Test method, device, equipment, medium and product of autonomous vehicle

Abstract

The application discloses a kind of test method, device, equipment, medium and product of automatic driving vehicle. The method comprises: determining motion adjustment parameter according to the first motion parameter and the second motion parameter of the current test period;Third motion parameter is obtained by inputting motion adjustment parameter to dynamics model;The first motion parameter of next test period is outputted by inputting third motion parameter to test car;The next test period is updated to current test period, and the first motion parameter and the second motion parameter of current test period are returned to be executed to obtain, until simulation driving scene test ends, and the test result of the test car is determined according to the motion adjustment parameter of each test period.The technical scheme solves the problem that test result is not high in reliability, realizes closed loop matching of test car and dynamics model, restores the real response of test car to driving scene to the greatest extent, and is favorable for improving the reliability of test result.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to a testing method, apparatus, equipment, medium, and product for autonomous vehicles. Background Technology

[0002] Currently, autonomous driving testing has become an indispensable part of the development process for autonomous vehicles. Testers deploy autonomous driving algorithms on autonomous vehicles and use these vehicles as test vehicles to test the performance of the algorithms under various operating conditions. They repeatedly test multiple driving scenarios to verify the performance and shortcomings of the autonomous driving algorithms, thereby shortening the software development cycle.

[0003] In autonomous driving testing, matching the dynamic model with the test vehicle is crucial for vehicle-in-the-loop testing. Existing technologies typically use dynamic simulation software to build dynamic models. The input and output results of the dynamic models are ideal values. In actual autonomous driving, the dynamic response is affected by multiple factors, such as engine oil temperature and clutch engagement, which means that the dynamic model cannot cover the vehicle response caused by changes in the real driving environment, resulting in low realism of the test results. Summary of the Invention

[0004] This invention provides a testing method, apparatus, equipment, medium, and product for autonomous vehicles to address the problem of low reliability in autonomous driving test results. By obtaining the actual output motion parameters of the test vehicle, the motion adjustment parameters of the dynamic model are determined, achieving closed-loop matching between the test vehicle and the dynamic model. This maximizes the reproduction of the test vehicle's real response to driving scenarios and helps improve the reliability of test results.

[0005] According to one aspect of the present invention, a testing method for an autonomous vehicle is provided, the method comprising:

[0006] Obtain the first and second motion parameters for the current test cycle; the first motion parameter is the target motion parameter output by the test vehicle based on the simulated driving scenario; the second motion parameter is the target motion parameter determined by the dynamic model based on the simulated driving scenario; the target motion parameter is a measurable motion parameter of the test vehicle.

[0007] Based on the first motion parameter and the second motion parameter, determine the motion adjustment parameters;

[0008] The motion adjustment parameters are input into the dynamic model to obtain the third motion parameters; the third motion parameters include motion parameters that are not measurable by the test vehicle and motion parameters that are measurable by the test vehicle.

[0009] The third motion parameter is input to the test vehicle so that the test vehicle outputs the first motion parameter for the next test cycle based on the third motion parameter and the simulated driving scenario.

[0010] The next test cycle is updated to the current test cycle. The process returns to obtain the first and second motion parameters of the current test cycle until the simulated driving scenario test ends. Based on the motion adjustment parameters of each test cycle, the test results of the test vehicle are determined.

[0011] According to another aspect of the present invention, a testing apparatus for an autonomous vehicle is provided, the apparatus comprising:

[0012] The motion parameter acquisition module is used to acquire the first motion parameter and the second motion parameter of the current test cycle; the first motion parameter is the target motion parameter output by the test vehicle based on the simulated driving scenario; the second motion parameter is the target motion parameter determined by the dynamic model based on the simulated driving scenario; the target motion parameter is a measurable motion parameter of the test vehicle.

[0013] The adjustment parameter determination module is used to determine motion adjustment parameters based on the first motion parameter and the second motion parameter;

[0014] The motion parameter generation module is used to input the motion adjustment parameters into the dynamic model to obtain the third motion parameter; the third motion parameter includes motion parameters that are not measurable by the test vehicle and motion parameters that are measurable by the test vehicle.

[0015] The motion parameter output module is used to input the third motion parameter to the test vehicle, so that the test vehicle outputs the first motion parameter for the next test cycle based on the third motion parameter and the simulated driving scenario;

[0016] The test result determination module is used to update the next test cycle to the current test cycle, return to obtain the first motion parameters and the second motion parameters of the current test cycle, until the simulated driving scenario test ends, and determine the test results of the test vehicle based on the motion adjustment parameters of each test cycle.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the testing method for an autonomous vehicle according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the testing method for an autonomous vehicle according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the testing method for an autonomous vehicle as described in any embodiment of the present invention.

[0021] The technical solution of this invention involves acquiring a first motion parameter and a second motion parameter for the current test cycle. The first motion parameter is a target motion parameter output by the test vehicle based on a simulated driving scenario. The second motion parameter is a target motion parameter determined by a dynamic model based on the simulated driving scenario. The target motion parameter is a measurable motion parameter of the test vehicle. Motion adjustment parameters are determined based on the first and second motion parameters. The motion adjustment parameters are input to the dynamic model to obtain a third motion parameter. The third motion parameter includes both unmeasurable and measurable motion parameters of the test vehicle. The third motion parameter is input to the test vehicle so that the test vehicle outputs the first motion parameter for the next test cycle based on the third motion parameter and the simulated driving scenario. The next test cycle is updated to the current test cycle, and the process of acquiring the first and second motion parameters for the current test cycle is repeated until the simulated driving scenario test ends. The test results of the test vehicle are determined based on the motion adjustment parameters for each test cycle. This technical solution solves the problem of low reliability of test results. By obtaining the actual output motion parameters of the test vehicle, the motion adjustment parameters of the dynamic model are determined, and closed-loop matching between the test vehicle and the dynamic model is achieved. This maximizes the reproduction of the test vehicle's real response to driving scenarios and helps improve the reliability of test results.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1This is a flowchart of a testing method for an autonomous vehicle according to Embodiment 1 of the present invention;

[0025] Figure 2 This is a flowchart of a testing method for an autonomous vehicle according to Embodiment 2 of the present invention;

[0026] Figure 3 This is a schematic diagram of the structure of a test device for an autonomous vehicle according to Embodiment 3 of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device for implementing the testing method for autonomous vehicles according to embodiments of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices. The acquisition, storage, use, and processing of data in the technical solutions of this application all comply with the relevant provisions of national laws and regulations.

[0030] Example 1

[0031] Figure 1 This is a flowchart illustrating a testing method for an autonomous vehicle according to Embodiment 1 of the present invention. This embodiment is applicable to vehicle-in-the-loop testing scenarios for autonomous vehicles, particularly for testing autonomous vehicles themselves. The method can be executed by a testing device for autonomous vehicles, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0032] S110. Obtain the first motion parameter and the second motion parameter of the current test cycle; the first motion parameter is the target motion parameter output by the test vehicle based on the simulated driving scenario; the second motion parameter is the target motion parameter determined by the dynamic model based on the simulated driving scenario; the target motion parameter is the measurable motion parameter of the test vehicle.

[0033] This solution can be executed by a vehicle-in-the-loop (VIN) testing system, which may include a test vehicle, a hardware testing platform, and a simulation testing platform. The test vehicle is the autonomous vehicle to be tested, equipped with an autonomous driving algorithm to respond to simulated driving scenarios. The hardware testing platform is used to mount the test vehicle, allowing it to run and collect some of its motion parameters. For example, a swivel test bench can be used to simulate the lateral and longitudinal motion of the test vehicle and collect its wheel angles. The simulation testing platform generates simulated driving scenarios and injects these scenarios into the test vehicle and its dynamic model. The dynamic model can run on the simulation testing platform. The hardware testing platform can communicate with the test vehicle via a CAN bus, and the simulation testing platform can communicate with the hardware testing platform via Ethernet. The simulation testing platform can inject simulated driving scenarios and motion parameters into the test vehicle through the hardware testing platform.

[0034] The simulated driving scenario can have a certain duration. The vehicle-in-the-loop testing system can acquire the motion parameters output by the test vehicle and the dynamic model according to a preset test cycle. The duration of the simulated driving scenario can be much longer than the test cycle and is an integer multiple of the test cycle. For example, the duration of the simulated driving scenario is 5 minutes, and the test cycle is 5 microseconds.

[0035] The vehicle-in-the-loop (VIL) testing system can acquire the first and second motion parameters for the current test cycle. The first motion parameter is the target motion parameter output by the autonomous driving algorithm in the test vehicle based on the simulated driving scenario, and the second motion parameter is the target motion parameter determined by the dynamics model based on the simulated driving scenario. Understandably, during testing, the hardware testing platform has difficulty acquiring all motion parameters of the autonomous vehicle during actual driving scenarios. For example, when the test vehicle performs lateral and longitudinal movements on a rotating test bench, its physical position does not change, and the VIL testing system cannot acquire the test vehicle's position parameters. Therefore, the target motion parameters are the measurable motion parameters of the test vehicle, i.e., partial motion parameters, such as vehicle speed and wheel angle.

[0036] S120. Determine motion adjustment parameters based on the first motion parameters and the second motion parameters.

[0037] Understandably, a vehicle-in-the-loop testing system can use a first motion parameter as the actual value and a second motion parameter as the simulated value, calculating the difference between the two. Based on this difference, and using a PID control model, motion adjustment parameters are determined to gradually bring the actual and simulated values ​​closer together.

[0038] S130. Input the motion adjustment parameters into the dynamic model to obtain the third motion parameters; the third motion parameters include motion parameters that are not measurable by the test vehicle and motion parameters that are measurable by the test vehicle.

[0039] The vehicle-in-the-loop testing system can input motion adjustment parameters into the dynamics model, enabling the dynamics model to calculate all motion parameters of the test vehicle based on these parameters. These third motion parameters include both measurable motion parameters such as vehicle speed and wheel angles, and unmeasurable motion parameters such as lateral and longitudinal positions. The specific parameters within the third motion parameters can be determined based on the model design of the dynamics model.

[0040] S140. Input the third motion parameter to the test vehicle so that the test vehicle outputs the first motion parameter for the next test cycle based on the third motion parameter and the simulated driving scenario.

[0041] The vehicle-in-the-loop testing system can input a third motion parameter to the test vehicle, providing it with all the necessary motion parameters to complete the next test cycle. The autonomous driving algorithm in the test vehicle can then output control commands based on the third motion parameter and simulated driving scenarios to initiate the next test cycle. Hardware testing platforms such as swivel test benches can collect the first motion parameter output by the test vehicle for the next test cycle based on the control commands.

[0042] S150. Update the next test cycle to the current test cycle, return to obtain the first motion parameters and second motion parameters of the current test cycle, until the simulated driving scenario test ends, and determine the test results of the test vehicle based on the motion adjustment parameters of each test cycle.

[0043] The vehicle-in-the-loop testing system can update the next test cycle to the current test cycle and return to execute S110-S140 until the simulated driving scenario test ends. The vehicle-in-the-loop testing system can determine whether the response of the autonomous driving algorithm in the test vehicle to the simulated driving scenario is within the preset error range based on the motion adjustment parameters of each test cycle, and then output the test results of the test vehicle.

[0044] The technical solution of this invention involves acquiring a first motion parameter and a second motion parameter for the current test cycle. The first motion parameter is a target motion parameter output by the test vehicle based on a simulated driving scenario. The second motion parameter is a target motion parameter determined by a dynamic model based on the simulated driving scenario. The target motion parameter is a measurable motion parameter of the test vehicle. Motion adjustment parameters are determined based on the first and second motion parameters. The motion adjustment parameters are input to the dynamic model to obtain a third motion parameter. The third motion parameter includes both unmeasurable and measurable motion parameters of the test vehicle. The third motion parameter is input to the test vehicle so that the test vehicle outputs the first motion parameter for the next test cycle based on the third motion parameter and the simulated driving scenario. The next test cycle is updated to the current test cycle, and the process of acquiring the first and second motion parameters for the current test cycle is repeated until the simulated driving scenario test ends. The test results of the test vehicle are determined based on the motion adjustment parameters for each test cycle. This technical solution solves the problem of low reliability of test results. By obtaining the actual output motion parameters of the test vehicle, the motion adjustment parameters of the dynamic model are determined, and closed-loop matching between the test vehicle and the dynamic model is achieved. This maximizes the reproduction of the test vehicle's real response to driving scenarios and helps improve the reliability of test results.

[0045] Example 2

[0046] Figure 2 This is a flowchart of a testing method for an autonomous vehicle provided in Embodiment 2 of the present invention. This embodiment is a refinement based on the above embodiment. Figure 2 As shown, the method includes:

[0047] S210. Obtain the first motion parameter and the second motion parameter of the current test cycle; the first motion parameter is the target motion parameter output by the test vehicle based on the simulated driving scenario; the second motion parameter is the target motion parameter determined by the dynamic model based on the simulated driving scenario; the target motion parameter is a measurable motion parameter of the test vehicle.

[0048] In this scheme, the target motion parameters include vehicle speed and wheel angle; the motion adjustment parameters include vehicle speed adjustment parameters and wheel angle adjustment parameters.

[0049] In one feasible approach, the dynamic model is a mathematical model obtained by performing dynamic simulations based on the structural parameters of the test vehicle; the structural parameters include vehicle appearance parameters, power structure parameters, and steering structure parameters.

[0050] Specifically, the vehicle appearance parameters may include the test vehicle's mass, body length, body width, body height, wheelbase, and tire specifications. The powertrain parameters may include the operating parameters of the test vehicle's engine, transmission, clutch, driveshaft, and other powertrain components, such as engine horsepower and torque. The steering parameters may include the operating parameters of the test vehicle's steering wheel, steering gear, and steering rod, such as steering ratio and turning radius. The vehicle-in-the-loop testing system can design a dynamic model of the test vehicle based on its structural parameters and using dynamics software such as CarSim.

[0051] S220. Based on the difference between the first motion parameter and the second motion parameter, determine the motion adjustment parameter using a PID control model.

[0052] In one feasible solution, determining the motion adjustment parameters based on the difference between the first motion parameter and the second motion parameter, using a PID control model, includes:

[0053] Based on the difference between the first vehicle speed and the second vehicle speed, the vehicle speed adjustment parameters are determined using a PID control model.

[0054] Based on the difference between the first wheel angle and the second wheel angle, the angle adjustment parameters are determined using a PID control model.

[0055] Understandably, the first vehicle speed is the vehicle speed in the first motion parameter, the second vehicle speed is the vehicle speed in the second motion parameter, the first wheel angle is the wheel angle in the first motion parameter, and the second wheel angle is the wheel angle in the second motion parameter.

[0056] Since the actual response of the test vehicle during the vehicle-in-the-loop test originates from chassis movements, the detailed parameters of the vehicle chassis in the dynamics model do not need to be overly concerned. The simulation visualization requirements can be covered once the dynamics model is built. The vehicle-in-the-loop test system can determine the vehicle speed adjustment parameters based on the difference between the first and second vehicle speeds using a PID control model. These speed adjustment parameters can be throttle opening, braking force, or engine torque. The PID control model can synchronize the dynamics model and the test vehicle's movements according to the simulated driving scenario, adjusting the vehicle speed synchronously to achieve longitudinal matching between the dynamics model and the test vehicle.

[0057] During the lateral matching process between the dynamic model and the test vehicle, the principles of the electric power steering (EPS) system and specific parameter details such as the steering rack-and-pinion ratio are not considered. This is because the steering relationship between the steering wheel input command and the actual wheel steering is affected by many factors such as vehicle speed, driving resistance, and gradient, making it impossible to directly establish a white-box steering relationship model. Therefore, the wheel hub test bench is connected to the test vehicle's wheels via a flange assembly, allowing the test bench to collect the actual wheel angle values. The vehicle-in-the-loop testing system can determine the angle adjustment parameters based on the difference between the first and second wheel angles, using a PID control model. These angle adjustment parameters can be the changes in wheel angle.

[0058] S230. Input the motion adjustment parameters into the dynamic model to obtain the third motion parameters; the third motion parameters include motion parameters that are not measurable by the test vehicle and motion parameters that are measurable by the test vehicle.

[0059] Optionally, the third motion parameter includes the test vehicle's speed parameter, attitude parameter, and position parameter.

[0060] As is easily understood, the third motion parameter can include speed parameters such as vehicle speed, acceleration, lateral velocity, and longitudinal velocity; attitude parameters such as center of gravity, track width, front overhang, and rear overhang; and map position and turn signal position parameters of the test vehicle in the simulated driving scenario. The vehicle-in-the-loop testing system can inject various motion parameters into the test vehicle for subsequent testing.

[0061] S240. Input the third motion parameter to the test vehicle so that the test vehicle outputs the first motion parameter for the next test cycle based on the third motion parameter and the simulated driving scenario.

[0062] S250. Update the next test cycle to the current test cycle, return to obtain the first motion parameters and second motion parameters of the current test cycle, until the simulated driving scenario test ends, and determine the test results of the test vehicle based on the motion adjustment parameters of each test cycle.

[0063] The technical solution of this invention only requires basic vehicle structural parameters to construct a dynamic model, without needing detailed parameters such as engine characteristic curves, transmission shift logic, and suspension parameters, nor requiring extensive testing to obtain dynamic data from the test vehicle. By subtracting the target motion parameters output by the dynamic model from those output by the test vehicle, a data closed loop is formed based on a PID control model, achieving matching between the dynamic model and the test vehicle, and maximizing the reproduction of the test vehicle's realistic response to driving scenarios. The third motion parameter output by the dynamic model is injected into the test vehicle, compensating for the limitation of the test vehicle lacking certain motion parameters during the whole-vehicle-in-the-loop test.

[0064] Example 3

[0065] Figure 3 This is a schematic diagram of the structure of a testing device for an autonomous vehicle provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0066] The motion parameter acquisition module 310 is used to acquire the first motion parameter and the second motion parameter of the current test cycle; the first motion parameter is the target motion parameter output by the test vehicle based on the simulated driving scenario; the second motion parameter is the target motion parameter determined by the dynamic model based on the simulated driving scenario; the target motion parameter is a measurable motion parameter of the test vehicle.

[0067] The adjustment parameter determination module 320 is used to determine motion adjustment parameters based on the first motion parameter and the second motion parameter;

[0068] The motion parameter generation module 330 is used to input the motion adjustment parameters into the dynamic model to obtain the third motion parameter; the third motion parameter includes motion parameters that are not measurable by the test vehicle and motion parameters that are measurable by the test vehicle.

[0069] The motion parameter output module 340 is used to input the third motion parameter to the test vehicle, so that the test vehicle outputs the first motion parameter for the next test cycle based on the third motion parameter and the simulated driving scenario;

[0070] The test result determination module 350 is used to update the next test cycle to the current test cycle, return to obtain the first motion parameters and the second motion parameters of the current test cycle, until the simulated driving scenario test ends, and determine the test results of the test vehicle based on the motion adjustment parameters of each test cycle.

[0071] In this solution, the adjustment parameter determination module 320 is specifically used for:

[0072] Based on the difference between the first motion parameter and the second motion parameter, motion adjustment parameters are determined using a PID control model.

[0073] In one feasible solution, the target motion parameters include vehicle speed and wheel angle; the motion adjustment parameters include vehicle speed adjustment parameters and wheel angle adjustment parameters.

[0074] Based on the above scheme, the parameter adjustment determination module 320 is specifically used for:

[0075] Based on the difference between the first vehicle speed and the second vehicle speed, the vehicle speed adjustment parameters are determined using a PID control model.

[0076] Based on the difference between the first wheel angle and the second wheel angle, the angle adjustment parameters are determined using a PID control model.

[0077] In this embodiment, the third motion parameter includes the speed parameter, attitude parameter, and position parameter of the test vehicle.

[0078] In a preferred embodiment, the dynamic model is a mathematical model obtained by performing dynamic simulations based on the structural parameters of the test vehicle in advance; the structural parameters include vehicle appearance parameters, power structure parameters, and steering structure parameters.

[0079] The testing device for autonomous vehicles provided in this embodiment of the invention can execute the testing method for autonomous vehicles provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0080] Example 4

[0081] Figure 4 A schematic diagram of an electronic device 410 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0082] like Figure 4As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0083] Multiple components in electronic device 410 are connected to I / O interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of displays, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0084] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as testing methods for autonomous vehicles.

[0085] In some embodiments, the testing method for autonomous vehicles may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the testing method for autonomous vehicles described above may be performed. Alternatively, in other embodiments, processor 411 may be configured to perform the testing method for autonomous vehicles by any other suitable means (e.g., by means of firmware).

[0086] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0087] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other test apparatus for a programmable autonomous vehicle, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0088] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0089] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0090] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0091] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0092] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0093] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A test method of an autonomous vehicle, characterized by, The method comprises: acquiring first motion parameters and second motion parameters of a current test cycle; the first motion parameters are target motion parameters output by a test vehicle based on a simulation driving scene; the second motion parameters are target motion parameters determined by a dynamics model based on the simulation driving scene; the target motion parameters are measurable motion parameters of the test vehicle; the target motion parameters comprise vehicle speed and wheel rotation angle; determining motion adjustment parameters based on a PID control model according to a difference between the first motion parameters and the second motion parameters; the motion adjustment parameters comprise vehicle speed adjustment parameters and rotation angle adjustment parameters; inputting the motion adjustment parameters to the dynamics model to obtain third motion parameters; the third motion parameters comprise non-measurable motion parameters of the test vehicle and measurable motion parameters of the test vehicle; the third motion parameters comprise speed parameters, attitude parameters and position parameters of the test vehicle; the dynamics model is a mathematical model obtained by pre-dynamics simulation based on structure parameters of the test vehicle; the structure parameters comprise vehicle appearance parameters, power structure parameters and steering structure parameters; inputting the third motion parameters to the test vehicle to enable the test vehicle to output first motion parameters of a next test cycle based on the third motion parameters and the simulation driving scene; updating the next test cycle to the current test cycle and returning to acquire the first motion parameters and the second motion parameters of the current test cycle until the simulation driving scene test ends and a test result of the test vehicle is determined according to the motion adjustment parameters of each test cycle.

2. The method of claim 1, wherein, The determining of the motion adjustment parameters based on the PID control model according to the difference between the first motion parameters and the second motion parameters comprises: determining vehicle speed adjustment parameters based on the PID control model according to a difference between first vehicle speed and second vehicle speed; determining rotation angle adjustment parameters based on the PID control model according to a difference between first wheel rotation angle and second wheel rotation angle.

3. A test device for an autonomous vehicle, characterized in that The device comprises: a motion parameter acquisition module configured to acquire first motion parameters and second motion parameters of a current test cycle; the first motion parameters are target motion parameters output by a test vehicle based on a simulation driving scene; the second motion parameters are target motion parameters determined by a dynamics model based on the simulation driving scene; the target motion parameters are measurable motion parameters of the test vehicle; the target motion parameters comprise vehicle speed and wheel rotation angle; an adjustment parameter determination module configured to determine motion adjustment parameters based on a PID control model according to a difference between the first motion parameters and the second motion parameters; the motion adjustment parameters comprise vehicle speed adjustment parameters and rotation angle adjustment parameters; The motion parameter generation module is configured to input the motion adjustment parameter into the dynamics model to obtain third motion parameters; the third motion parameters include unmeasurable motion parameters of the test vehicle and measurable motion parameters of the test vehicle; the third motion parameters include speed parameters, attitude parameters and position parameters of the test vehicle; the dynamics model is a mathematical model obtained by pre-dynamics simulation based on structure parameters of the test vehicle; the structure parameters include vehicle appearance parameters, power structure parameters and steering structure parameters; The motion parameter output module is configured to input the third motion parameters into the test vehicle, so that the test vehicle outputs first motion parameters of a next test period based on the third motion parameters and the simulation driving scene. The test result determination module is configured to update a next test period to a current test period, return to execute the acquisition of the first motion parameters and the second motion parameters of the current test period, until the simulation driving scene test is completed, and determine a test result of the test vehicle according to the motion adjustment parameters of each test period.

4. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the test method of the autonomous vehicle according to any one of claims 1-2.

5. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the test method of the autonomous vehicle according to any one of claims 1-2 when executed.

6. A computer program product comprising a computer program which, when executed by a processor, implements the test method of the autonomous vehicle according to any one of claims 1-2.

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