram of the coordinate system for the unmanned elk test in an embodiment of the present invention;

[0048] Figure 3 It is a schematic diagram of the obstacle avoidance test lane, its anchor points and the high-risk collision area;

[0049] Figure 4 It is a schematic diagram for judging whether the high-risk collision area collides with the vehicle;

[0050] Figure 5 It is a schematic diagram for constructing the coordinate system of the unmanned elk test in an embodiment of the present invention. Specific embodiments

[0051] The following is a further detailed description through specific embodiments:

[0052] The embodiment is basically as shown in the appendix Figure 1 as follows:

[0053] An offline judgment method for the passing performance of an unmanned elk test based on positioning signals, comprising:

[0054] S1, at the moment when the unmanned elk test enters the test state, based on the vehicle positioning antenna coordinates and the vehicle heading, establish a test coordinate system.

[0055] The test state refers to the state after the test personnel remotely start the test, the vehicle is manipulated to break away from the static state, prepare for the test to reach the test conditions, and execute the objective test state of the passenger car obstacle avoidance emergency lane change, including: the test preparation stage, the test running stage, and the test end stage. The test preparation stage is the stage after the start of the objective test of the passenger car obstacle avoidance emergency lane change, when the vehicle accelerates from a standstill along a straight line to the target speed of the obstacle avoidance emergency lane change; the test execution stage is the stage when the vehicle is manipulated by the unmanned test system and starts to execute a lane change movement with the goal of passing through the obstacle avoidance test lane without colliding with the lane markings; the test end stage is the stage when the vehicle is controlled by the unmanned test system to decelerate to a standstill after deviating from the obstacle avoidance test lane by a certain distance or passing through the obstacle avoidance test lane.

[0056] The test coordinate system is a Cartesian coordinate system established with the longitude and latitude of the vehicle positioning point at the moment of switching from the test preparation stage to the test execution stage as the coordinate origin, and based on two lines passing through the vehicle positioning point and parallel and perpendicular to the vehicle. In this coordinate system, the forward direction of the vehicle is the positive direction of the X axis, the left direction of the vehicle is the positive direction of the Y axis, the counterclockwise direction is the positive direction of the heading, and the range of the heading angle in the coordinate system is [0°, 360°).

[0057] S2. Perform coordinate transformation and calculate the vehicle corner coordinates. Convert the longitude, latitude, and heading data transmitted by the vehicle positioning device into coordinates in the global Cartesian coordinate system (XOY), and then convert the coordinates (X p , Y p ) in the global Cartesian coordinate system XOY into coordinates (x p , y p ) in the unmanned moose test coordinate system xoy. As shown in Figure 2 , the unmanned moose test coordinate system is at the moment when the unmanned moose test enters the test state. Based on the vehicle position at this moment as the origin and the vehicle's orientation as 0° in the Cartesian coordinate system.

[0058] The vehicle corner coordinates include the corners of the vehicle's shape simplified as a rectangle. The vehicle corner coordinates are calculated based on the vehicle width, vehicle length, the distance between the positioning main antenna and the vehicle's front envelope line, the distance between the positioning main antenna and the vehicle's left envelope line, as well as the coordinates and heading of the vehicle positioning point in the test coordinate system.

[0059] The calculation model of the vehicle corner coordinates includes:

[0060]

[0061] Among them, is the left front corner coordinate, is the left rear corner coordinate, is the right front corner coordinate, is the right rear corner coordinate, W veh is the vehicle width, and the vehicle width is the overall vehicle width excluding the rearview mirrors; L veh is the vehicle length, and the vehicle length is the distance between the outermost front and rear endpoints of the vehicle perpendicular to the Y and X planes respectively, L pf is the distance between the positioning main antenna and the vehicle's front envelope line, L pl is the distance between the positioning main antenna and the vehicle's left envelope line, (x p , y p ) are the coordinates of the vehicle positioning point in the test coordinate system, is the heading.

[0062] S3. Screen the high-risk collision areas of the emergency lane change test lane for the unmanned moose test; that is, select some points with high boundaries of the emergency lane change test lane for the moose test as a reference for judging whether the boundary of the emergency lane change test lane for the moose test overlaps with the vehicle's outer contour.

[0063] The high-risk collision areas are to align the positions of the anchor points of the emergency lane change test lane in the test coordinate system and calculate the spatial positions of 4 high-risk collision points and 3 high-risk collision boundaries. As shown in Figure 3 , the 4 high-risk collision points are respectively R 1 , R2 , R 3 and R 4 , and the three high - risk collision boundaries are L 1 , L 2 and L 3 . The anchor point P a of the emergency lane - changing test lane is the lower - left corner point of the emergency lane - changing test lane. P a is located on the y - axis of the test coordinate system xoy, and the y - value is -(W veh / 2 + 0.2).

[0064] S4. Determine whether a collision occurs in the high - risk collision area during the unmanned elk test based on the collision factor of the high - risk collision area; the collision factor of the high - risk collision area includes the collision factor of the high - risk collision point and the collision factor of the high - risk collision boundary.

[0065] The method for judging the occurrence of a collision at the high - risk collision point includes: calculating the ordinate y - value of the vehicle envelope corresponding to the abscissa x - value of the high - risk collision point in the test coordinate system, and judging whether the vehicle envelope overlaps with the emergency lane - changing test lane by comparing the ordinate y - coordinate of the high - risk collision point. Specifically, as Figure 4 shown, the collision judgment of the vehicle with the high - collision - risk point R 1 (with coordinates (x r1 , y r1 ) in the test coordinate system xoy) is as follows:

[0066]

[0067] In the formula, y jud_1 is the maximum y - value of the cross - section of the vehicle when the left - hand side boundary of the vehicle appears at the x - position of the high - risk collision point R 1 shown.

[0068]

[0069] Therefore, the collision judgment factor C 1 of the R R1 point is:

[0070]

[0071] Similarly, when the vehicle performs the unmanned elk test, the collision judgment of the vehicle with the high - collision - risk point R 2 (with coordinates (x r2 , y r2 ) in the test coordinate system) is as follows:

[0072]

[0073] In the formula, y jud_2 is the left - hand side boundary of the vehicle when it appears at the high - risk collision point R2 The maximum y value of the cross-section of the vehicle at the x position shown.

[0074] R 2 Point collision judgment factor C R2 is:

[0075]

[0076] When the vehicle performs an unmanned moose test, the collision occurrence judgment between the vehicle and the high-collision-risk point R 3 (with coordinates (x r3 , y r3 ) in the test coordinate system) is as follows:

[0077]

[0078] In the formula, y jud_3 is the maximum y value of the cross-section of the vehicle at the x position where the left boundary of the vehicle appears at the high-risk collision point R 3 shown.

[0079] R 3 Point collision judgment factor C R3 is:

[0080]

[0081] When the vehicle performs an unmanned moose test, the collision occurrence judgment between the vehicle and the high-collision-risk point R 4 (with coordinates (x r4 , y r4 ) in the test coordinate system) is as follows:

[0082]

[0083] In the formula, y jud_4 is the maximum y value of the cross-section of the vehicle at the x position where the left boundary of the vehicle appears at the high-risk collision point R 4 shown.

[0084] R 4 Point collision judgment factor C R4 is:

[0085]

[0086] Taking the high-risk collision point R 1 as an example, if the y value of the vehicle envelope is greater than or equal to the y value of R 1 , the vehicle envelope overlaps with the emergency lane change test lane, and the vehicle is regarded as failing the moose test because it collides with the test lane.

[0087] The method for judging collision occurrence at the high-risk collision boundary includes: calculating the longitudinal maximum and minimum values of the vehicle corner points in the complete time series, and comparing them with the high-risk collision boundaries L 2 and L 3 to determine whether a collision occurs during the vehicle's execution of the unmanned moose test. The judgment principles include:

[0088]

[0089] Among them, L 2 and L 3 are the high-risk collision boundaries, y L2 and y L3 are the y-direction positions in the test coordinate system, and y Pfl , y Prl , y Pfr , y Prr are the sets of ordinate values of the left front corner point, left rear corner point, right front corner point, and right rear corner point in the full time series of the unmanned moose test execution respectively.

[0090] The collision judgment factors C 2 and C 3 for the L L2 and L L3 boundaries are:

[0091]

[0092] In the formula, y Pfl , y Prl , y Pfr , y Prr are the sets of y values of the vehicle's left front, left rear, right front, and right rear corner points in the full time series of the unmanned moose test execution respectively. y L2 and y L3 are the y-direction positions of the high-risk collision boundaries L 2 , L 3 in the test coordinate system.

[0093]

[0094] In the formula, are the sets of y values corresponding to the vehicle's left front, left rear, right front, and right rear corner points when x = [0, x r1 in the test coordinate system; y L1 is the y-direction position of the high-risk collision boundary L 1 in the test coordinate system.

[0095] The collision judgment factor C 1 for the L L1 boundary is:

[0096]

[0097] Based on the above collision judgment factors, the passability of the moose test is judged.

[0098] S5. Judge whether the moose test passes based on the moose test passability judgment factors. The moose test passability judgment method is as follows: If a collision occurs in any one of the high-risk collision points or high-risk collision boundaries, the moose test fails. On the contrary, only when neither the high-risk collision point nor the high-risk collision boundary has a collision does the moose test pass.

[0099] The calculation model of the moose test passability judgment factor is as follows:

[0100]

[0101] Among them, C R1 , C R2 , C R3 , C R4 , C L1 , C L2 , C L3 are the collision judgment factors of the high-risk collision point and the high-risk collision boundary respectively. When P moose = 1, the moose test passes; when P moose = 0, the moose test fails.

[0102] The specific implementation process includes:

[0103] (1) Test preparation: Measure the vehicle length and width of the vehicle to be tested; Measure the distance between the positioning main antenna and the outermost end point in front of the vehicle and perpendicular to the Y plane, and measure the distance between the positioning main antenna and the outermost end point on the left side of the vehicle and perpendicular to the X plane; Start the chassis electronic control function (vehicle motion mode, braking energy recovery mode, ESP, etc.) and set the mode or intensity of the control function, and record the chassis electronic control function combination and function mode / intensity; Set the reference path for obstacle avoidance emergency lane change, trajectory tracking parameters, the target speed for obstacle avoidance emergency lane change, and the y-direction distance between the obstacle avoidance test lane anchor point and the origin of the test coordinate system; Park the vehicle at the target position of the test site, keep the vehicle gear in D gear; The unmanned test system takes over the driving right, and the experimental personnel evacuate from the test site, not less than 100 m away from the test site.

[0104] (2) Unmanned Test: The experimenter remotely sends a test start instruction, and the vehicle is manipulated by the unmanned test system to enter the test state from a stationary state; after entering the test state, the unmanned test system first enters the test preparation stage. In this stage, it is required that the unmanned test system accelerates the vehicle along a straight line according to the target speed of obstacle avoidance and emergency lane change. The deviation of the vehicle positioning centroid from the target straight line is not more than 0.3 m. When the vehicle reaches the target speed of obstacle avoidance and emergency lane change and maintains within the range of the target speed of obstacle avoidance and emergency lane change ±0.5 km / h for 500 ms, the unmanned test system enters the test execution stage; record the longitudinal speed of the vehicle at the moment when it enters the test execution stage; at the moment of entering the test execution stage, establish a test coordinate system based on the longitude, latitude coordinates and heading of the vehicle positioning point at this moment, as Figure 5 shown. In the test coordinate system, the unmanned test system controls the vehicle to track the reference path of obstacle avoidance and emergency lane change until the X value of the vehicle positioning point in the test coordinate system is greater than 68 m, or the Y value is greater than 5 m, or the Y value is less than -4 m, then the test execution stage ends and enters the test end stage; after entering the test end stage, using the test coordinate system, the vehicle performs a braking maneuver with lateral position control, and the Y position must always be kept between -6 m and +9 m, and the X position when the vehicle stops is not greater than 120 m; record the flag signals of the three stages of the unmanned test system in the test state, the longitude and latitude coordinates of the vehicle centroid, the heading, the vehicle speed, the yaw rate, the lateral acceleration, the steering wheel angle, the steering wheel rotation speed, and the pedal feed.

[0105] (3) Judgment of Test Passability: Draw a test coordinate system based on the longitude, latitude information and heading information of the vehicle centroid at the switching moment between the test preparation stage and the test execution stage of the unmanned test system; draw the obstacle avoidance test lane in the test coordinate system according to the preset Y coordinate of the anchor point. Among them, the x value of the obstacle avoidance test lane anchor point is the same as that of the origin of the test coordinate system, and the lane heading is the same as the x direction of the test coordinate system, as Figure 3 shown; convert the longitude, latitude information and heading information of the vehicle collected at 100 Hz in the test execution stage into coordinates and headings in the test coordinate system; according to the coordinates and headings in the test coordinate system, combined with the distance between the positioning antenna and the vehicle body, the vehicle length and the vehicle width, draw the dynamic occupancy area of the vehicle in the test execution stage in the test coordinate system; according to the y-direction distance of the obstacle avoidance test lane anchor point relative to the origin of the coordinate system, draw the passenger car emergency lane change test lane line in the test coordinate system; observe whether the dynamic occupancy area of the vehicle overlaps with the test lane line in the high-risk collision area as Figure 3 shown. If there is an overlap, the test fails; if there is no overlap, the test passes, and the test passing speed is the vehicle speed at the moment of entering the test execution stage.

[0106] The above are only embodiments of the present invention, and common general technical solutions and / or characteristics in the solutions are not described in detail herein. It should be noted that for those skilled in the art, without departing from the technical solutions of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope claimed in this application shall be subject to the content of its claims, and the specific implementation manners and the like recorded in the specification can be used to interpret the content of the claims.

Claims

1. An offline judgment method for the passability of an unmanned elk test based on positioning signals, characterized in that: include: S1, when the unmanned elk test enters the test state, the test coordinate system is established based on the vehicle positioning antenna coordinates and vehicle heading; S2, coordinate conversion and vehicle corner point coordinate calculation; S3, screening high-risk collision areas of the emergency lane change test lane for unmanned elk testing; S4, judging the occurrence of a collision in the high-risk collision zone in the unmanned elk test based on the collision factor in the high-risk collision zone; S5, judging whether the moose test is passed based on the moose test passability judgment factor.

2. According to claim 1, a method for offline judgment of passability of unmanned elk test based on positioning signals is characterized in that: The vehicle corner point coordinates include corner points of a vehicle shape simplified into a rectangle, and the calculation model of the vehicle corner point coordinates includes: in, is the coordinate of the left front corner, is the coordinate of the left rear corner, is the coordinate of the right front corner, is the coordinate of the right rear corner, W veh is the vehicle width, L veh is the vehicle length, L pf To locate the distance between the main antenna and the front envelope of the vehicle, L pl To locate the distance between the main antenna and the left envelope of the vehicle, (x p ,y p ) is the coordinate of the vehicle positioning point in the test coordinate system, For the heading.

3. According to claim 1, a method for offline judgment of passability of unmanned elk test based on positioning signals is characterized in that: The high-risk collision area includes: aligning the position of the emergency lane change test lane anchor point in the test coordinate system, and calculating the spatial positions of 4 high-risk collision points and 3 high-risk collision boundaries.

4. The offline determination method for the passability of an unmanned elk test based on positioning signals according to claim 3 is characterized in that: The anchor point of the emergency lane change test lane is the lower left corner of the emergency lane change test lane, and the vertical coordinate value of the anchor point is -(W veh / 2+0.2).

5. The offline judgment method for the passability of unmanned elk test based on positioning signals according to claim 1 is characterized in that: The collision factor includes a collision factor of a high-risk collision point, and the collision factor of the high-risk collision point includes: Among them, C R1 , C R2 , C R3 and C R4 are the collision factors of high-risk collision points R1, R2, R3, and R4; y jud1 ,y jud2 ,y jud3 ,y jud4 y are the maximum ordinate values ​​of the cross-section of the vehicle at the horizontal coordinate positions of the high-risk collision points R1, R2, R3, and R4, respectively; r1 ,y r2 ,y r3 and r4 They are the ordinate values ​​of the high-risk collision points R1, R2, R3, and R4 respectively.

6. The offline determination method for the passability of an unmanned elk test based on positioning signals according to claim 5 is characterized in that: The calculation model of the maximum vertical coordinate value of the cross-section of the vehicle at the horizontal coordinate position shown by the high-risk collision point at the left boundary of the vehicle is: In the formula, is the coordinate of the left front corner, is the coordinate of the left rear corner, x ri The high risk collision point R i Coordinates in the test coordinate system.

7. The offline determination method for the passability of an unmanned elk test based on positioning signals according to claim 1 is characterized in that: The collision factor also includes a collision factor of a high-risk collision boundary, and the collision factor of the high-risk collision boundary includes: Among them, C L1 , C L2 , C L3 are the collision factors of high-risk collision boundaries L1, L2, and L3, respectively; are the ordinate value sets 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 test; L1 ,y L2 ,y L3 They are the ordinate values ​​of L1, L2 and L3 in the test coordinate system respectively.

8. The offline determination method for the passability of an unmanned elk test based on positioning signals according to claim 1 is characterized in that: The calculation model of the elk test passability judgment factor is: When P moose =1, the elk test passes; when P moose =0, the elk test failed; Among them, C R1 ,C R2 ,C R3 ,C R4 ,C L1 ,C L2 ,C L3 They are the collision judgment factors for high-risk collision points and high-risk collision boundaries respectively.

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