Vehicle intelligent test system and method based on automatic driving

Abstract

The invention provides a vehicle intelligent test system and method based on automatic driving, and the system comprises an execution mechanism which carries out the operation of driving a vehicle to act according to a control instruction of an intelligent driving controller; the vehicle-mounted wireless transceiver module is used for realizing communication between the intelligent driving controller and the remote monitoring module; the vehicle-mounted sensing module is used for monitoring vehicle environment information; the positioning module is used for collecting vehicle positioning information; the intelligent driving controller is used for generating a control instruction to guide the execution mechanism to act; and the remote monitoring module is used for receiving the vehicle test information. Through the automatic driving system with sensing, planning and control functions, the driving robot has multiple working modes such as an imitation instruction mode, a trajectory tracking mode, a preset instruction mode, a cruise driving mode, a data recording mode, a roadside parking mode, an emergency parking mode and a remote control mode, and the application scene of the driving robot is widened.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle testing, and in particular relates to a vehicle intelligent testing system and method based on automatic driving. Background Art

[0002] During the vehicle development phase, a series of tests are required to ensure that the vehicle's functions and performance meet regulations and user requirements. Driving robots have significant advantages in improving test efficiency, improving test objectivity and accuracy, reducing the labor intensity of test personnel, and reducing the risk of injury to test personnel. Therefore, in recent years, driving robots have gradually played an increasingly important role in vehicle testing. However, the current application scenarios of driving robots are usually limited to the following two typical areas: 1) Automobile emission durability tests where the vehicle does not need to move; 2) Partial driving assistance function tests with fixed and simple test conditions (such as AEB tests). However, durability tests in test sites, which take a lot of time, human resources, and vehicle resources, and have fixed conditions, have never widely used driving robots, and their overall application range is relatively narrow.

[0003] For the above problems, the improvements made by those skilled in the art are as follows:

[0004] Patent 1: The Chinese patent application publication number is CN 111618834 A, the application publication date is September 4, 2020, and the invention name is "A steering device for a car driving robot". The invention includes a steering wheel, a steering wheel locking device, a control device, a power device, and an adjustment device. It can ensure that the car will not fail to turn due to factors such as bumps during driving; improve the accuracy and reliability of car tests; adapt to different models and expand the scope of application.

[0005] Patent 2: The Chinese patent application publication number is CN 112557054 A, and the application publication date is March 26, 2021. The invention name is "A pedal driving robot for unmanned driving test". The pedal robot described in the invention includes a fixed seat, a drive assembly, a swing arm and a pedal widening assembly. The drive assembly includes a servo motor, a torque sensor and a reducer. The robot is fixed on the driving seat of the vehicle under test, completes the relevant driving actions required for the experiment, and realizes precise driving control of the vehicle.

[0006] Patent 3: The Chinese patent application publication number is CN 112649209 A, and the application publication date is April 13, 2021. The invention name is "A steering driving robot for unmanned driving test". The steering robot described in the invention consists of a steering wheel fixing assembly, a split ring gear assembly, a drive assembly and a limit fixing assembly. The robot is fixed on the driving seat of the vehicle under test, driving the vehicle to complete the relevant driving actions required for the experiment, and realizing precise driving control of the vehicle.

[0007] Patent 4: The Chinese patent application publication number is CN 114684252 A, the application publication date is July 1, 2022, and the invention name is "A steering actuator for an autonomous driving robot". The steering actuator described in the invention includes a steering wheel connecting ring, a mounting plate, a drive motor, a claw assembly, a support rod, a tension and pressure sensor, and a guide rail connector. The invention can control the steering of the car steering wheel, monitor the steering wheel output torque, and can be quickly installed and disassembled.

[0008] Patent Five: The Chinese utility model patent authorization announcement number is CN 213812889 U, the authorization announcement date is July 27, 2021, and the invention name is "Autonomous driving robot clutch actuator". The autonomous driving robot clutch actuator described in the invention includes a mounting seat, a servo motor, a reducer, a rocker arm, a connecting rod assembly and a clutch pedal adapter.

[0009] Patent Six: The Chinese utility model patent authorization announcement number is CN214197208 U, the authorization announcement date is September 14, 2021, and the invention name is "A gear shift actuator for an autonomous driving robot". The autonomous driving robot gear shift actuator described in the invention includes a first drive device, a first swing arm, a second drive device, a second swing arm and a T-shaped swing frame.

[0010] Patent Seven: The Chinese utility model patent authorization announcement number is CN214084193 U, the authorization announcement date is August 31, 2021, and the invention name is "Autonomous driving robot braking actuator". The autonomous driving robot braking actuator described in the invention includes a mounting seat, a servo motor, a reducer, a rocker arm, a connecting rod assembly and a brake pedal adapter.

[0011] Patent Eight: The Chinese patent authorization announcement number is CN 109491364B, the authorization announcement date is April 1, 2022, and the name of the invention is "A driving robot system and control method for vehicle testing". The invention discloses a driving robot system and control method for vehicle testing. The trajectory tracking unit is used to realize high-precision following control of the preset driving trajectory by the driving robot system. By adjusting the front wheel turning angle and driving speed of the vehicle, the vehicle follows the preset target trajectory, thereby realizing the target instruction of the high-precision trajectory tracking unit being sent to the test vehicle to realize the motion control of the vehicle.

[0012] However, in the above patent documents, Patents 1 to 7 only provide actuators of driving robots with different structures. The driving robot system for vehicle testing proposed in Patent 8 can enable the monitoring center to issue instructions at any time to control the tested vehicle and control the vehicle to follow the preset target trajectory. However, the intelligence level is still not high, the functions realized are relatively single, and it cannot meet the diverse testing needs.

[0013] In general, existing solutions cannot solve the problems of low intelligence and narrow application scope of driving robots.

[0014] Invention patent content

[0015] In view of the above problems, the present invention provides a vehicle intelligent testing system and method based on automatic driving, which improves the intelligence of the driving robot through an automatic driving system with perception, planning and control functions, so that the driving robot has multiple working modes such as imitation command mode, trajectory tracking mode, preset command mode, cruise driving mode, data recording mode, roadside parking mode, emergency parking mode and remote control mode, thus broadening the application scenarios of the driving robot. The purpose of the present invention is achieved through the following technical solutions:

[0016] The present invention first provides a vehicle intelligent testing system based on automatic driving, which includes:

[0017] An actuator, used to execute the operation of driving the vehicle according to the control instruction of the intelligent driving controller, wherein the operation of driving the vehicle includes walking, steering, braking, deceleration, and acceleration;

[0018] On-board wireless transceiver module, used to realize communication between the intelligent driving controller and the remote monitoring module;

[0019] On-board perception sensor module, used to monitor vehicle environmental information;

[0020] Positioning module, used to collect vehicle positioning information;

[0021] Intelligent driving controller, used to receive user instructions and information transmitted by remote monitoring module, vehicle-mounted sensing module, positioning module, and actuator, and generate control instructions to guide the actuator action in combination with the vehicle status information transmitted by the vehicle electronic control unit;

[0022] The remote monitoring module is used to receive vehicle test information, wherein the vehicle test information includes vehicle environment information, positioning information, and vehicle status information.

[0023] Preferably, the actuator includes: a steering robot, an accelerator pedal robot, a brake pedal robot, a clutch pedal robot, a gear shifting robot, etc. Preferably, the steering robot, the accelerator pedal robot, the brake pedal robot, the clutch pedal robot, the gear shifting robot and the intelligent driving controller 2 adopt an independent and redundant communication scheme, that is, they all adopt separate input / output interfaces, and the same signal transmits data through two independent interfaces and lines respectively.

[0024] Furthermore, the remote monitoring module includes a remote server and a driving simulation device, wherein the remote server is used to receive vehicle test information, and the driving simulation device is used to simulate the action of the actuator.

[0025] Furthermore, the test system also includes: an on-board operation module, which is used to receive user instructions and / or test parameter configurations through human-computer interaction, so that testers can control and adjust the test process directly on the test vehicle according to actual conditions on site.

[0026] Furthermore, the vehicle-mounted sensing module includes at least two of a camera, a laser radar, a millimeter-wave radar, and an ultrasonic radar; preferably, the vehicle-mounted sensing module includes a front-view camera, a right-view camera, a left-view camera, a rear-view camera, and a laser radar. Preferably, in order to facilitate adjustment according to test requirements, the above-mentioned various sensors can be fixed on the test vehicle in a simple manner, and the simple manner includes but is not limited to: bonding, suction cup fixing, etc., which can achieve fast installation and removal.

[0027] Furthermore, the positioning module includes a vehicle-mounted positioning submodule and a roadside positioning submodule.

[0028] Furthermore, the intelligent driving controller has:

[0029] The system startup module starts the test system according to the test request sent by the user and jumps to the mode selection module;

[0030] The mode selection module generates a demand receiving form according to the system startup instruction, receives the user mode selection instruction, and jumps to the corresponding instruction module according to the mode selection instruction;

[0031] The imitation command module receives the command from the mode selection module to start the imitation command test;

[0032] The trajectory tracking module receives the instruction from the mode selection module to start the trajectory tracking test;

[0033] The preset command module receives the command of the mode selection module to start the preset command test;

[0034] A data recording module records test data according to a request recording instruction sent by a user or receives a test start signal;

[0035] The safety monitoring module receives vehicle environment information, positioning information, and vehicle status information, and judges the test risk based on this information, and executes preset risk control actions based on the judgment results.

[0036] Furthermore, the intelligent driving controller also has: a cruise driving module, which receives instructions from the mode selection module to start a cruise driving test.

[0037] Furthermore, the intelligent driving controller also has:

[0038] A roadside stop instruction module receives instructions from the safety monitoring module and executes the roadside stop action;

[0039] The emergency stop command module receives the command from the safety monitoring module and executes the emergency stop action;

[0040] and / or,

[0041] The remote control module receives instructions from the remote monitoring module to drive the actuator to operate.

[0042] The present invention further provides a vehicle intelligent testing method based on automatic driving, which comprises:

[0043] The imitation command test method records the sensor data of the staff during manual driving through the data recording module, and sends the manual driving sensor data to the actuator in time series after the manual test is completed, so as to control the vehicle to complete the imitation command test;

[0044] The trajectory tracking test method receives the expected trajectory information of the vehicle input by the user, generates a trajectory tracking instruction and sends it to the execution mechanism to complete the trajectory tracking instruction test, wherein the expected trajectory information includes the vehicle driving path information and the vehicle speed adjustment information;

[0045] The preset instruction test method receives vehicle control instruction information input by a user, generates a preset test instruction and sends it to an actuator to complete the preset instruction test. The vehicle control instruction information includes vehicle driving pedal, brake pedal, and steering wheel angle time series information.

[0046] Preferably, the imitation instruction testing method includes the steps of instruction recording activation, instruction recording, imitation instruction activation, starting point control, and instruction playback; the trajectory tracking testing method includes the steps of trajectory entry, trajectory tracking activation, starting point control, and tracking control; the preset instruction testing method includes the steps of instruction entry, preset instruction activation, starting point control, and instruction execution.

[0047] Furthermore, the vehicle intelligent testing method also includes: a cruise command testing method, receiving vehicle destination information input by a user, generating a cruise command and sending it to an execution mechanism to complete a cruise command test.

[0048] The beneficial effects of the present invention are:

[0049] (1) The present invention adds a vehicle-mounted perception sensor module, so that the driving robot has the ability to perceive obstacles in front of and around the vehicle and road boundaries;

[0050] (2) The present invention adds an automatic driving function based on perception, planning, and control, which can realize unmanned testing;

[0051] (3) The present invention improves the intelligence level of the driving robot, enabling the driving robot to have multiple working modes such as imitation command mode, trajectory tracking mode, preset command mode, cruise driving mode, data recording mode, roadside parking mode, emergency parking mode and remote control mode, thereby broadening the application scenarios of the driving robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The figure shows a schematic diagram of the vehicle intelligent testing system architecture of the present invention;

[0053] Figure 2 FIG. 1 is a schematic diagram of a vehicle intelligent testing system according to Embodiment 1 of the present invention;

[0054] Figure 3 It is a schematic diagram of the internal layout of a vehicle equipped with an intelligent testing system according to Embodiment 2 of the present invention;

[0055] Figure 4 The figure shows a schematic diagram of the external layout of a vehicle body equipped with an intelligent testing system according to Embodiment 2 of the present invention;

[0056] Figure 5 Shown is a schematic diagram of the method flow of Example 3 of the present invention. DETAILED DESCRIPTION

[0057] The technical scheme of the preferred embodiment of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Embodiment 1:

[0059] This embodiment provides a vehicle intelligent testing system with an automatic driving function, such as Figure 1 and Figure 2 As shown, the test system has an actuator 1, an intelligent driving controller 2, a vehicle-mounted operation module 3, a vehicle-mounted perception sensor module 4, a positioning module 5, a vehicle-mounted wireless transceiver module 6, a remote monitoring module 7 and a power supply module 8. The composition and functions of each mechanism are as follows:

[0060] The actuator 1 is used to execute the operation of driving the vehicle according to the control instruction of the intelligent driving controller, and the operation of driving the vehicle includes walking, steering, braking, deceleration, and acceleration. In a specific embodiment, the actuator 1 adopts an actuator commonly used in the field of driving robots, including: a steering robot, an accelerator pedal robot, a brake pedal robot, a clutch pedal robot, and a shift robot. The input / output port of the actuator 1 is a CAN (Controller Area Network) interface or an RS485 interface, the input signal is the control signal of the actuator, and the output signal is the state signal of the actuator. Taking the accelerator pedal as an example, the input signal is the expected percentage of the accelerator pedal travel; the output signal is the actual percentage of the accelerator pedal travel; preferably, the steering robot, the accelerator pedal robot, the brake pedal robot, the clutch pedal robot, the shift machine and the intelligent driving controller 2 adopt an independent and redundant communication scheme, that is, each adopts a separate input / output interface, and the same signal transmits data through two independent interfaces and lines respectively.

[0061] The intelligent driving controller 2 is used to receive user instructions and information transmitted by the remote monitoring module, the vehicle-mounted sensing module, the positioning module, and the actuator, and generates control instructions to guide the actuator action in combination with the vehicle state information transmitted by the vehicle electronic control unit. In a preferred embodiment, the intelligent driving controller 2 includes a SoC (System on Chip, called a system-level chip) and has the ability to run the perception algorithm; b) the intelligent driving controller 2 includes an actuator drive interface, a vehicle-mounted operation module interface, a vehicle-mounted wireless transceiver module interface, a positioning module interface, and a vehicle-mounted perception sensor module interface. The actuator drive interface is a CAN interface or an RS485 interface; the vehicle-mounted operation module interface includes a tablet computer interface and a key interface, wherein the tablet computer interface is an Ethernet interface; the key interface is a GPIO (General-purpose input / output) interface; the vehicle-end wireless transceiver module interface is an Ethernet interface; the positioning module interface is a CAN interface; the vehicle-mounted perception sensor module interface is a common sensor interface in the field of intelligent driving, which varies according to the selection of sensors, including but not limited to an LVDS (Low Voltage Differential Signaling) interface for camera data transmission, a CAN interface for millimeter wave radar and ultrasonic radar data transmission, an Ethernet interface for laser radar data transmission, etc.; the intelligent driving controller 2 contains a vehicle automatic driving test method software program; the vehicle automatic driving test method software program has the following functional modules: mode selection, imitation command mode, trajectory tracking mode, preset command mode, cruise driving mode, data recording setting, data recording mode, safety monitoring, roadside parking mode, emergency parking mode and remote control mode.

[0062] The vehicle-mounted operation module 3 is used to receive user instructions and / or test parameter configurations through human-computer interaction. In a preferred embodiment, the vehicle-mounted operation module 3 includes a vehicle-mounted operation tablet computer 301 and a vehicle-mounted operation button 302. The vehicle-mounted operation button 302 includes a test start / stop button, an emergency stop button, and a data recording start / end button; the test start / stop button can be used to activate functions of different control modes; the vehicle-mounted operation tablet computer 301 has an Ethernet interface, which is mainly used by test personnel to configure the test on the vehicle.

[0063] The vehicle-mounted perception sensor module 4 is used to monitor vehicle environmental information. Preferably, the vehicle-mounted perception sensor module 4 includes a sensor / sensor combination commonly used for perception in the field of intelligent driving, and the sensor includes, but is not limited to, a camera, a laser radar, a millimeter-wave radar, an ultrasonic radar, etc.; the vehicle-mounted perception sensor module 4 is characterized in that the sensor can be fixed on the test vehicle in a simple manner, and the simple manner includes, but is not limited to: bonding, suction cup fixing, etc., and the preferred manner is a fixing manner that can achieve rapid installation and removal.

[0064] The positioning module 5 is used to collect vehicle positioning information; the positioning module 5 is a common positioning solution in the field of intelligent driving. In the preferred solution, the positioning module 5 includes two parts: a vehicle-mounted positioning submodule 501 and a road-side positioning submodule 502. The external interface of the positioning module is a CAN interface, and the vehicle-mounted positioning submodule 501 and the road-side positioning submodule 502 communicate with each other through a wireless network.

[0065] The vehicle-mounted wireless transceiver module 6 is used to realize the communication between the intelligent driving controller and the remote monitoring module. Preferably, the vehicle-mounted wireless transceiver module 6 is a 5G wireless transceiver module commonly used in the communication field.

[0066] The remote monitoring module 7 is used to receive vehicle test information, and the vehicle test information includes vehicle environment information, positioning information, and self-vehicle status information. In a preferred embodiment, the remote monitoring module 7 is composed of a remote server 701 and a driving simulation device 702. The remote server 701 is a server with multiple displays and keyboards commonly used in the computer field. The remote server 701 contains a vehicle automatic driving remote monitoring software program. The vehicle automatic driving remote monitoring software program can display environmental monitoring, self-vehicle monitoring and system monitoring information on the display of the remote server 701, and send the control instructions of the driving simulation device 702 to the on-board wireless transceiver module 6 through a 5G wireless signal to achieve remote control of the vehicle. The environmental monitoring refers to the intelligent driving robot controller 2 obtaining information about the surroundings of the vehicle through the on-board perception sensor module 4 and discriminating the threats of the surrounding information to the vehicle. The vehicle surrounding information includes road boundaries, drivable areas, and obstacles. The self-vehicle monitoring refers to the self-vehicle status obtained by the intelligent driving robot controller 2 through the vehicle CAN line. The vehicle status includes fault information of the vehicle engine / drive motor status, EPS (Electric Power Steering), ESP (Electronic Stability Program), ABS (antilock brake system), etc. The system monitoring refers to the intelligent driving robot controller 2 obtaining the status of the actuator 1 through the CAN line. The actuator 1 status includes fault information of the steering robot, accelerator pedal robot, brake pedal robot, clutch pedal robot, shift robot, etc. The remote server 701 and the driving simulation device 702 are connected via RS485. The driving simulation device 702 is a simulator commonly used in the automotive field and is equipped with a steering wheel, accelerator, brake pedal, and shift lever; it is characterized in that the driving simulation device 702 also includes: a test start / stop button, an emergency stop button, and a data recording start / end button;

[0067] The power module 8 is a common power module in the field of electronics and electrical appliances. It outputs different voltages and supplies power to the actuator 1, the intelligent driving robot controller 2, the vehicle-mounted operation module 3, the vehicle-mounted sensing sensor module 4, the vehicle-mounted positioning submodule 501, and the vehicle-mounted wireless transceiver module 6 respectively.

[0068] The connection relationship between the above components is as follows: the CAN interface of the actuator 1 is connected to the CAN interface of the intelligent driving robot controller 2 through a twisted pair of wires; the Ethernet interface of the vehicle-mounted operation tablet computer 301 is connected to the Ethernet interface of the intelligent driving robot controller 2 through a network cable; the vehicle-mounted operation button 302 is connected to the GPIO interface of the intelligent driving robot controller 2 through a wire; the vehicle-mounted perception sensor module 4 uses different interfaces to connect to the vehicle-mounted perception sensor interface of the intelligent driving robot controller 2 according to the different sensors selected. The CAN interface of the positioning module 5 is connected to the CAN interface of the intelligent driving robot controller 2 through a twisted pair of wires; the Ethernet interface of the vehicle-mounted wireless transceiver module 6 is connected to the Ethernet interface of the intelligent driving robot controller 2 through a network cable; the remote monitoring module 7 is connected to the vehicle-mounted wireless transceiver module 6 through a 5G wireless signal.

[0069] Embodiment 2:

[0070] This embodiment 1 provides a test vehicle capable of carrying the system of embodiment 1, and its specific structure is as follows: Figure 3 and Figure 4 As shown, the vehicle body is provided with a front bumper 16, a right rearview mirror 17, a left rearview mirror 18, a front windshield 19, a roof 20 and a rear windshield 21 fixed thereto according to conventional vehicle settings. An actuator 1, an intelligent driving controller 2, a vehicle-mounted operation module 3, a power module 8, a steering wheel 9, a brake pedal 10, an accelerator pedal 11, a main driving floor 12, a central control armrest 13, a co-pilot interior panel 14, and a co-pilot floor 15 are arranged in the cockpit, and a vehicle-mounted perception sensor module 4, a positioning module 5, and a vehicle-mounted wireless transceiver module 6 are arranged on the vehicle body.

[0071] The actuator 1 comprises:

[0072] A steering actuator for implementing a steering operation, the steering actuator comprising: a steering robot motion mechanical structure 111, a steering robot motor 112 and a steering robot fixed mechanical structure 113;

[0073] A brake actuator for implementing a brake operation, the brake actuator comprising a pedal robot motion mechanical structure 121, a brake pedal robot motor 122 and a brake pedal robot fixed mechanical structure 123;

[0074] The speed-changing actuator is used to realize acceleration and deceleration operations, and the speed-changing actuator includes an accelerator pedal robot motion mechanical structure 131 , an accelerator pedal robot motor 132 and an accelerator pedal robot fixed mechanical structure 133 .

[0075] The vehicle-mounted operation module 3 includes a vehicle-mounted operation tablet computer 301 and a vehicle-mounted operation button 302;

[0076] The vehicle-mounted perception sensor module 4 includes a front-view camera 401, a right-view camera 402, a left-view camera 403, a rear-view camera 404 and a laser radar 405; wherein the front-view camera 401 is fixed on the front windshield 19; the right-view camera 402 is fixed on the right rearview mirror 17; the left-view camera 403 is fixed on the left rearview mirror 18; the rear-view camera 404 is fixed on the rear windshield 21; the laser radar 405 is fixed on the front bumper 16; and the fixing method is bonding;

[0077] The positioning module 5 includes a vehicle-mounted positioning submodule 501 and a road measurement positioning submodule 502 .

[0078] Embodiment 3:

[0079] This embodiment provides an intelligent testing method based on the system and vehicle of Embodiment 1 and Embodiment 2, such as Figure 5 As shown, the method includes the following contents:

[0080] Step S100-mode selection: used to set the working mode of the vehicle automatic driving test system, mainly divided into mode setting and jump to each mode. The mode setting can be remotely set through the remote monitoring module 7, or it can be controlled in the vehicle through the vehicle operation module 3. Jump to each mode includes jumping to the following modes: imitation command mode, trajectory tracking mode, preset command mode, and cruise driving mode.

[0081] Step S200-Imitation instruction test method: The data recording module records the sensor data of the staff during manual driving, and after the manual test is completed, the sensor data of manual driving is sent to the actuator in a time series to control the vehicle to complete the imitation instruction test. The specific implementation process of this process is as follows:

[0082] Step S201-Command record activation:

[0083] The test personnel starts recording the command by clicking the test start / stop button in the remote monitoring module 7 or the vehicle operation module 3 .

[0084] Step S202-command record:

[0085] The test personnel drive the vehicle in a specific place according to the specific requirements of the test. The intelligent driving robot controller 2 reads the sensor data of the actuator 1 through the CAN interface, and reads the vehicle starting position / orientation data of the positioning module 5 through the CAN interface, and stores them. The sensor data includes the steering wheel angle of the steering robot, the accelerator pedal opening of the accelerator pedal robot, the brake pedal travel of the brake pedal robot, the clutch pedal travel of the clutch pedal robot, the gear position of the gear shift robot, etc.

[0086] Step S203-Imitation instruction activation:

[0087] The test personnel starts the simulation of the command by clicking the test start / stop button in the remote monitoring module 7 or the vehicle operation module 3 .

[0088] Step S204-Starting point control:

[0089] The intelligent driving robot controller 2 uses a parking algorithm commonly used in the field of intelligent driving to control and calculate the steering wheel angle, accelerator pedal travel, and brake pedal travel instructions of the vehicle when it stops at the starting point. Then, the above instructions are executed by the actuator 1, so that the vehicle automatically drives to the starting position, and the vehicle direction is consistent with the starting position. The parking algorithm includes three parts: perception, planning, and control. The perception algorithm is a neural network-based algorithm commonly used in the field of intelligent driving, which is characterized by only identifying road boundaries, drivable areas, and obstacles. The planning algorithm is a planning algorithm based on a combination of curves and straight lines or a hybrid A* algorithm commonly used in the field of intelligent driving. The control algorithm is a PID-based algorithm.

[0090] Step S205-instruction playback:

[0091] After the vehicle stops at the starting position in the correct direction, the intelligent driving robot controller 2 controls the vehicle brake pedal through the actuator 1 to control the vehicle to stop at the starting point for a preset time T1; after the preset time T1, the intelligent driving robot controller 2 prompts the test personnel through the display of the remote server 701 or the vehicle-mounted operation tablet computer 301 that the test is about to begin. The intelligent driving robot controller 2 sends the actuator sensor data recorded in the instruction record to the actuator in a time series to control the vehicle to travel.

[0092] Step S300-trajectory tracking test method is used to track the preset trajectory input by the test personnel. It generates a trajectory tracking instruction by receiving the expected trajectory information of the vehicle input by the user and sends it to the execution mechanism to complete the trajectory tracking instruction test. The expected trajectory information includes the vehicle driving path information and the vehicle speed adjustment information. In a specific embodiment, the specific implementation process of this method is as follows:

[0093] Step S301-track entry:

[0094] The test personnel input the desired vehicle running trajectory information by operating the remote server 701 or the vehicle-mounted tablet computer 301. The trajectory information includes the vehicle's driving path and the vehicle speed when driving along the path.

[0095] Step S302 - Activate track tracking:

[0096] The tester starts the trajectory tracking by clicking the test start / stop button in the remote monitoring module 7 or the vehicle operation module 3 .

[0097] Step S303-Starting point control:

[0098] The intelligent driving robot controller 2 uses a parking algorithm commonly used in the field of intelligent driving to control and calculate the steering wheel angle, accelerator pedal travel, and brake pedal travel instructions of the vehicle when it stops at the starting point. Then, the above instructions are executed by the actuator 1, so that the vehicle automatically drives to the starting position, and the vehicle is in the same direction. The parking algorithm includes three parts: perception, planning, and control. The perception algorithm is a neural network-based algorithm commonly used in the field of intelligent driving, which is characterized by only identifying road boundaries, drivable areas, and obstacles. The planning algorithm is a curve-based, previously combined planning algorithm or a hybrid A* algorithm commonly used in the field of intelligent driving. The control algorithm is an algorithm based on PID (Proportional Integral Derivative).

[0099] Step S304-Tracking control:

[0100] After the vehicle stops at the starting position in the correct direction, the intelligent driving robot controller 2 manipulates the vehicle brake pedal through the actuator 1 to control the vehicle to stop at the starting point for a preset time T1; after the preset time T1, the intelligent driving robot controller 2 prompts the test personnel through the remote monitoring module 7 or the display screen of the vehicle-mounted operation module 3 that the test is about to begin. The intelligent driving robot controller 2 uses the control algorithm commonly used in the field of intelligent driving to control and calculate the steering wheel angle, accelerator pedal travel, and brake pedal travel instructions of the vehicle following the recorded trajectory, and then executes the above instructions through the actuator 1 to make the vehicle follow the trajectory. The control algorithm includes but is not limited to PID algorithm, LQR (linear quadratic regulator) algorithm, and MPC (Model Predictive Control) algorithm.

[0101] S400-preset instruction test method, used to execute preset instructions, by receiving vehicle control instruction information input by the user, generating preset test instructions and sending them to the execution mechanism to complete the preset instruction test, wherein the vehicle control instruction information includes vehicle driving pedal, brake pedal, and steering wheel angle time series information. In a specific implementation, the specific implementation process of the method is as follows:

[0102] Step S401-instruction input:

[0103] The test personnel input the vehicle starting position / orientation data and the time series information of the driving pedal, brake pedal, and steering wheel angle by operating the remote server 701 or the vehicle-mounted tablet computer 301 according to the specific requirements of the test.

[0104] Step S402-preset command activation:

[0105] The test personnel starts the execution of the command by clicking the test start / stop button in the remote monitoring module 7 or the vehicle operation module 3 .

[0106] Step S403-Starting point control:

[0107] The intelligent driving robot controller 2 uses a parking algorithm commonly used in the field of intelligent driving to control and calculate the steering wheel angle, accelerator pedal travel, and brake pedal travel instructions of the vehicle when it stops at the starting point. Then, the actuator 1 executes the above instructions to make the vehicle automatically drive to the starting position, and the vehicle is in the same direction. The parking algorithm includes three parts: perception, planning, and control. The perception algorithm is a neural network-based algorithm commonly used in the field of intelligent driving, which is characterized in that it only recognizes the drivable area. The planning algorithm is a curve-based, previously combined planning algorithm or a hybrid A* algorithm commonly used in the field of intelligent driving. The control algorithm is a PID-based algorithm.

[0108] Step S404-instruction execution:

[0109] After the vehicle stops at the starting position in the correct direction, the intelligent driving robot controller 2 controls the vehicle brake pedal through the actuator 1 to control the vehicle to stop at the starting point for a preset time T1; after the preset time T1, the intelligent driving robot controller 2 prompts the test personnel through the display of the remote server 701 or the vehicle-mounted operation tablet computer 301 that the test is about to begin. The intelligent driving robot controller 2 sends the time series information of the driving pedal, brake pedal, and steering wheel angle entered in the command entry to the actuator in a time series to control the vehicle to travel.

[0110] S500-Cruise command test method, the cruise driving mode is used for automatic driving control between the vehicle preparation area and the test site, and between different test sites. It generates a cruise command by receiving the vehicle destination information input by the user and sends it to the actuator to complete the cruise command test. In a specific implementation, the specific implementation process of this method is as follows:

[0111] Step S501-end point entry:

[0112] The tester enters the desired test starting point information as the end point of the cruise driving by operating the remote server 701 or the vehicle-mounted tablet computer 301. The desired starting point information includes the starting point position and the vehicle direction information.

[0113] Step S502-routing and speed planning:

[0114] The intelligent driving robot controller 2 uses a route planning algorithm commonly used in the field of intelligent driving to calculate an alternative route for the vehicle to travel from the current position to the desired test starting point. The route planning algorithm includes but is not limited to the A* algorithm and the Dijstra algorithm.

[0115] The intelligent driving robot controller 2 calculates the optional upper speed limit Vlimit based on the road curvature information contained in the optional route and the speed limit information of the road itself. The upper speed limit Vlimit is the smaller of the speed limit information of the road itself and the speed limit determined by the road curvature. The calculation method of the speed limit determined by the road curvature is as follows:

[0116] VLimitρ=√aLatMax / ρ

[0117] Wherein, VLimitρ is the speed limit determined by the road curvature; aLatMax is the preset maximum allowed lateral acceleration of the vehicle; ρ is the road curvature;

[0118] Step S503-routing and speed setting:

[0119] The test personnel selects a desired route from the alternative routes by operating the remote server 701 or the vehicle-mounted operating tablet computer 301;

[0120] The tester sets the expected speed of the vehicle when traveling along the expected route. The set expected speed cannot exceed the optional speed upper limit Vlimit in the route and speed planning.

[0121] Step S504-cruise driving activation:

[0122] The tester turns on the cruise control by clicking the test start / stop button in the remote monitoring module 7 or the vehicle operation module 3 .

[0123] Step S505-cruise control:

[0124] The intelligent driving robot controller 2 uses a driving algorithm commonly used in the field of intelligent driving to control and calculate the steering wheel angle, accelerator pedal travel, and brake pedal travel instructions of the vehicle when it travels to the desired test starting point. Then, the above instructions are executed by the actuator 1, so that the vehicle automatically drives to the test starting point, and the vehicle is in the same direction. The driving algorithm includes three parts: perception, planning, and control. The perception algorithm is a neural network-based algorithm commonly used in the field of intelligent driving, characterized in that it only recognizes the drivable area, lane line, and road boundary. The planning algorithm is an algorithm based on the centerline of the road commonly used in the field of intelligent driving, including but not limited to the Werling algorithm and the Lattice algorithm. The control algorithm includes but is not limited to the PID algorithm, the LQR (linear quadratic regulator) algorithm, and the MPC (Model Predictive Control) algorithm.

[0125] Step S600: Data recording settings

[0126] The data recording setting is used to set whether to start data recording, the content of recording, the cycle, etc. The recorded content includes one or more information selected by the tester, such as vehicle information, actuator information, and sensor information.

[0127] The data recording settings can be remotely set through the remote server 701, or can be set in the vehicle through the vehicle-mounted operating tablet computer 301.

[0128] Step S700: Data recording mode

[0129] The data recording mode is used to record the content data that needs to be recorded in 6. Data recording settings. The optional content data includes but is not limited to: actuator working status and sensor signals, vehicle operation module command signals, positioning information, perception sensor raw data, perception algorithm output signals, planning algorithm output signals, and control algorithm output signals.

[0130] The intelligent driving robot controller 2 reads and records the content data that needs to be recorded in the data recording setting 6 through the actuator drive interface, the vehicle-mounted operation module interface, the vehicle-end wireless transceiver module interface, the positioning module interface, and the vehicle-mounted perception sensor module interface.

[0131] Step S800: Security monitoring

[0132] Safety monitoring is used to monitor the test vehicle's surrounding environment, the vehicle's own status, and the vehicle's autonomous driving test system's own status, and switch the vehicle's autonomous driving system's operating mode when the vehicle is at risk based on the vehicle's surrounding environment, the vehicle's own status, and the vehicle's own autonomous driving test system's own status.

[0133] Step S801-Environmental monitoring:

[0134] The environmental monitoring refers to the intelligent driving robot controller 2 obtaining information about the vehicle's surroundings through the vehicle-mounted perception sensor module 4 and identifying threats to the vehicle from the surrounding information.

[0135] The vehicle surrounding information includes road boundaries, drivable areas, and obstacles.

[0136] Step S802-Self-vehicle monitoring:

[0137] The intelligent driving robot controller 2 obtains the vehicle status through the vehicle CAN line. The vehicle status includes the vehicle engine / drive motor status, EPS (Electric Power Steering, electric power steering system), ESP (Electronic Stability Program, electronic stability program), ABS (antilockbrake system, anti-lock braking system) and other fault information.

[0138] Step S803-System Monitoring:

[0139] The intelligent driving robot controller 2 obtains the state of the actuator 1 through the CAN line. The state of the actuator 1 includes fault information of the steering robot, the accelerator pedal robot, the brake pedal robot, the clutch pedal robot, the gear shift robot, etc.

[0140] When the vehicle monitoring detects a vehicle fault, it will switch to the roadside parking mode;

[0141] When the environmental monitoring detects that the vehicle exceeds the road boundary / drivable area, is too close to the obstacle ahead, or the environmental detection fails, it jumps to the emergency stop mode;

[0142] When the system monitoring detects a system failure, it jumps to the emergency stop mode;

[0143] When the tester sends a remote control request in the remote monitoring module 7, it jumps to the remote control mode;

[0144] Step S900 - Roadside Stop Mode

[0145] The intelligent driving robot controller 2 uses a driving algorithm commonly used in the field of intelligent driving to control and calculate the steering wheel angle, accelerator pedal travel, and brake pedal travel instructions for the vehicle to park at a roadside parking spot. Then, the actuator 1 executes the above instructions to make the vehicle automatically drive to the roadside parking spot. The parking algorithm includes three parts: perception, planning, and control. The perception algorithm is a neural network-based algorithm commonly used in the field of intelligent driving, which is characterized by only identifying the drivable area. The planning algorithm is a spline-based algorithm commonly used in the field of intelligent driving. The control algorithm is a PID-based algorithm.

[0146] Step SA00 - Emergency Stop Mode:

[0147] The intelligent driving robot controller 2 adopts the driving algorithm commonly used in the field of intelligent driving to control and calculate the brake pedal travel instructions required for emergency stopping of the vehicle, and then executes the above instructions through the actuator 1 to automatically stop the vehicle. The driving algorithm includes three parts: perception, planning and control. The perception algorithm is a neural network-based algorithm commonly used in the field of intelligent driving, which is characterized by only identifying drivable areas and obstacles. The planning algorithm is an algorithm based on the centerline of the road commonly used in the field of intelligent driving, including but not limited to the Werling algorithm and the Lattice algorithm. The control algorithm includes but is not limited to the PID algorithm, the LQR (linear quadratic regulator) algorithm, and the MPC (Model Predictive Control) algorithm;

[0148] Step SF00 - Remote Control Mode:

[0149] The intelligent driving robot controller 2 sends the real-time control information of the driving simulation device 702, such as the driving pedal, brake pedal, steering wheel angle, etc., to the actuator to control the vehicle driving.

[0150] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention. The dimensions described in the drawings and embodiments are not related to the specific objects and are not used to limit the protection scope of the present invention. The dimensions of the objects can be selected and changed according to actual needs.

Claims

1. A vehicle intelligent testing system based on autonomous driving, comprising: An actuator, used to execute the operation of driving the vehicle according to the control instruction of the intelligent driving controller, wherein the operation of driving the vehicle includes walking, steering, braking, deceleration, and acceleration; On-board wireless transceiver module, used to realize communication between the intelligent driving controller and the remote monitoring module; On-board perception sensor module, used to monitor vehicle environmental information; Positioning module, used to collect vehicle positioning information; Intelligent driving controller, used to receive user instructions and information transmitted by remote monitoring module, vehicle-mounted sensing module, positioning module, and actuator, and generate control instructions to guide the actuator action in combination with the vehicle status information transmitted by the vehicle electronic control unit; The remote monitoring module is used to receive vehicle test information, wherein the vehicle test information includes vehicle environment information, positioning information, and vehicle status information.

2. The vehicle intelligent testing system based on automatic driving according to claim 1 is characterized in that: The remote monitoring module includes a remote server and a driving simulation device, wherein the remote server is used to receive vehicle test information, and the driving simulation device is used to simulate the action of the actuator.

3. A vehicle intelligent testing system based on automatic driving according to claim 1 or 2, characterized in that: It also includes: an on-vehicle operation module, which is used to receive user instructions and / or test parameter configurations through human-computer interaction.

4. A vehicle intelligent testing system based on automatic driving according to any one of claims 1 to 3, characterized in that: The vehicle-mounted perception sensor module includes at least two of a camera, a laser radar, a millimeter-wave radar, and an ultrasonic radar; preferably, the vehicle-mounted perception sensor module includes a front-view camera, a right-view camera, a left-view camera, a rear-view camera, and a laser radar.

5. A vehicle intelligent testing system based on automatic driving according to any one of claims 1 to 4, characterized in that: The positioning module includes a vehicle-mounted positioning submodule and a roadside positioning submodule.

6. A vehicle intelligent testing system based on automatic driving according to any one of claims 1 to 5, characterized in that: The intelligent driving controller has: The mode selection module generates a demand receiving form according to the system startup instruction, receives the user mode selection instruction, and jumps to the corresponding instruction module according to the mode selection instruction; The imitation command module receives the command from the mode selection module to start the imitation command test; The trajectory tracking module receives the instruction from the mode selection module to start the trajectory tracking test; The preset command module receives the command of the mode selection module to start the preset command test; A data recording module records test data according to a request recording instruction sent by a user or receives a test start signal; The safety monitoring module receives vehicle environment information, positioning information, and vehicle status information, and judges the test risk based on this information, and executes preset risk control actions based on the judgment results.

7. The vehicle intelligent testing system based on automatic driving according to claim 4, characterized in that: The intelligent driving controller also has: The cruise driving module receives the instruction from the mode selection module to start the cruise driving test.

8. The vehicle intelligent testing system based on automatic driving according to claim 1, characterized in that: The intelligent driving controller also has: A roadside stop instruction module receives instructions from the safety monitoring module and executes the roadside stop action; The emergency stop command module receives the command from the safety monitoring module and executes the emergency stop action; and / or, The remote control module receives instructions from the remote monitoring module to drive the actuator to operate.

9. A vehicle intelligent testing method based on autonomous driving, comprising: The imitation command test method records the sensor data of the staff during manual driving through the data recording module, and sends the manual driving sensor data to the actuator in time series after the manual test is completed, so as to control the vehicle to complete the imitation command test; The trajectory tracking test method receives the expected trajectory information of the vehicle input by the user, generates a trajectory tracking instruction and sends it to the execution mechanism to complete the trajectory tracking instruction test, wherein the expected trajectory information includes the vehicle driving path information and the vehicle speed adjustment information; The preset instruction test method receives vehicle control instruction information input by a user, generates a preset test instruction and sends it to an actuator to complete the preset instruction test. The vehicle control instruction information includes vehicle driving pedal, brake pedal, and steering wheel angle time series information.

10. A vehicle intelligent testing method based on automatic driving according to claim 9, characterized in that: Also includes: The cruise command test method receives vehicle destination information input by a user, generates a cruise command and sends it to an actuator to complete the cruise command test.

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