An Unmanned Elk Testing System and Method for Intelligent Chassis

Abstract

The present invention relates to the technical field of intelligent chassis testing, and particularly relates to an unmanned moose test system and method for an intelligent chassis. The unmanned moose test control system includes a main controller, a driving robot, a positioning system and a remote data supervision system. The unmanned moose test method includes: unmanned execution of moose test and digital verification of passability. Unmanned execution of moose test means that after the unmanned moose control system manipulates the vehicle to reach the test vehicle speed, it follows the target path and passes through the emergency obstacle avoidance lane change lane. Digital verification of passability is to calculate the overlap degree between the vehicle and the lane markings based on the vehicle's longitude and latitude positioning information, the installation position of the positioning antenna, and the basic information of the vehicle, and to determine whether the moose test is passed.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent chassis testing, and in particular to an unmanned elk testing system and method for an intelligent chassis. Background Art

[0002] In the field of automotive engineering, the vehicle's handling stability and emergency obstacle avoidance capabilities are important indicators for measuring the vehicle's chassis safety and overall performance, among which the moose test is a crucial link.

[0003] Traditional moose tests mainly rely on the driver to manually operate the vehicle. During the test, the driver needs to drive the vehicle at different speeds along the specified route in a specific venue to perform emergency avoidance operations on simulated obstacles, in order to evaluate the vehicle's handling stability and emergency avoidance capabilities.

[0004] However, this traditional method of moose testing that relies on drivers has many shortcomings that are difficult to overcome.

[0005] Individual differences among drivers greatly affect the accuracy and repeatability of test results. Different drivers have different reaction speeds, operating habits, driving experience, and understanding of test instructions. Such human factors lead to deviations in test results, resulting in a lack of reliable horizontal comparison between test data, which is not conducive to performance optimization and quality control in the process of automobile R&D and production.

[0006] Traditional testing methods have safety risks. The moose test requires the vehicle to perform extreme operations such as emergency steering at high speeds. If you are not careful, it may cause serious accidents such as vehicle loss of control and collision, posing a direct threat to the driver's life safety.

[0007] Traditional testing is inefficient. Each test requires the driver to make adequate preparations, including familiarizing themselves with the test site, adjusting the vehicle status, and adjusting their psychology. Moreover, due to the risks and uncertainties in the test process, drivers usually need to make multiple attempts to complete a relatively ideal test, which undoubtedly consumes a lot of time and manpower costs.

[0008] Based on this, there is an urgent need for an unmanned elk testing system and method for intelligent chassis, which can solve the problems of low accuracy, poor safety and low efficiency corresponding to the existing elk testing, and realize accurate, safe and efficient unmanned elk testing. Summary of the invention

[0009] One of the purposes of the present invention is to provide an unmanned elk testing system and method for an intelligent chassis, which can solve the problems of low accuracy, poor safety and low efficiency corresponding to the existing elk testing, and realize accurate, safe and efficient unmanned elk testing.

[0010] In order to achieve the above-mentioned purpose, an unmanned elk test system for an intelligent chassis is provided, comprising an unmanned elk test control system arranged on a vehicle and a driving robot for controlling a steering wheel and pedals of the vehicle;

[0011] The unmanned elk test control system includes an unmanned elk test main controller, a positioning system, a CAN receiving module, and a CAN sending module. The unmanned elk test main controller is electrically connected to the CAN receiving module and the CAN sending module respectively. The output end of the positioning system is connected to the input end of the CAN receiving module, and the output end of the CAN sending module is connected to the input end of the driving robot;

[0012] The unmanned elk test main controller includes an elk test passing vehicle speed judgment module and an elk test control module;

[0013] The elk test control module is used to collect the vehicle latitude and longitude positioning information corresponding to the positioning system through the CAN receiving module, and is also used to generate a control instruction according to the collected vehicle latitude and longitude positioning information, and send it to the driving robot through the CAN sending module, instructing the driving robot to control the vehicle steering wheel and pedals, simulating the vehicle passing the elk test;

[0014] The moose test passing speed judgment module is used to judge whether the vehicle has passed the moose test and determine the moose test passing speed corresponding to the vehicle passing the moose test based on a preset test passing speed judgment strategy during the process of simulating the vehicle passing the moose test.

[0015] Technical principle and effect of this scheme: In this scheme, the system consists of an unmanned elk test control system and a driving robot installed on the vehicle. The positioning system in the unmanned elk test control system obtains the vehicle's positioning information in real time, and the information is transmitted to the unmanned elk test main controller through the CAN receiving module. After the main controller processes the information, it sends the control command to the driving robot through the CAN sending module, thereby realizing the control of the vehicle's steering wheel and pedals, forming a complete closed-loop system of information collection, processing and command execution.

[0016] The elk test control module obtains the vehicle latitude and longitude positioning information from the positioning system through the CAN receiving module. This information contains key data such as the vehicle's position in the test site and the direction of travel. Based on this data, the module can understand the vehicle's status in real time and generate corresponding control instructions according to the rules and requirements of the elk test. For example, when the vehicle approaches a specific location in the test route, the control instruction will instruct the driving robot to turn the steering wheel to simulate the vehicle's action of avoiding obstacles, or control the pedal to adjust the vehicle speed, thereby simulating the vehicle's driving process in the elk test.

[0017] In the process of simulating a vehicle passing the moose test, the moose test passing speed judgment module makes a judgment based on a preset test passing speed judgment strategy, and determines the moose test passing speed corresponding to the vehicle passing the moose test.

[0018] In traditional manual driving tests, different testers have significant differences in driving habits, reaction speed, and operating strength. For example, some testers steer the wheel quickly, while others steer relatively slowly, which can cause large fluctuations in test results. The unmanned elk test system uses the unmanned elk test control system to work in conjunction with the driving robot, and performs tests strictly in accordance with preset procedures, eliminating test deviations caused by human factors and ensuring that each test result accurately reflects the vehicle's own performance.

[0019] During vehicle development, it is often necessary to conduct multiple moose tests on different configurations or improved versions of the same vehicle model to verify the performance improvement effect. The automated system can repeatedly perform tests with completely consistent test conditions, such as initial vehicle speed, steering wheel rotation angle range, pedal control rhythm, etc. Whether the test is conducted once or a hundred times, the process and operation of each test remain highly consistent, providing a stable and reliable data basis for vehicle performance evaluation, greatly improving the credibility of the test results.

[0020] The system's moose test uses a vehicle speed judgment module, which can accurately determine whether the vehicle can complete the unmanned moose test at the corresponding speed according to the preset strategy during the test. This data directly reflects the vehicle's safe handling limit under extreme conditions, helping vehicle manufacturers to clearly define the vehicle's safety performance boundaries and point the way for subsequent safety design and performance optimization. If the moose test still fails after multiple tests at a certain speed, the vehicle speed data can be determined. Based on the determined speed data, vehicle manufacturers can optimize and upgrade key safety components and systems such as the vehicle's braking system, tire grip, electronic stability program, and intelligent chassis.

[0021] In traditional manual driving tests for elk, testers need to prepare before each test, adjust the driving posture, familiarize themselves with the test route, etc. After the test is completed, they need to manually record and organize the data, and the whole process takes a long time. However, this automated testing system can complete everything from vehicle startup, test execution to data collection and recording in a short time. For example, a manual test may take half an hour to complete a test, while the automated system only takes a few minutes, greatly shortening the single test cycle. That is, it can solve the problems of low accuracy, poor safety and low efficiency corresponding to the existing elk test, and realize accurate, safe and efficient unmanned elk testing.

[0022] Further, the moose test control module includes an initial module and a path tracking module; the moose test passing vehicle speed judgment module includes a vehicle passability judgment module;

[0023] The initialization module is used to initialize the vehicle target path and path tracking control parameters during a round of elk test and determine the current elk test entry speed corresponding to the current round of elk test;

[0024] The path tracking module is used to determine the vehicle target path corresponding to the current round of elk test according to the vehicle target path and the path tracking control parameters based on the preset elk passing path parameterization rules in this round of elk test, and control the vehicle to track the vehicle at the current elk test entry speed through the emergency obstacle avoidance lane corresponding to the vehicle target path based on the determined vehicle target path;

[0025] The vehicle passability judgment module is used to judge whether the vehicle has passed the corresponding moose test in this round of moose test based on the preset vehicle passability judgment strategy, according to the vehicle latitude and longitude positioning information collected by the positioning system and the preset vehicle basic information after the vehicle passes through the corresponding emergency obstacle avoidance lane.

[0026] Beneficial effects: In this solution, at the beginning of each round of elk testing, the vehicle target path and path tracking control parameters are initialized to lay the foundation for the subsequent test process. This operation standardizes the entire test process. No matter how many rounds of testing are conducted, each round can be carried out from a standard and clear initial state, avoiding test errors caused by residual data or state interference from the previous round of testing, greatly simplifying the complexity of the test process, and eliminating the need for testers to manually perform a lot of tedious parameter settings and path planning before each test.

[0027] According to the vehicle target path and path tracking control parameters, the vehicle target path corresponding to each round of elk test is accurately determined according to the preset vehicle target path determination strategy. This process can highly simulate various emergency avoidance scenarios that may occur on real roads, such as simulating different curve radii and different obstacle spacings. At the same time, the vehicle is controlled to track the emergency obstacle avoidance lane corresponding to the vehicle target path at the current elk test entry speed, ensuring that the vehicle's driving state during the test is highly consistent with the preset scenario, providing reliable guarantee for accurately evaluating the vehicle's handling performance under different working conditions, and achieving accurate and reliable judgment on whether the vehicle has passed the elk test.

[0028] Furthermore, the parameterization rule of the elk path is:

[0029] The first horizontal coordinate vector corresponding to the vehicle target path in the test coordinate system Determine the sampling range and set the corresponding sampling interval;

[0030] According to the first horizontal coordinate vector The determined sampling range, sampling interval, corresponding vehicle target path and path tracking control parameters are used to calculate the first ordinate vector corresponding to the vehicle target path in the test coordinate system based on the preset first ordinate vector calculation formula. , forming a corresponding vehicle target path through the first abscissa vector and the first ordinate vector;

[0031] The preset first ordinate vector calculation formula;

[0032]

[0033]

[0034]

[0035] In the formula, and is the intermediate variable, , , , , , , are the 7 parameters in the vehicle target path parameters. is the path shape influencing factor, , is the corresponding lateral key position influencing factor, , is the corresponding lateral velocity influence factor, , is the corresponding longitudinal velocity influence factor.

[0036] Beneficial effects: By determining the sampling range of the first horizontal coordinate vector corresponding to the target path of the vehicle 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 that the vehicle may travel in the 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 too dense or sparse sampling points.

[0037] The seven parameters of the vehicle target path parameters are comprehensively considered to accurately describe the changes of the target path in the ordinate direction. By accurately calculating the first ordinate vector, an accurate and expected vehicle target path can be generated, providing an accurate reference for the path tracking of subsequent vehicles.

[0038] Furthermore, the vehicle passability judgment strategy is:

[0039] The vehicle latitude and longitude positioning information collected by the positioning system is converted into the global Cartesian coordinate system, and the coordinates in the global Cartesian coordinate system are converted into the test coordinate system to form the corresponding vehicle positioning point coordinates; and according to the vehicle positioning point coordinates and the preset vehicle basic information, the vehicle outer contour corner point coordinates corresponding to the vehicle are calculated, and the vehicle outer contour corner points include the left front corner point , left rear corner , right front corner , right back corner point ;

[0040] The coordinates of the corner points of the vehicle outer contour are calculated as follows:

[0041]

[0042]

[0043]

[0044]

[0045] In the formula, is the width of the vehicle, is the length of the vehicle, To locate the distance between the main antenna and the front envelope of the vehicle, To locate the distance between the main antenna and the left envelope of the vehicle, is the coordinate of the vehicle positioning point corresponding to the test coordinate system, To test the heading of the vehicle in the coordinate system, Left front corner Coordinates in the test coordinate system;

[0046] According to the coordinates of the corner points of the vehicle's outer contour in the test coordinate system, based on the preset basic vehicle information and the preset vehicle envelope The value calculation formula is used to calculate the vehicle's collision risk in each high-risk collision area in the test coordinate system. The corresponding vehicle envelope under the value value;

[0047] The preset vehicle envelope is located at the high risk point The value calculation formula is:

[0048]

[0049]

[0050]

[0051]

[0052] In the formula, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section value, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section value, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section value, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section value;

[0053] According to the calculated high-risk collision areas of the vehicle in the test coordinate system The corresponding vehicle envelope under the value Value, based on the preset collision judgment logic, determines whether the vehicle passes the corresponding elk test in this round of elk test. The high-risk collision area includes 4 high-risk collision points , , and , and 3 high-risk collision boundaries , and .

[0054] Beneficial effects: In this solution, the latitude and longitude positioning information of the vehicle collected by the positioning system is converted to a global Cartesian coordinate system, and then converted to a test coordinate system to form the coordinates of the vehicle positioning point. This process builds a unified spatial reference framework for the entire test. In different rounds and different test environments, the vehicle's location information can be measured with the same standard, avoiding data errors and analysis difficulties caused by coordinate system confusion. The system can accurately depict the actual outer contour of the vehicle in the test coordinate system, providing accurate basic data for subsequent judgments on whether the vehicle will collide with high-risk areas. Compared with the method of simply estimating the vehicle's contour, this strategy can more realistically reflect the actual space occupied by the vehicle in the test.

[0055] Based on the calculated y value of the vehicle envelope, combined with the preset collision judgment logic, a judgment is made on whether the vehicle has passed the moose test. This judgment process comprehensively considers 4 high-risk collision points and 3 high-risk collision boundaries, and comprehensively covers the areas where collision risks may occur in the moose test. Compared with the method of judging the vehicle's passability based only on some key positions, this strategy is more rigorous and can effectively avoid misjudgment caused by missing potential collision risk points, providing reliable judgment results for vehicle performance evaluation.

[0056] Furthermore, the collision judgment logic is:

[0057] According to the calculated high-risk collision areas of the vehicle in the test coordinate system The corresponding vehicle envelope under the value The collision factor corresponding to each high-risk collision area in the elk test is calculated based on the collision factor calculation formula;

[0058] The collision factor calculation formula is:

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] In the formula, They are the left front, left rear, right front and right rear corners of the vehicle in the full time sequence of the elk test execution. A collection of values, and It is a high risk collision boundary In the test coordinate system Towards position; The left front, left rear, right front and right rear corners of the vehicle are in the test coordinate system. Corresponding A collection of values, It is a high risk collision boundary The y-position in the test coordinate system; for Point collision judgment factor, for Point collision judgment factor, for Point collision judgment factor, for Point collision judgment factor, for Boundary collision judgment factor, for Boundary collision judgment factor, for Boundary collision judgment factor;

[0067] According to the collision factors corresponding to each high-risk collision area in the elk test, the corresponding elk test pass judgment factor is calculated ;

[0068] The calculation of the elk test passability judgment factor is as follows:

[0069]

[0070] If the judgment factor , then the vehicle passes the elk test. If the judgment factor , the vehicle fails the moose test.

[0071] Beneficial effect: When calculating the collision factor of the collision boundary, such as The calculation takes into account the left front, left rear, right front and right rear corners of the vehicle in the test coordinate system. Corresponding This means that we not only focus on the position of the vehicle at a certain moment, but also comprehensively consider the changes in the position of the vehicle during a period of driving time. Compared with judging only based on a certain instantaneous position, it can more comprehensively and truly reflect the actual risk of the vehicle when passing through the collision boundary area, because the position of the vehicle may fluctuate due to dynamic control during driving, and this calculation method effectively captures the risk changes caused by such fluctuations.

[0072] The collision factors corresponding to each high-risk collision area are integrated into the moose test pass judgment factor. This integration method provides a unified and scientific judgment standard for whether a vehicle has passed the moose test. Regardless of which high-risk area the vehicle faces during the test, it can be evaluated as a whole through this comprehensive judgment factor.

[0073] The present invention also provides an unmanned elk testing method for a smart chassis, using the above-mentioned unmanned elk testing system for a smart chassis. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a logic block diagram of an unmanned elk testing system for a smart chassis in Embodiment 1 of the present invention;

[0075] Figure 2 This is a display diagram corresponding to the vehicle in the test coordinate system in the first embodiment of the present invention;

[0076] Figure 3 This is a schematic diagram showing a high-risk collision area in Embodiment 1 of the present invention;

[0077] Figure 4 The vehicle and the high-risk collision point in the first embodiment of the present invention Schematic diagram of the collision. DETAILED DESCRIPTION

[0078] The following is further described in detail through specific implementation methods:

[0079] Embodiment 1

[0080] An unmanned elk testing system for intelligent chassis, basically Figure 1 As shown, it includes an unmanned elk test control system arranged on a vehicle; the unmanned elk test control system includes an unmanned elk test main controller, a positioning system, a CAN receiving module, a CAN sending module and a driving robot for controlling the steering wheel and pedals of the vehicle, the unmanned elk test main controller is electrically connected to the CAN receiving module and the CAN sending module respectively, the output end of the positioning system is connected to the input end of the CAN receiving module, and the output end of the CAN sending module is connected to the input end of the driving robot;

[0081] The unmanned elk test main controller includes an elk test speed judgment module and an elk test control module; in this embodiment, it also includes a power supply system, the power supply system includes a low-voltage power supply and a high-voltage power supply, the low-voltage power supply is used to power the positioning system and the unmanned elk test main controller, and the high-voltage power supply is used to power the driving robot. At the same time, the data sent by the CAN sending module will also be monitored by the remote data supervision system. In this embodiment, an intermediate server needs to be established, and the CAN sending module is connected to the intermediate server through a serial port, Ethernet, etc. The data sent by the CAN sending module is processed by the intermediate server and sent to the remote data supervision system.

[0082] The elk test control module is used to collect the vehicle latitude and longitude positioning information corresponding to the positioning system through the CAN receiving module, and is also used to generate a control instruction according to the collected vehicle latitude and longitude positioning information, and send it to the driving robot through the CAN sending module, instructing the driving robot to control the vehicle steering wheel and pedals, and control the vehicle to perform the elk test;

[0083] The moose test passing speed judgment module is used to judge whether the vehicle has passed the moose test and determine the vehicle's moose test passing speed based on a preset test passing speed judgment strategy after the vehicle completes the moose test.

[0084] The moose test control module includes an initial module and a path tracking module; the moose test passing speed judgment module includes a vehicle passability judgment module;

[0085] The initialization module is used to initialize the vehicle target path and path tracking control parameters during a round of elk test and determine the current elk test entry speed corresponding to the current round of elk test;

[0086] The path tracking module is used to determine the vehicle target path corresponding to this round of elk test according to the vehicle target path and path tracking control parameters based on the preset elk passing path parameterization rules in this round of elk test, and control the vehicle to track the emergency obstacle avoidance lane corresponding to the vehicle target path at the current elk test entry speed based on the determined vehicle target path; in this embodiment, the emergency obstacle avoidance lane is the emergency obstacle avoidance lane specified in GB / T 40521.

[0087] The parameterization rule of the elk path is:

[0088] The first horizontal coordinate vector corresponding to the vehicle target path in the test coordinate system Determine the sampling range and set the corresponding sampling interval;

[0089] According to the first horizontal coordinate vector The determined sampling range, sampling interval, corresponding vehicle target path and path tracking control parameters are used to calculate the first ordinate vector corresponding to the vehicle target path in the test coordinate system based on the preset first ordinate vector calculation formula. , forming a corresponding vehicle target path through the first abscissa vector and the first ordinate vector;

[0090] Preset calculation formula for the first ordinate vector;

[0091]

[0092]

[0093]

[0094] In the formula, and is the intermediate variable, , , , , , , are the 7 parameters in the vehicle target path parameters. is the path shape influencing factor, , is the corresponding lateral key position influencing factor, , is the corresponding lateral velocity influence factor, , is the corresponding longitudinal velocity influence factor.

[0095] In this embodiment, based on the determined vehicle target path, the tracking logic of controlling the vehicle to track the vehicle at the current elk test entry speed through the emergency obstacle avoidance lane corresponding to the corresponding vehicle target path is:

[0096]

[0097]

[0098] In the formula, is the preview error angle, , and are the longitudinal position, lateral position and heading angle of the vehicle positioning point in the experimental coordinate system, is the real-time speed of the vehicle, For preview time, is the vehicle wheelbase. is an important control parameter of pure tracking and is strongly related to the accuracy of path tracking. It is one of the vehicle target path and path tracking control parameters. is the steering wheel angle.

[0099] The vehicle passability judgment module is used to judge whether the vehicle has passed the corresponding moose test in this round of moose test based on the preset vehicle passability judgment strategy, according to the vehicle latitude and longitude positioning information collected by the positioning system and the preset vehicle basic information after the vehicle passes through the corresponding emergency obstacle avoidance lane.

[0100] The vehicle passability judgment strategy is:

[0101] The vehicle latitude and longitude positioning information collected by the positioning system is converted into the global Cartesian coordinate system, and the coordinates in the global Cartesian coordinate system are converted into the test coordinate system to form the corresponding vehicle positioning point coordinates; and according to the vehicle positioning point coordinates and the preset vehicle basic information, the vehicle outer contour corner point coordinates corresponding to the vehicle are calculated, and the vehicle outer contour corner points include the left front corner point , left rear corner , right front corner , right rear corner point In this embodiment, the test coordinate system is as follows Figure 2 As shown. The vehicle longitude and latitude positioning information includes the longitude and latitude and heading data corresponding to the vehicle positioning. In the test coordinate system, the vehicle is simplified into a rectangle, and the coordinates of the corner points of the vehicle outer contour are calculated for the four corner points of the vehicle shape simplified into a rectangle.

[0102] The coordinates of the corner points of the vehicle outer contour are calculated as follows:

[0103]

[0104]

[0105]

[0106]

[0107] In the formula, is the width of the vehicle, is the length of the vehicle, To locate the distance between the main antenna and the front envelope of the vehicle, To locate the distance between the main antenna and the left envelope of the vehicle, is the coordinate of the vehicle positioning point corresponding to the test coordinate system, To test the heading of the vehicle in the coordinate system, Left front corner Coordinates in the test coordinate system;

[0108] According to the coordinates of the corner points of the vehicle's outer contour in the test coordinate system, based on the preset basic vehicle information and the preset vehicle envelope The value calculation formula is used to calculate the vehicle's collision risk in each high-risk collision area in the test coordinate system. The corresponding vehicle envelope under the value value;

[0109] The preset vehicle envelope is located at the high risk point The value calculation formula is:

[0110]

[0111]

[0112]

[0113]

[0114] In the formula, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section value, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section value, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section value, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section value;

[0115] According to the calculated high-risk collision areas of the vehicle in the test coordinate system The corresponding vehicle envelope under the value Value, based on the preset collision judgment logic, determines whether the vehicle passes the corresponding elk test in this round of elk test. The high-risk collision area includes 4 high-risk collision points , , and , and 3 high-risk collision boundaries , and In this embodiment, the high-risk collision area is selected by selecting some points at the high boundary of the lane of the elk test emergency lane change test. Figure 3As shown, a schematic diagram of the corresponding high-risk collision area is shown, where the anchor point is the lower left corner of the emergency lane change test lane.

[0116] The collision judgment logic is:

[0117] According to the calculated high-risk collision areas of the vehicle in the test coordinate system The corresponding vehicle envelope under the value Value, calculate the collision factor corresponding to each high-risk collision area in the elk test;

[0118] The collision factor calculation formula is:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126] In the formula, They are the left front, left rear, right front and right rear corners of the vehicle in the full time sequence of the elk test execution. A collection of values, and It is a high risk collision boundary In the test coordinate system Towards position; The left front, left rear, right front and right rear corners of the vehicle are in the test coordinate system. Corresponding A collection of values, It is a high risk collision boundary The y-position in the test coordinate system; for Point collision judgment factor, for Point collision judgment factor, for Point collision judgment factor, for Point collision judgment factor, for Boundary collision judgment factor, for Boundary collision judgment factor, for Boundary collision judgment factor;

[0127] According to the collision factors corresponding to each high-risk collision area in the elk test, the corresponding elk test pass judgment factor is calculated ;

[0128] The calculation of the elk test passability judgment factor is as follows:

[0129]

[0130] If the judgment factor , then the vehicle passes the elk test. If the judgment factor , the vehicle fails the moose test.

[0131] This embodiment also discloses an unmanned elk testing method for a smart chassis, using the above-mentioned unmanned elk testing system for a smart chassis.

[0132] Embodiment 2

[0133] Compared with the first embodiment, the difference of this embodiment is that: the preset collision judgment logic is:

[0134] According to the calculated high-risk collision areas of the vehicle in the test coordinate system The corresponding vehicle envelope under the value The collision factor corresponding to each high-risk collision area in the elk test is calculated based on the collision factor calculation formula;

[0135] The collision factor calculation formula is:

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] In the formula, They are the left front, left rear, right front and right rear corners of the vehicle in the full time sequence of the elk test execution. A collection of values, and It is a high risk collision boundary In the test coordinate system Towards position; The left front, left rear, right front and right rear corners of the vehicle are in the test coordinate system. Corresponding A collection of values, It is a high risk collision boundary The y-position in the test coordinate system; for Point collision judgment factor, for Point collision judgment factor, for Point collision judgment factor, for Point collision judgment factor, for Boundary collision judgment factor, for Boundary collision judgment factor, for Boundary collision judgment factor;

[0144] According to the collision factors corresponding to each high-risk collision area in the elk test, the corresponding elk test pass judgment factor is calculated ;

[0145] The calculation of the elk test passability judgment factor is as follows:

[0146]

[0147] If the judgment factor , then the vehicle passes the elk test. If the judgment factor , then the vehicle fails the elk test. In this embodiment, if any of the four high-risk collision points or three high-risk collision boundaries collides, the elk test fails. On the contrary, only if none of the four high-risk collision points and the three high-risk collision boundaries collide, the elk test passes. This embodiment does not rely on whether the obstacle avoidance test lane marking specified in the standard is collided as the basis for judging the passability of the elk test, avoiding the ambiguity of the passability judgment caused by the airflow blown by the test lane marking generated by the movement of the vehicle; secondly, the digital judgment method can avoid the safety risks caused by the wheels being involved in the cone barrels of the obstacle avoidance test lane markings; finally, the digital judgment method automatically generates an obstacle avoidance test lane according to the standard when entering the unmanned elk test state for collision judgment, which can ensure that each time the experimental state is entered, the vehicle's heading and position are fixed relative to the obstacle avoidance test lane, effectively avoiding the problem that the initial conditions for entering the experimental state are changed due to the motion error before the vehicle enters the experimental state before the experiment, and the original elk test results cannot be effectively reproduced.

[0148] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is described too much here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can know all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The 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 deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope 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. An unmanned elk testing system for intelligent chassis, characterized by: Includes unmanned elk test main controller, positioning system and driving robot; The unmanned elk test main controller includes: The path tracking module is used to determine the vehicle target path corresponding to the current round of elk test according to the vehicle target path and the path tracking control parameters in this round of elk test, and based on the determined vehicle target path, control the vehicle to track the vehicle at the current elk test entry speed through the emergency obstacle avoidance lane corresponding to the vehicle target path; The vehicle passability judgment module is used to judge whether the vehicle has passed the corresponding elk test in this round of elk test after the vehicle passes the corresponding emergency obstacle avoidance lane; The specific judgment is as follows: According to the vehicle latitude and longitude positioning information collected by the positioning system and the preset vehicle basic information, the coordinates of the vehicle outer contour corner points corresponding to the vehicle in the test coordinate system are calculated; According to the coordinates of the vehicle outer contour corner points in the test coordinate system and the preset vehicle basic information, based on the preset vehicle envelope The value calculation formula is used to calculate the vehicle's collision risk in each high-risk collision area in the test coordinate system. The corresponding vehicle envelope under the value value; According to the vehicle in each high-risk collision area in the test coordinate system The corresponding vehicle envelope under the value Value, based on the preset collision judgment logic, determines whether the vehicle passes the corresponding elk test in this round of elk test; The vehicle outer contour corner points include the left front corner point , left rear corner , right front corner , right back corner point ; The coordinates of the corner points of the vehicle outer contour are calculated as follows: In the formula, is the width of the vehicle, is the length of the vehicle, To locate the distance between the main antenna and the front envelope of the vehicle, To locate the distance between the main antenna and the left envelope of the vehicle, is the coordinate of the vehicle positioning point corresponding to the test coordinate system, To test the heading of the vehicle in the coordinate system, Left front corner Coordinates in the test coordinate system; The preset vehicle envelope is located at the high risk point The value calculation formula is: In the formula, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section value, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section value, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section value, The left side of the vehicle appears at a high-risk collision point of Position vehicle with maximum cross section The high-risk collision area includes 4 high-risk collision points. , , and , and 3 high-risk collision boundaries , and .

2. The unmanned elk testing system for intelligent chassis according to claim 1, characterized in that: Determining the vehicle target path corresponding to this round of elk test includes: The first horizontal coordinate vector corresponding to the vehicle target path in the test coordinate system Determine the sampling range and set the corresponding sampling interval; According to the first horizontal coordinate vector The determined sampling range, sampling interval, corresponding vehicle target path and path tracking control parameters are used to calculate the first ordinate vector corresponding to the vehicle target path in the test coordinate system based on the preset first ordinate vector calculation formula. , forming a corresponding vehicle target path through the first abscissa vector and the first ordinate vector; The preset first ordinate vector calculation formula; In the formula, and is the intermediate variable, , , , , , , These are the 7 parameters in the vehicle target path parameters. is the path shape influencing factor, , is the corresponding lateral key position influencing factor, , is the corresponding lateral velocity influence factor, , is the corresponding longitudinal velocity influence factor.

3. The unmanned elk testing system for intelligent chassis according to claim 2, characterized in that: The collision judgment logic is: According to the calculated high-risk collision areas of the vehicle in the test coordinate system The corresponding vehicle envelope under the value Value, calculate the collision factor corresponding to each high-risk collision area in the elk test; The collision factor calculation formula is: In the formula, They are the left front, left rear, right front and right rear corners of the vehicle in the full time sequence of the elk test execution. A collection of values, and It is a high risk collision boundary In the test coordinate system Towards position; The left front, left rear, right front and right rear corners of the vehicle are in the test coordinate system. Corresponding A collection of values, It is a high risk collision boundary The y-position in the test coordinate system; for Point collision judgment factor, for Point collision judgment factor, for Point collision judgment factor, for Point collision judgment factor, for Boundary collision judgment factor, for Boundary collision judgment factor, for Boundary collision judgment factor; According to the collision factors corresponding to each high-risk collision area in the elk test, the corresponding elk test passability judgment factor is calculated ; The calculation of the elk test passability judgment factor is as follows: If the judgment factor , then the vehicle passes the elk test. If the judgment factor , the vehicle fails the moose test.

4. The unmanned elk testing system for intelligent chassis according to claim 3, characterized in that: The unmanned elk test main controller also includes: The initialization module is used to initialize the vehicle target path and path tracking control parameters during a round of elk test and determine the current elk test entry speed corresponding to this round of elk test.

5. An unmanned elk testing method for a smart chassis, characterized in that: An unmanned elk testing system for a smart chassis using any one of claims 1 to 4 above.

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