CN114047742A - Intelligent piloting advanced driver assistance hardware in-loop test system and method - Google Patents
Intelligent piloting advanced driver assistance hardware in-loop test system and method Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent navigation, in particular to an intelligent navigation advanced auxiliary driving hardware in-loop test system and a method, which comprises a power supply management module, a workstation, a real-time simulator, a video injection device, a fault injection device, a signal conditioning device, a vehicle machine device, a program-controlled power supply and an intelligent navigation domain controller; according to the invention, a real intelligent navigation domain controller is adopted, and the required sensor signals are constructed through scene software and are injected into the domain controller, so that the software algorithm and hardware defects can be found more timely and comprehensively, the development period is shortened, and the risk cost caused by a large amount of real vehicle tests can be saved; the real vehicle data is analyzed to generate an Opendrive format, and scene simulation software is imported, so that the problem of matching between a simulation map and an actual algorithm map loading module is solved; the vehicle machine receives the positioning information simulated by the virtual scene, and outputs and feeds back the navigation information to the controller, so that the verification of the high-precision positioning fusion algorithm is realized.
Description
Technical Field
The invention relates to the technical field of intelligent navigation, in particular to an intelligent navigation advanced driver assistance hardware in-loop test system and method.
Background
The intelligent piloting advanced assistant driving system supports vehicles to plan according to a navigation path on the basis of functions of cruise speed control, vehicle distance keeping, steering assistant, steering lamp control lane changing and the like, realizes automatic ramp entering and exiting and main lane switching on most of highway sections, urban elevated roads and other road sections in a high-precision map coverage area, and can automatically adjust the speed, intelligently change lanes and overtake slow vehicles according to information such as road speed limit, environmental perception and the like, thereby realizing automatic assistant driving according to a navigation path under an appointed path.
Therefore, compared with ordinary low-grade automatic driving, the intelligent piloting high-grade auxiliary driving system comprises a more complex technology and is the deep fusion of a navigation system, a high-precision map and the automatic auxiliary driving system. The advanced assistant driving development brings great convenience to people, and meanwhile, the safety of the advanced assistant driving development is also important to attention of customers and developers.
Complex decision and control algorithms must ensure that the vehicle reacts properly to the nearly endless number of events that may occur in a real traffic environment while reducing the driving burden. Relying on conventional testing methods can be quite time consuming and expensive.
Therefore, simulation technology is favored in recent years, and simulation is a test method with reproducible scene and low cost risk. However, for such advanced driving assistance systems in the industry at present, due to the limitation that the simulation environment is difficult to be consistent with the real road environment and the like, the test verification mainly adopts a way of directly performing road test, and the problems of low efficiency, high safety risk and the like exist.
In order to solve the problems, the application provides an intelligent piloting advanced assistant driving hardware in-loop test system and method.
Disclosure of Invention
Objects of the invention
In order to solve the technical problems in the background art, the invention provides an intelligent piloting advanced auxiliary driving hardware-in-the-loop test system and method, and solves the problems of high real vehicle cost, difficult scene construction and reproduction, navigation information simulation and system level verification. Wherein, the real vehicle is with high costs the problem: there is a need for more abundant sensor modules, such as camera modules, radar modules, high-precision maps and positioning modules. Therefore, the environmental construction cost of the whole system is high, the actual road environmental test of the high-grade navigation assistant driving is difficult, and more manpower and material resources are needed to realize richer test case verification; the problem of difficulty in scene construction and reproduction is as follows: the number of scenes is verified, the irreproducibility of dangerous scenes is verified, and the testing efficiency is low. Due to the complexity of the road environment, the difficulty of simulation building of some complicated road conditions or simulation reproduction of special traffic scenes is high by software, and even if rough simulation fitting can be performed by software, the finally obtained experimental result has no persuasiveness and credibility; navigation information simulation problem: the real-vehicle NOP function needs to perform user interaction and output real-time navigation information, but software does not have a vehicle in loop simulation, so that part of functions of the vehicle need to be simulated; system level authentication problem: for the simulation of model-in-loop or software-in-loop, the function verification of the whole system is difficult to realize for the perception algorithm, the barrier fusion algorithm, the high-precision map positioning fusion algorithm and the navigation auxiliary algorithm of the whole system.
(II) technical scheme
In order to solve the above problems, the present invention provides an intelligent piloting advanced driver assistance hardware-in-the-loop test system, which includes a power management module for controlling, distributing and protecting the power of the system test equipment; a programme-controlled power for being directed at intelligent navigation territory controller power supply still includes:
the workstation is provided with upper computer software and scene simulation software and outputs video stream signals;
the video injection equipment is used for converting the video stream signal output by the workstation into a vehicle-mounted GMSL video stream signal and outputting a video image matched with the interface of the intelligent navigation domain controller so as to realize video injection for the intelligent navigation domain controller;
the real-time simulator is provided with a high-performance processor, an I/O signal board card and a CAN communication board card; the communication between the chassis signal of the vehicle body and the radar signal is established with the intelligent navigation domain controller through the CAN communication board card, the high-performance processor runs a real-time operating system, is provided with lower computer software and runs a vehicle dynamics model, and establishes communication with a workstation through the Ethernet;
the vehicle-mounted equipment is integrated with a navigation map and receives a GPS positioning signal through the CAN communication board card;
the fault injection equipment is used for injecting simulation faults into the intelligent navigation domain controller;
the signal conditioning equipment is used for realizing electrical characteristic matching between the I/O signal board card and the intelligent navigation domain controller and realizing communication between digital signals and analog signals between the real-time simulator and the intelligent navigation domain controller;
the intelligent navigation domain controller is provided with a first chip, a second chip and a third chip, the third chip acquires relative positions, high-precision map information and navigation information of a vehicle on the basis of high-precision map data, integrates a GPS positioning signal, a sensing signal output by the second chip and a navigation signal output by the vehicle equipment to form a signal set, the first chip receives the signal set signal and outputs a vehicle control signal, the CAN communication board card transmits the vehicle control signal into the high-performance processor to control a vehicle dynamics model, and the navigation signal output by the vehicle equipment is transmitted into the intelligent navigation domain controller through an Ethernet.
Preferably, when the workstation runs the scene simulation software, the workstation outputs a video stream signal through a DP interface of the workstation, and the video injection device receives the video stream signal through a mini DP interface and synchronously performs format conversion and protocol conversion on the received video stream signal through a processor therein.
Preferably, the first chip can be used for realizing a control planning algorithm of intelligent navigation, the second chip can be used for realizing a deep learning and camera perception algorithm, and the third chip can be used for realizing a high-precision map positioning fusion algorithm and a diagnosis function safety algorithm.
Preferably, a memory space is arranged in the third chip and used for storing high-precision map data.
Preferably, the signal conditioning equipment is provided with an overcurrent and overvoltage protection circuit.
The invention provides an intelligent piloting advanced assistant driving hardware in-loop test method, which is based on the intelligent piloting advanced assistant driving hardware in-loop test system and comprises the following steps:
step S01, building a test scene;
step S02, connecting the intelligent piloting advanced assistant driving hardware to the circuit between the devices of the ring test system, and then electrifying the devices;
step S03, simulating a required sensor signal;
step S04, injecting the simulated signal in the step S03 into the intelligent navigation domain controller;
step S05, setting a navigation destination on the vehicle equipment;
step S06, configuring a simulated intelligent piloting advanced auxiliary driving main switch in upper computer software, simulating a function switch of a real vehicle body, injecting a signal of the simulated switch into an intelligent piloting domain controller through CAN communication, and then operating an intelligent piloting advanced auxiliary driving hardware-in-the-loop test system, wherein the steps of operating the upper computer software of a workstation, operating scene simulation software and starting the simulated intelligent piloting advanced auxiliary driving main switch are included;
step S07, recording the result of the test data;
step S08, when the vehicle in the simulation environment drives to the set destination or the test time exceeds the set maximum operation limit value, the test is finished;
and step S09, analyzing the data recorded in the step S07, and evaluating the test result, wherein the test result passes or fails.
Preferably, the step S01 includes the following sub-steps:
step S011, analyzing the acquired high-precision map through a corresponding protocol thereof, and extracting required map information;
step S012, developing an automatic map generation tool, automatically generating the extracted map information into an Opendrive map format which can be imported into scene simulation software, importing a map into the scene simulation software, and generating a static road network;
s013, building a vehicle dynamic model in scene simulation software, wherein the vehicle dynamic model comprises parameter modeling of a vehicle body, a chassis, an engine, a suspension, steering, transmission and a brake mechanism, and the vehicle dynamic model is calibrated with the dynamic performance of a real vehicle to realize that the transverse and longitudinal errors are less than 10%;
step S014, building a camera model on scene simulation software, including internal and external parameter setting of a camera, calibrating with actual road shooting, and adjusting camera parameters to realize that the error of the actual shooting effect is less than 10%;
step S015, after the static road network is generated in the step S012, a dynamic scene is built, the running track and the running speed of the mobile unit are set according to the specific test scene requirements, and a trigger is added to control the state change of the barrier; or setting a running track of the mobile unit in a random traffic flow mode to generate a random dynamic scene, wherein the mobile unit comprises obstacle vehicles and pedestrians.
Preferably, the step S03 includes the following sub-steps:
step S031, simulating a camera signal, namely simulating a video stream signal shot by a real road test camera by loading the camera model which is set up in the step S01 and simulating a video stream shot by the camera model in an environment;
step S032, radar signal simulation, namely analyzing signals required by a radar in a simulation test scene, and directly simulating the radar signals in an assignment mode according to a CAN signal protocol sent by the actual radar;
step 033, simulating a chassis signal, analyzing a corresponding signal from a simulation test scene according to a chassis CAN signal protocol, and performing assignment processing;
step S034, positioning signal simulation, namely simulating the positioning information of the vehicle in a simulation environment through the position of the vehicle model built in the step S01 in the static road network;
step S035, synchronous pulse signal simulation, outputting square wave signals with the 1S period duty ratio of 50% in workstation upper computer software to simulate pulse signals for synchronizing the chips in the intelligent navigation area controller;
and step S036, simulating a fault signal, and simulating a fault caused by an external factor in fault injection equipment.
Preferably, in step S04, the camera signal is injected through a video device, the radar signal, the chassis signal and the positioning signal are injected into the intelligent navigation domain controller through a CAN communication board, the synchronization pulse signal is injected into the intelligent navigation domain controller through an I/O signal board, and the fault signal is injected into the intelligent navigation domain controller through a fault injection device.
Preferably, the step S015 may build a demand test scenario library in advance, and load scenarios as needed when the test system is performed.
The technical scheme of the invention has the following beneficial technical effects:
the invention adopts a real intelligent navigation domain controller, constructs required sensor signals (such as a camera, a radar, a chassis, positioning and the like) through scene software, and injects the signals into the domain controller to test and verify the functions of all algorithms in the intelligent navigation domain controller. The testing method can more timely and comprehensively discover the defects of the software algorithm and the hardware, shorten the development period and save a great deal of risk cost brought by real vehicle testing;
according to the method, the real vehicle data are analyzed, the Opendrive format is generated, the scene simulation software is introduced, the problem that the simulation map is matched with the actual algorithm loading map module is solved, the static road network of the user is built through the method, richer dynamic scenes are simulated, and the method is safe, reliable and strong in repeatability;
the invention adopts the method of using the car machine in the loop, reduces the development of navigation information simulation, can carry out human-computer interaction operation more truly as a real car, and the car machine outputs the navigation information back to the controller by receiving the positioning information simulated by the virtual scene, thereby realizing the verification of the high-precision positioning fusion algorithm.
Drawings
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a flow chart illustrating the steps of the present invention;
FIG. 3 is a flowchart of the test scenario setup procedure in the present invention;
FIG. 4 is a flow chart of the steps of the sensor signal required for simulation in the present invention
Reference numbers in the figures: 1. a power management module; 2. a workstation; 201. upper computer software; 202. scene simulation software; 3. a real-time simulation machine; 301. a high performance processor; 302. an I/O signal board card; 303. a CAN communication board card; 4. a video injection device; 5. a fault injection device; 6. a signal conditioning device; 7. a vehicle machine device; 8. a program-controlled power supply; 9. an intelligent navigation domain controller; 901. a first chip; 902. a second chip; 903. and a third chip.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
As shown in fig. 1, an intelligent piloting advanced assistant driving hardware-in-loop test system provided by one aspect of the present invention includes a power management module 1, a workstation 2, a real-time simulator 3, a video injection device 4, a fault injection device 5, a signal conditioning device 6, a vehicle machine device 7, a programmable power supply 8, and an intelligent piloting domain controller 9.
The power management module 1 mainly implements functions of controlling, distributing, and protecting the power of the entire test device. The programmable power supply 8 can adjust the voltage according to the requirement to supply power for the intelligent navigation domain controller 9; the programmable power supply 8 has a voltage compensation function and can adjust the voltage drop generated in the voltage transmission process, so that the voltage is kept at a specific voltage value by the intelligent navigation domain controller 9.
On the workstation 2, a Windows operating system is adopted, and the upper computer software 201 and the scene simulation software 202 are installed and communicated with the real-time simulator 3 through the ethernet.
The real-time simulator 3 is configured with a high-performance processor 301, an I/O signal board 302 and a CAN communication board 303. The high performance processor 301 runs a real time operating system, installs lower computer software and runs a vehicle dynamics model, and communicates with the workstation 2 via ethernet. The real-time simulator 3 realizes the communication between the chassis signal of the vehicle body and the radar signal with the intelligent navigation domain controller 9 through the CAN communication board card 303. The real-time simulator 3 communicates with the intelligent navigation domain controller 9 between digital signals and analog signals through the I/O signal board 302 and the signal conditioning equipment 6.
The video injection equipment 4 is used for converting the video stream signal output by the workstation 2 into a vehicle-mounted GMSL video stream signal; when the workstation 2 runs the scene simulation software 202, a video stream is output through a DP interface of the workstation 2, and the video injection device receives the video stream through a miniDP interface; and a processor in the video injection device 4 synchronously performs format conversion and protocol conversion on the received video stream, and finally outputs a video image matched with the interface of the intelligent navigation domain controller 9 to realize video injection for the intelligent navigation domain controller 9.
A first chip 901, a second chip 902 and a third chip 903 are arranged in the intelligent navigation domain controller 9; the first chip 901 mainly realizes an intelligent navigation control planning algorithm, the second chip 902 mainly realizes a deep learning and camera perception algorithm, the third chip 903 mainly realizes a high-precision map positioning fusion algorithm and a diagnosis function safety algorithm, and the third chip 903 is also internally provided with a 32G memory space for storing high-precision map data. The third chip 903 is used for fusing a positioning signal, a sensing signal output by the second chip 902 and a navigation signal output by the vehicle-mounted equipment 7 based on high-precision map data to comprehensively generate vehicle relative position, high-precision map information and navigation information; after the first chip 901 receives the signals, vehicle control signals such as an accelerator, a brake and a steering wheel are output through a planning control algorithm of the first chip 901; the vehicle control signal is transmitted to the high-performance processor 301 through the CAN communication board 303 of the real-time simulator 3 to control the vehicle dynamics model.
The car-mounted device 7 is integrated with a navigation map, the car-mounted device 7 receives a GPS positioning signal through a CAN communication board card 303 of the real-time simulator 3, and the car-mounted device 7 outputs navigation information after a user inputs destination information in the car-mounted device 7; the navigation information is transmitted to the intelligent navigation area controller 9 through the ethernet.
The method of using the vehicle MP5 in the loop is adopted, development of navigation information simulation is reduced, human-computer interaction operation can be carried out more truly as a real vehicle, the vehicle equipment 7 receives positioning information of virtual scene simulation, the navigation information is output and fed back to the intelligent navigation domain controller 9, and verification of a high-precision positioning fusion algorithm is achieved.
The signal conditioning equipment 6 is used for realizing the electrical characteristic matching between the I/O signal board 302 of the real-time simulator 3 and the intelligent navigation domain controller 9; meanwhile, the device is provided with an overcurrent and overvoltage protection circuit, when the input current and the voltage exceed a certain value, the circuit can be automatically cut off, so that the intelligent navigation domain controller 9 and the I/O signal board card 302 are protected, and the damage of the test device caused by the wrong connection of a signal wire by a tester is avoided.
The fault injection device 5 simulates faults caused by various external factors which may occur, and directly acts on the output or input end of the intelligent navigation domain controller 9 through a model or other additional hardware to realize the injection of various faults, so as to verify the diagnosis and functional safety algorithm.
Referring to fig. 2-4, the intelligent piloting advanced driver assistance hardware-in-the-loop test method provided by the second aspect of the invention includes step S01, building a test scenario; as shown in fig. 3, the test scenario building includes the following sub-steps: step S011, analyzing the acquired high-precision map through a corresponding protocol thereof, and extracting required map information; step S012, developing automatic map generation tool, automatically generating Opendrive map format capable of being imported in scene simulation software from the extracted map information, importing map in the scene simulation software, and generating static road network;
the Opendrive format is generated, the scene simulation software 202 is imported, the problem of matching of a simulation map and an actual algorithm loading map module is solved, the static road network is built through the method, richer dynamic scenes are simulated, and the method is safe, reliable and strong in repeatability.
And S013, building a vehicle dynamic model in the scene simulation software 202, wherein the vehicle dynamic model comprises parameter modeling of a vehicle body, a chassis, an engine, a suspension, steering, transmission and a brake mechanism, and the vehicle dynamic model is calibrated with the dynamic performance of a real vehicle to realize that the transverse and longitudinal errors are less than 10%. Step S014, a camera model is built on the scene simulation software 202, the camera model comprises internal and external parameter settings of the camera, and camera parameters are adjusted by calibrating the camera model with actual road shooting, so that the error of the camera model with the actual shooting effect is less than 10%. Step S015, after the static road network is generated in the step S012, a dynamic scene is built, barrier vehicles, pedestrian running tracks and running speeds are set according to specific test scene requirements, and triggers are added to control the state change of the barriers. And the barrier vehicles and pedestrians can be set in a random traffic flow mode to generate a random dynamic scene. The step is a preposed step of the test system, a requirement test scene library can be set up in advance, and the scene is loaded as required when the test system is carried out. The track and the running speed of the pet or other movable objects can be included.
Step S02, connecting lines of intelligent piloting advanced assistant driving hardware among all devices of the ring test system, and then electrifying the devices;
step S03, simulating a required sensor signal; as shown in fig. 4, the test scenario building includes the following sub-steps: and step S031, simulating a camera signal, namely, simulating a video stream signal shot by the real road test camera by loading the video stream shot by the camera model in the simulation environment, wherein the camera model is set up in the step S01. And S032, simulating the radar signal, analyzing a signal required by the radar in a simulation test scene, and directly simulating the radar signal in an assignment mode according to a CAN signal protocol sent by an actual radar. Step 033, simulating a chassis signal, analyzing a corresponding signal from a simulation test scene according to a chassis CAN signal protocol, and performing assignment processing; the switching signal for controlling the on/off of the actual vehicle body is assigned to the chassis signal by setting up the switching signal through the upper computer software 201 of the workstation 2. Step S034, positioning signal simulation, namely simulating the positioning information of the vehicle in a simulation environment through the position of the vehicle model built in the step S01 in the static road network; step S035, synchronous pulse signal simulation, square wave signals with the 1S period duty ratio of 50% are output in the upper computer software 201 of the workstation 2, and pulse signals synchronous to chips in the intelligent navigation domain controller 9 are simulated. Step S036, simulating a fault signal, in which a fault caused by an external factor, such as a camera being blocked by the fault signal, is simulated in the fault injection device 5. This step is only required when verifying the fault diagnosis and functional safety algorithms in the intelligent navigation domain controller 9.
The functions of all algorithms in the intelligent navigation domain controller 9 are tested and verified by adopting the real intelligent navigation domain controller 9, constructing required sensor signals (camera, radar, chassis, positioning and the like) through the scene simulation software 202, and injecting the signals into the domain controller. The testing method can more timely and comprehensively discover the defects of the software algorithm and the hardware, shorten the development period and save a large amount of risk cost brought by real vehicle testing.
Step S04, injecting the signals simulated in the step S03 into the intelligent navigation area controller 9 respectively; camera signals are injected 4 through a video device; radar signals, chassis signals and positioning signals are injected into the intelligent navigation domain controller 9 through the CAN communication board card 303; the synchronous pulse signals are injected into the intelligent navigation domain controller 9 through the I/O signal board card 302, and the fault signals are injected into the intelligent navigation domain controller 9 through the fault injection equipment 5.
Step S05, a navigation destination is set on the in-vehicle device 7, and after the in-vehicle device 7 receives the positioning information through the CAN communication, the navigation information is planned and then transmitted to the intelligent navigation domain controller 9 through the ethernet.
Step S06, configuring a simulated intelligent piloting advanced assistant driving main switch in the upper computer software, simulating a function switch of a real vehicle body, and injecting a simulated switch signal into the intelligent piloting area controller 9 through CAN communication; and then operating the intelligent piloting advanced assistant driving hardware in-loop test system, wherein the system comprises upper computer software 201 for operating the workstation 2, operating scene simulation software 202 and starting a main switch for simulating the intelligent piloting advanced assistant driving.
Step S07, automatically recording process data required by the test through a developed data recording module in the process of operating the intelligent piloting advanced assistant driving hardware in the loop test system;
in step S08, when the vehicle in the simulated environment is driven to the set destination or the test time exceeds the set maximum operating limit, the test is automatically ended.
Step S09, it is evaluated whether the test passed by analyzing the data recorded in step S07.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention.
Claims (10)
1. An intelligent piloting advanced driver assistance hardware-in-the-loop test system is characterized by comprising a power supply management module (1) for controlling, distributing and protecting a system test equipment power supply; a programme-controlled power supply (8) for being directed at intelligent navigation area controller (9) power supply still includes:
the workstation (2) is provided with upper computer software (201) and scene simulation software (202) and outputs video stream signals;
the video injection equipment (4) is used for converting the video stream signal output by the workstation (2) into a vehicle-mounted GMSL video stream signal, outputting a video image matched with the interface of the intelligent navigation domain controller (9) and realizing video injection for the intelligent navigation domain controller (9);
the real-time simulator (3) is provided with a high-performance processor (301), an I/O signal board card (302) and a CAN communication board card (303); the communication between the chassis signals of the vehicle body and the radar signals is established with the intelligent navigation domain controller (9) through the CAN communication board card (303), the high-performance processor (301) runs a real-time operating system, is provided with lower computer software and runs a vehicle dynamics model, and establishes communication with the workstation (2) through the Ethernet.
2. The intelligent piloting advanced assistant driving hardware-in-the-loop test system as claimed in claim 1, further comprising a car machine (7) integrated with a navigation map, and receiving a GPS positioning signal through the CAN communication board card (303);
the fault injection equipment (5) is used for injecting a simulation fault into the intelligent navigation domain controller (9);
the signal conditioning equipment (6) is used for realizing electrical characteristic matching between the I/O signal board card (302) and the intelligent navigation domain controller (9) and realizing communication between digital signals and analog signals between the real-time simulator (3) and the intelligent navigation domain controller (9);
the intelligent navigation domain controller (9) is provided with a first chip (901), a second chip (902) and a third chip (903), the third chip (903) integrates a GPS positioning signal, a perception signal output by the second chip (902) and a navigation signal output by the vehicle equipment (7) based on high-precision map data to acquire the relative position of a vehicle, high-precision map information and navigation information to form a signal set, the first chip (901) receives the signal set signal and outputs a vehicle control signal, the CAN communication board card (303) transmits the vehicle control signal to the high-performance processor (301) to control a vehicle dynamics model, and the navigation signal output by the vehicle equipment (7) is transmitted to the intelligent navigation domain controller (9) through an Ethernet.
3. An intelligent piloted advanced driver assistance hardware-in-the-loop test system as claimed in claim 1, characterized in that said workstation (2) outputs a video stream signal through a DP interface of the workstation (2) when running the scene simulation software (202), and said video injection device (4) receives the video stream signal through a mini DP interface and synchronously performs format conversion and protocol conversion on the received video stream signal through its internal processor.
4. The system for testing the hardware-in-the-loop of the intelligent piloting advanced assistant driver according to claim 2, characterized in that the first chip (901) can be used for realizing the control planning of the intelligent piloting domain controller (9), the second chip (902) can be used for realizing the deep learning and the camera perception, and the third chip (903) can be used for realizing the high-precision map positioning fusion and the diagnosis function safety.
5. The system for testing the intelligent piloted advanced driver assistance hardware in the loop as claimed in claim 4, wherein a memory space is provided in the third chip (903) for storing high precision map data.
6. An intelligent piloting advanced assistant driving hardware in-loop test method is based on the intelligent piloting advanced assistant driving hardware in-loop test system and is characterized by comprising the following steps:
step S01, building a test scene;
step S02, connecting the intelligent piloting advanced assistant driving hardware to the circuit between the devices of the ring test system, and then electrifying the devices;
step S03, simulating a required sensor signal;
step S04, injecting the simulated signal in the step S03 into the intelligent navigation domain controller;
step S05, setting a navigation destination on the vehicle equipment;
step S06, configuring a simulated intelligent piloting advanced auxiliary driving main switch in upper computer software, simulating a function switch of a real vehicle body, injecting a signal of the simulated switch into an intelligent piloting domain controller through CAN communication, and then operating an intelligent piloting advanced auxiliary driving hardware-in-the-loop test system, wherein the steps of operating the upper computer software of a workstation, operating scene simulation software and starting the simulated intelligent piloting advanced auxiliary driving main switch are included;
step S07, recording the result of the test data;
step S08, when the vehicle in the simulation environment drives to the set destination or the test time exceeds the set maximum operation limit value, the test is finished;
and step S09, analyzing the data recorded in the step S07, and evaluating the test result, wherein the test result passes or fails.
7. The intelligent piloting advanced driver assistance hardware in-the-loop test method of claim 6, characterized in that: the step S01 includes the following sub-steps:
step S011, analyzing the acquired high-precision map through a corresponding protocol thereof, and extracting required map information;
step S012, developing an automatic map generation tool, automatically generating the extracted map information into an Opendrive map format which can be imported into scene simulation software, importing a map into the scene simulation software, and generating a static road network;
s013, building a vehicle dynamic model in scene simulation software, wherein the vehicle dynamic model comprises parameter modeling of a vehicle body, a chassis, an engine, a suspension, steering, transmission and a brake mechanism, and the vehicle dynamic model is calibrated with the dynamic performance of a real vehicle to realize that the transverse and longitudinal errors are less than 10%;
step S014, building a camera model on scene simulation software, including internal and external parameter setting of a camera, calibrating with actual road shooting, and adjusting camera parameters to realize that the error of the actual shooting effect is less than 10%;
step S015, after the static road network is generated in the step S012, a dynamic scene is built, the running track and the running speed of the mobile unit are set according to the specific test scene requirements, and a trigger is added to control the state change of the barrier; or setting a running track of the mobile unit in a random traffic flow mode to generate a random dynamic scene, wherein the mobile unit comprises obstacle vehicles and pedestrians.
8. The intelligent piloting advanced driver assistance hardware in-the-loop test method of claim 6, characterized in that: the step S03 includes the following sub-steps:
step S031, simulating a camera signal, namely simulating a video stream signal shot by a real road test camera by loading the camera model which is set up in the step S01 and simulating a video stream shot by the camera model in an environment;
step S032, radar signal simulation, namely analyzing signals required by a radar in a simulation test scene, and directly simulating the radar signals in an assignment mode according to a CAN signal protocol sent by the actual radar;
step 033, simulating a chassis signal, analyzing a corresponding signal from a simulation test scene according to a chassis CAN signal protocol, and performing assignment processing;
step S034, positioning signal simulation, namely simulating the positioning information of the vehicle in a simulation environment through the position of the vehicle model built in the step S01 in the static road network;
step S035, synchronous pulse signal simulation, outputting square wave signals with the 1S period duty ratio of 50% in workstation upper computer software to simulate pulse signals for synchronizing the chips in the intelligent navigation area controller;
and step S036, simulating a fault signal, and simulating a fault caused by an external factor in fault injection equipment.
9. The intelligent piloting advanced driver assistance hardware in-the-loop test method of claim 6, characterized in that: in the step S04, the camera signal is injected through the video device, the radar signal, the chassis signal and the positioning signal are injected into the intelligent navigation area controller through the CAN communication board, the synchronization pulse signal is injected into the intelligent navigation area controller through the I/O signal board, and the fault signal is injected into the intelligent navigation area controller through the fault injection device.
10. The intelligent piloting advanced driver assistance hardware in-the-loop test method of claim 7, wherein: in the step S015, a demand test scenario library may be set up in advance, and a scenario may be loaded as needed when the test system is performed.
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