CN117191342A - ADB intelligent car lamp SIL test system based on VTD - Google Patents
ADB intelligent car lamp SIL test system based on VTD Download PDFInfo
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Abstract
The invention is applicable to the technical field of intelligent driving, and provides an ADB intelligent car lamp SIL test system based on a VTD, which comprises the following steps: the VTD simulation platform is used for constructing a virtual test scene for the self-adaptive high and low beam road conditions, the high-speed road conditions, the gradient road conditions and the curve road conditions of the ADB intelligent car lamp; and the upper computer system is matched with the VTD simulation platform to automatically test the ADB intelligent car lamp. According to the invention, virtual test scenes are built for various road conditions of the ADB intelligent car lamp based on the VTD simulation platform, software-level building is performed for the ADB functional logic based on the CANoe platform, dynamic simulation is performed for the car by using the DYNA4, so that a SIL test closed loop for the ADB is formed, the performance of the ADB algorithm logic under a pressure test can be rapidly verified, parameterized adjustment can be performed for the test scenes, and the research and development period of the ADB can be effectively shortened.
Description
Technical Field
The invention belongs to the technical field of intelligent driving, and particularly relates to an ADB intelligent car lamp SIL test system based on a VTD.
Background
The automobile industry is actively developed in an intelligent manner, and a car lamp serving as a road lighting role is developed from basic functional lighting to an intelligent visual interaction system. ADB (Adaptive Driving Beam) self-adaptation head-light function is as important part in the intelligent car light, can promote the night driving security and the intelligent of vehicle under low light condition. Currently, ADB adaptive headlights are often combined with an image processor, and the projected light pattern of the vehicle lamp is dynamically adjusted by sensing and analyzing traffic participants in the surrounding environment of the vehicle.
At present, the test on the ADB is mainly an actual test, wherein most of the test is a light distribution test aiming at the luminous intensity of a car lamp in a laboratory darkroom environment and a real road test of an ADB assembly real car. Aiming at the aspect of an adjusting method and a device of an ADB intelligent car lamp, in the self-adaptive adjusting method and the device of the brightness and the light area of the prior art, when the ADB is activated, the self-adaptive adjusting method of the brightness and the light area of the ADB intelligent car lamp firstly lights up a central light area, and then lights up other light areas on two sides in sequence, so that the illumination intensity is basically consistent after each subarea is overlapped with the ambient light; if a certain light area is in a closed state, the light area is skipped, the next light area in an open state is lightened, and the self-adaptive adjustment of the brightness under different environment lights is carried out, so that the problem of inconsistent illuminance after the ADB light is overlapped with different environment lights can be effectively solved.
However, the current analog simulation test for ADB still has a gap, and the verification of ADB technology generally needs an expensive and time-consuming real-road test, however, the conventional road test and laboratory verification methods are limited by geographical locations and climate conditions, and often cannot cover various complex road environments. Therefore, we propose a VTD-based ADB intelligent car lamp SIL test system.
Disclosure of Invention
The invention aims to provide a VTD-based ADB intelligent car lamp SIL test system, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an ADB intelligent vehicle lamp SIL test system based on VTD, comprising:
the VTD simulation platform is used for building virtual test scenes of self-adaptive far and near light road conditions, high-speed road conditions, gradient road conditions and curve road conditions of the ADB intelligent car lamp and comprises a scene library, a camera sensor, an optical machine model, a driver model, a car model and a radar sensor module;
the upper computer system is used for automatically testing the ADB intelligent car lamp in a mode of being matched with the VTD simulation platform and comprises a CANoe platform, a DYNA4 dynamic platform and a vTESTstudio automatic testing module;
the DYNA4 dynamic platform comprises a current frame state, dynamic calculation and a next frame state;
the vtestudio automation test module comprises an automation flow execution module;
the CANoe platform comprises:
IO module, VTD sensor perception information, VTD camera acquisition picture, VTD sensor original data, driver information, vehicle pose information, ADB controller SIL logic module and light type output;
the IO module is used for receiving information sent to the CANoe platform in the VTD simulation platform, and the IO module obtains the sensing information of the VTD sensor, the acquisition picture of the VTD camera, the original data of the VTD sensor, the driver information and the vehicle pose information through unpacking and analyzing the input information of the VTD simulation platform;
the IO module is in communication connection with the light type output;
the ADB controller SIL logic module is used for judging the perception information of the VTD sensor, the acquired picture of the VTD camera and the original data of the VTD sensor, and inputting the light type output information into the IO module for outputting to the VTD simulation platform after judging;
the IO module is in communication connection with the DYNA4 dynamic platform and is used for acquiring high-precision DYNA4 dynamic information;
and the IO module is in communication connection with an automatic flow execution module in the vtestudio automatic test module and is used for completing the automatic test of the ADB intelligent car lamp.
Further, the ADB controller SIL logic module includes three test modes:
when the input is the perception information of the VTD sensor, the object information detected by the VTD simulation platform is directly sent to an ADB intelligent car lamp SIL logic module, and the relative positions and the relative speed information of the vehicle and the pedestrian which are identified and separated by the VTD sensor are directly sent to an ADB controller;
when the input is that a VTD camera collects images, the images collected by a VTD simulation platform are directly injected into an ADB intelligent car lamp SIL logic module, and the ADB intelligent car lamp SIL logic module firstly carries out object identification through a vision-oriented algorithm and then carries out light type output judgment;
when the input is the original data of the VTD sensor and the image is acquired by the VTD camera, the original data acquired by the VTD simulation platform is input into the ADB intelligent car lamp SIL logic module to sense the surrounding environment of the vehicle, the ADB intelligent car lamp SIL logic module further transmits the information of the sensed traffic participant to the light logic, and finally the car lamp light type output of the next frame is output.
Furthermore, the scene library comprises a scene library special for self-adaptive far and near light road conditions, high-speed road conditions, gradient road conditions and curved road conditions.
Further, the camera sensor is used for providing a simulation picture for an ADB controller SIL logic module in the upper computer system and is used for testing visual recognition logic of the ADB controller.
Further, the optical machine model is used for projecting corresponding lamplight after receiving the light type output of the ADB controller SIL logic module.
Further, the driver model is used for carrying out corresponding steering, lane changing and acceleration actions on the vehicle through analysis of the road.
Furthermore, the host vehicle model is used for carrying out simulation modeling on a real vehicle and providing vehicle information.
Further, the radar sensor is used for disassembling the detection information and sending the detection information to an ADB controller SIL logic module of the upper computer system, and is used for testing and analyzing logic of the ADB intelligent car lamp.
Further, the DYNA4 dynamic platform further comprises a current frame state and dynamic calculation, wherein the current frame state is used for receiving driver information and vehicle pose information, the dynamic calculation is used for carrying out dynamic calculation on next frame pose information of the vehicle model, and the calculated information is transmitted to the IO module for communication through the next frame state.
Further, the vtestudio automation test module further includes an ADB test writing, source file, execution log, and test report module.
Compared with the prior art, the invention has the beneficial effects that:
1. the VTD simulation platform covers wide and various road conditions and environmental changes, and can simulate and build complex road traffic situations such as overpasses from roads with different gradients to various climatic conditions (rainy days, snowy days, sunny days, cloudy days and the like). The full scene simulation capability can comprehensively evaluate the performance of the ADB technology under various roads, and can meet the verification requirements of the ADB intelligent car lamp under various complex traffic environments.
2. The invention ensures the safety of the test by setting up the ADB test simulation environment based on the VTD, avoids the potential risks to experimenters and the environment, and has remarkable safety advantage when carrying out limit tests of high-speed running, wet and slippery road conditions of rain and snow and the like.
3. Compared with the traditional field road test, the VTD simulation platform has obvious cost advantage, and the built VTD virtual scene can be reused, so that various complex road conditions and scenes can be simulated at lower economic cost.
4. The invention uses the VTD analog simulation platform to carry out ADB light test, which can lead the test scene to be reproduced accurately, including confirmation of parameters such as weather, road gradient, traffic condition and the like; 1 can be realized: a full reproduction of level 1 helps to evaluate the performance of ADB headlights in different situations with a high degree of accuracy. The VTD simulation test ADB technology provides a high-efficiency controllable test platform for development teams, and is beneficial to development and test of ADB intelligent car lights.
5. According to the invention, the SIL test is built on the basis of the VTD simulation platform, and the logic of the ADB function can be quickly built and iterated through CANoe software, so that the development period of ADB function test and verification is shortened.
6. When the SIL test built on the VTD-based simulation platform is used, the function of the ADB headlight can be tested by using the vTESTstudio automatic test software, manual intervention is not needed, labor is saved, and test scores and test original data of the ADB function under various virtual scenes can be automatically generated after the test.
Drawings
Fig. 1 is a schematic structural view of the present invention.
Fig. 2 is a schematic diagram of a scene library in the VTD simulation platform of the present invention.
Fig. 3 is a schematic diagram illustrating a scene library by a vehicle coordinate system according to the present invention.
FIG. 4 is a schematic diagram of the anti-glare function of the ADB of the present invention.
Fig. 5 is a schematic diagram of a vehicle layout in a static scenario of the adaptive high and low beam road conditions of the present invention.
Fig. 6 is a schematic diagram of a high-speed road condition test scenario according to the present invention.
Fig. 7 is a schematic diagram of an ADB lamp optimized lamp illumination tilt angle under a grade road condition of the present invention.
Fig. 8 is a schematic diagram of a curve road condition test scenario according to the present invention.
Fig. 9 is a schematic diagram of a curve road condition-curve deviation road condition test scenario according to the present invention.
Fig. 10 is a schematic diagram of a curve road condition-curve shielding road condition test scenario according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Specific implementations of the invention are described in detail below in connection with specific embodiments.
As shown in fig. 1 and fig. 2, an ADB intelligent car light SIL test system based on VTD according to an embodiment of the present invention includes:
the VTD simulation platform is used for building virtual test scenes of the self-adaptive far and near light road conditions, the high-speed road conditions, the gradient road conditions and the curve road conditions of the ADB intelligent car lamp and comprises a scene library, a camera sensor, an optical machine model, a driver model, a host car model and a radar sensor module;
the upper computer system is used for automatically testing the ADB intelligent car lamp in a mode of being matched with the VTD simulation platform and comprises a CANoe platform, a DYNA4 dynamic platform and a vTESTstudio automatic testing module;
the DYNA4 dynamic platform comprises a current frame state, dynamic calculation and a next frame state;
the vtestudio automation test module comprises an automation flow execution module;
the CANoe platform comprises:
IO module, VTD sensor perception information, VTD camera acquisition picture, VTD sensor original data, driver information, vehicle pose information, ADB controller SIL logic module and light type output;
the IO module is used for receiving information sent to the CANoe platform in the VTD simulation platform, and the IO module obtains the sensing information of the VTD sensor, the acquisition picture of the VTD camera, the original data of the VTD sensor, the driver information and the vehicle pose information through unpacking and analyzing the input information of the VTD simulation platform;
the IO module is in communication connection with the light type output;
the ADB controller SIL logic module is used for judging the perception information of the VTD sensor, the acquired picture of the VTD camera and the original data of the VTD sensor, and inputting the light type output information into the IO module for outputting to the VTD simulation platform after judging;
the IO module is in communication connection with a next frame state in the DYNA4 dynamic platform and is used for acquiring high-precision DYNA4 dynamic information;
and the IO module is in communication connection with an automatic flow execution module in the vtestudio automatic test module and is used for completing the automatic test of the ADB intelligent car lamp.
In the embodiment of the present invention, preferably, the VTD simulation platform is built under the Ubuntu operating system. The ADB controller SIL logic module is an ADB controller logic module based on a pure software layer built by the CANoe platform, and receives data in the VTD simulation platform for judgment.
As a preferred embodiment of the present invention, the ADB controller SIL logic module includes three test modes:
when the input is the perception information of the VTD sensor, the object information detected by the VTD simulation platform is directly sent to an ADB intelligent car lamp SIL logic module, the ADB controller is not required to carry out the identification analysis of the picture, and the relative positions and the relative speed information of the vehicle and the pedestrian which are identified and separated by the VTD sensor are directly sent to the ADB controller;
when the input is that a VTD camera collects images, the images collected by a VTD simulation platform are directly injected into an ADB intelligent car lamp SIL logic module, and the ADB intelligent car lamp SIL logic module firstly carries out object identification through a vision-oriented algorithm and then carries out light type output judgment;
when the input is the original data of the VTD sensor and the image is acquired by the VTD camera, the original data acquired by the VTD simulation platform is input into the ADB intelligent car lamp SIL logic module to sense the surrounding environment of the vehicle, the ADB intelligent car lamp SIL logic module further transmits the information of the sensed traffic participant to the light logic, and finally the car lamp light type output of the next frame is output.
As shown in fig. 2, as a preferred embodiment of the present invention, the scene library includes a dedicated scene library designed for ADB intelligent vehicle lamp test, such as adaptive high and low beam road conditions, high speed road conditions, gradient road conditions, and curved road conditions.
As shown in fig. 1, as a preferred embodiment of the present invention, the camera sensor is used to provide a simulation screen for the SIL logic module of the ADB controller in the upper computer system, and is used to test the visual recognition logic of the ADB controller.
In the embodiment of the present invention, preferably, the camera sensor is an artificial camera built in accordance with a camera used by a vehicle in the real physical world.
As shown in fig. 1, as a preferred embodiment of the present invention, the optomechanical model is used to project corresponding lights after receiving the light-type output of the SIL logic module of the ADB controller.
In the embodiment of the invention, preferably, the optical machine model is a simulation model built according to the lamplight configuration of the ADB controller system to be tested.
As shown in fig. 1, as a preferred embodiment of the present invention, the driver model is used to perform corresponding steering, lane changing and acceleration actions on the vehicle through analysis of the road.
In the embodiment of the present invention, preferably, the driver model is a driver in the VTD simulation platform.
As shown in fig. 1, as a preferred embodiment of the present invention, the present vehicle model is used for simulation modeling of a real vehicle and providing vehicle information.
In the embodiment of the invention, preferably, the vehicle model is simulation modeling of a real vehicle in the VTD simulation platform, 3D modeling is carried out on a vehicle model carrying the ADB intelligent vehicle lamp and the model is imported into the VTD simulation platform, for example, the carried vehicle model of the ADB intelligent vehicle lamp cannot be modeled due to confidentiality and other reasons. The host vehicle model also includes information on the vehicle speed, the vehicle pitch angle, the vehicle coordinates, and the like of the vehicle in the VTD simulation platform.
As shown in fig. 1, as a preferred embodiment of the present invention, the radar sensor is configured to disassemble the detection information and send the detection information to the SIL logic module of the ADB controller of the host computer system, so as to test and analyze logic of the ADB intelligent vehicle lamp.
In the embodiment of the invention, preferably, the radar sensor is a simulation model of the radar sensor on a real vehicle in the VTD simulation platform, and the radar sensor can freely set the detailed parameters such as the FOV, the detection distance, the installation position and the like. The information of detected vehicles, pedestrians, bicycles, lane lines and the like can be disassembled in the VTD simulation platform and sent to an ADB controller SIL logic module, and the logic of the ADB intelligent car lamp is tested and analyzed.
As shown in fig. 1, as a preferred embodiment of the present invention, the DYNA4 dynamics platform further includes a current frame state, a dynamics calculation, and a next frame state, where the current frame state is used to receive driver information and vehicle pose information, the dynamics calculation is used to perform accurate dynamics calculation on the next frame pose information of the vehicle model, and the calculated information is transmitted to the IO module through the next frame state for communication.
As shown in FIG. 1, the vtestudio automation test module further comprises ADB test writing, source files, execution logs, and test reporting modules as a preferred embodiment of the present invention.
In the embodiment of the invention, preferably, the vtestudio automatic test platform provides an automatic test function for the system, builds an automatic flow of the ADB intelligent car lamp test based on the design of dynamic traffic of the ADB intelligent car lamp test scene, completes the automatic test execution of the ADB intelligent car lamp through communication with the IO module, and records video of the test process of the VTD simulation platform, stores the test case raw data, executes the report and generates the automatic test report.
The invention builds a virtual test scene for the self-adaptive far and near light road condition, the high-speed road condition, the gradient road condition and the curve road condition of the ADB intelligent vehicle lamp based on the VTD simulation platform, builds a software layer for the ADB functional logic based on the CANoe platform, and uses DYNA4 to simulate the dynamics of the vehicle to form a SIL (software in the loop) test closed loop for the ADB, thereby being capable of rapidly verifying the performance of the ADB algorithm logic under the pressure test, carrying out parameterization adjustment for the test scene and effectively shortening the research and development period of the ADB.
In the ADB intelligent car lamp SIL test system based on the VTD provided by the embodiment of the invention, a scene library is built by using a simulation platform based on the VTD, and 1048 test scene libraries are designed for ADB functions, wherein the test scene library comprises self-adaptive far and near light road conditions, high-speed road conditions, gradient road conditions and curve road conditions, as shown in fig. 2.
In the scene library description built for the ADB function, the description is carried out by a vehicle coordinate system, and the origin of coordinates is at the midpoint of the connecting line of the rear wheels of the vehicle and keeps the same height with the ground, as shown in fig. 3.
1.1, self-adapting far and near light road conditions, see fig. 4, the left image is that the common car lamp has no anti-dazzle function, and the right image is that when the ADB anti-dazzle function is started, the light affecting the sight area of the front car is closed.
When the function of the ADB headlamp is activated, the headlamp can be switched between far and near light according to surrounding light, vehicle speed perception, camera images and other information acquired by the vehicle. In the running process of the vehicle, if the vehicle senses darkening of light (overcast and rainy weather, tunnel entering, ramp situation with insufficient light entering, etc.), the ADB headlamp can be automatically started, the distance light and the near light can be switched according to road conditions, and an anti-glare function can be triggered according to scenes. Aiming at the functions, the self-adaptive far and near light path condition test scene is designed into a static light basic working condition, a static vehicle shielding working condition, a dynamic uniform shielding working condition and a dynamic acceleration and deceleration shielding working condition, wherein the total number of the scene libraries is 100, and the detailed classification is shown in tables 1 and 2.
The adaptive far and near light road condition-static scene (see fig. 5) is designed for the activation of the ADB light function and the ADB anti-dazzle function under the shielding of the vehicle at the characteristic position, and comprises the following steps: s01, switching the light function of the ADB to activate a test scene; s02, activating a test scene by an ADB anti-dazzle function under a close-range five-lane shielding vehicle; s03, activating a test scene by an ADB anti-dazzle function under a middle-distance five-lane shielding vehicle; s04, activating a test scene by an ADB anti-dazzle function under the long-distance five-lane shielding vehicle.
The road conditions of S02, S03 and S04 are five same-direction lanes, the width of a designed lane is 3.75m, the width of a lane line is 3.75m to 4.0m according to laws and regulations, and the width of the road can be changed according to the width of a test vehicle, so that a scene library is generalized. The simulation time of the self-adaptive far and near light road condition-static scene is from 6:00 to 23:00 is a single cycle, the time jump interval is 0.5hour, each jump interval is 3s, and the simulation time of each scene is from 6:00 to 23:00 sets up three cycles altogether. The test output is: the driver visual angle of the vehicle, the rearview mirror visual angle of the test vehicle and the following overlooking visual angle of the vehicle (video recording of the test process); automating an execution log; ADB intelligent light test report.
TABLE 1 adaptive high and low beam road conditions-static scenario
In a self-adaptive far and near light road condition-dynamic scene, setting D01, D02, D03, D04 and D05 to respectively be an ADB anti-dazzle function activation test of a vehicle in front of a right front vehicle, a vehicle in front of a left two-vehicle road, a vehicle in front of a right two-vehicle road and an ADB anti-dazzle function activation test of a vehicle in front of the right two-vehicle road at a constant speed; d06, D07, D08, and D09 are set as the anti-glare function activation tests for the ADB, which are accelerated and decelerated near the left vehicle ahead, the right vehicle ahead, and the right vehicle ahead, respectively.
In the adaptive high and low beam road condition-dynamic scenario, the initial simulation time in the test scenario is set to be 20 at night: at the moment 00, the light of the vehicle is ensured to be in an on state. Wherein the test output is: the driver visual angle of the vehicle, the following overlook visual angle of the vehicle, the visual angle of a tested vehicle rearview mirror in a D01-D05 scene and the light visual angle of the monitored vehicle in a D06-D09 scene (video recording of the testing process); automating an execution log; ADB intelligent light test report.
TABLE 2 adaptive high and low beam road conditions-dynamic scenario
1.2, high-speed road conditions, refer to fig. 6, wherein the left graph is a schematic diagram of the light range of the non-excited ADB high-speed mode, and the right graph is a schematic diagram of the light range of the excited ADB high-speed mode.
In the high-speed road condition test scene, the front lighting lamp gathers light under the working condition of high-speed running mainly aiming at the ADB function, so that the function of enhancing the illumination intensity in front is achieved, and a driver can observe road conditions better during high-speed movement conveniently. On the other hand, an anti-dazzle function test scene when other traffic participants are in high-speed running is set in the high-speed road condition. See table 3.
In the high-speed road condition, setting D01, D02, D03, D04 and D05 as test scenes aiming at vehicles approaching the front vehicle, the front vehicle of the left two roads, the front vehicle of the right two roads and the front vehicle of the right two roads at uniform speed under the high-speed working condition; d06, D07, D08, and D09 are set as test scenes for accelerating and decelerating near the left vehicle ahead, the right vehicle ahead, and the right vehicle ahead under the high-speed condition, respectively.
The initial simulation time in the test scenario is set to night 20: at the moment 00, the starting state of the car lamp is ensured. The test output of the high-speed road condition is the same as the test output of the self-adaptive far and near light road condition-dynamic scene.
TABLE 3 high speed road conditions scenario
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1.3, gradient road conditions, see fig. 7.
When the ADB headlamp runs on a sloping road, the irradiation range of the lamp can be adjusted according to the inclination angle of the vehicle body, so that the situation that the lamp can not illuminate the ground when the vehicle runs on a sloping road and the lamp can not illuminate a road at a distance when the vehicle runs on a downhill road is improved, and the night safety coefficient of the vehicle on the sloping road is improved. The road condition of the gradient is divided into an ascending working condition, a descending working condition and an acceleration and deceleration working condition. In the gradient road condition, the initial simulation time in the test scene is set as night 20: at the moment 00, the starting state of the car lamp is ensured.
Wherein the test output is: the driver of the vehicle has a visual angle; monitoring the light visual angle of the car light in a U01-U12 scene; the U01-U12 host vehicle follows the overlooking view angle; testing the viewing angle of the rearview mirror of the vehicle in a U07-U12 scene; monitoring the light viewing angle of the car light in a D01-D12 scene; D01-D12 host vehicle follows the overlooking view; testing the viewing angle of the vehicle rearview mirror in a D07-D12 scene; monitoring the light visual angle of the car light in the scene A01-A12; A01-A12 the vehicle follows the overlooking view; testing the view angle of the rearview mirror of the vehicle in the scene A07-A12, and recording the video in the testing process; automating an execution log; ADB intelligent light test report.
In the gradient road condition-uphill condition, U01-U06 are respectively the test scenes of 10km/h, 20km/h, 30km/h, 40km/h, 50km/h and 60km/h uniform climbing of the host vehicle on the uphill road condition of 1-15 degrees. U07-U12 are respectively 10km/h, 20km/h, 30km/h, 40km/h, 50km/h and 60km/h of the climbing road condition host vehicle with 1-15 degrees of uniform climbing anti-glare test scenes. See table 4.
Table 4 grade road conditions-uphill conditions scenario
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In the slope road condition-downhill working condition, D01-D06 are respectively the test scenes of 10km/h, 20km/h, 30km/h, 40km/h, 50km/h and 60km/h uniform climbing of the downhill road condition host vehicle of 1-15 degrees. D07-D12 are respectively 10km/h, 20km/h, 30km/h, 40km/h, 50km/h and 60km/h of the downhill road condition host vehicle with the 1-15 degrees of downhill road condition, and are uniform-speed climbing anti-glare test scenes. See table 5.
TABLE 5 grade road conditions-downhill conditions scenario
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In the slope road condition-acceleration and deceleration working condition, A01-A06 are respectively 1 degree to 15 degrees of the up-and-down slope road condition, and the speed of the vehicle is 1m/s 2 、2m/s 2 、3m/s 2 、4m/s 2 、5m/s 2 And 6m/s 2 Test scene of the slope. A07-A12 are respectively 1 degree to 15 degrees of acceleration and deceleration of the host vehicle under the condition of up-down slope road 2 、2m/s 2 、3m/s 2 、4m/s 2 、5m/s 2 And 6m/s 2 Anti-glare test scene of slope road. See table 6.
TABLE 6 road condition with gradient-acceleration and deceleration conditions scene
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1.4, curve road conditions, see fig. 8, the left graph is the light range of the unexcited ADB curve mode, and the light illuminates the right front of the vehicle. The right graph shows the light range after exciting ADB curve mode, and the light is inclined to the forward curve direction.
ADB intelligent lamp has curve illumination function (curve auxiliary lighting), and the vehicle can only light the place ahead road when the road is passed through to the vehicle, and the directional curve of turning has and shines the blind area, and when the sensor of vehicle perceives the vehicle to turn, ADB intelligent lamp can adjust the scope of illumination, optimizes and shines the blind area, promotes the driving safety of vehicle when ambient light is not enough.
In the road condition of the curve, the road condition is divided into two scene libraries of a curve deviation working condition and a curve shielding working condition.
The road condition of the curve is constructed according to the current situation of the actual urban road intersection, the turning radius of the road of the main road in reality is 20 m-30 m section, the turning radius of the secondary road is 15 m-20 m section, and the turning radius of the road of the non-main road is 10 m-20 m section. And constructing a turning road with a turning radius of 10m to 30m in the curve road condition test scene library for testing the ADB function. In the curve road condition, the initial simulation time in the test scene is set to be 20 at night: at the moment 00, the starting state of the car lamp is ensured. The test output is: the driver of the vehicle has a visual angle; monitoring the light viewing angle of the car light in a C01-C11 scene; the C01-C11 host vehicle follows the overlooking view; monitoring the light visual angle of the car light in the scene of O01-O11; the O01-O11 host vehicle follows the overlooking view; O01-O11 shields the view angle of the test vehicle, and the video is recorded in the test process; automating an execution log; ADB intelligent light test report.
In the curve offset condition (see FIG. 9), C01-C11 are constant speed curve test scenarios with turning radii of 10m, 12m, 14m, 16m, 18m, 20m, 22m, 24m, 26m, 28m, and 30m, respectively. See table 7.
TABLE 6 road condition of bend-offset condition scene
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In the curve shielding working condition (see fig. 10), O01-O11 are constant speed curve anti-glare test scenes with turning radii of 10m, 12m, 14m, 16m, 18m, 20m, 22m, 24m, 26m, 28m and 30m respectively. See table 8.
Table 8 road conditions of curved road-curved road shielding condition scene
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The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and improvements can be made by those skilled in the art without departing from the spirit of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent.
Claims (10)
1. An ADB intelligent vehicle lamp SIL test system based on VTD, comprising:
the VTD simulation platform is used for building virtual test scenes of self-adaptive far and near light road conditions, high-speed road conditions, gradient road conditions and curve road conditions of the ADB intelligent car lamp and comprises a scene library, a camera sensor, an optical machine model, a driver model, a car model and a radar sensor module;
the upper computer system is used for automatically testing the ADB intelligent car lamp in a mode of being matched with the VTD simulation platform and comprises a CANoe platform, a DYNA4 dynamic platform and a vTESTstudio automatic testing module;
the DYNA4 dynamic platform comprises current vehicle pose information and a next frame vehicle pose state;
the vtestudio automation test module comprises an automation flow execution module;
the CANoe platform comprises:
IO module, VTD sensor perception information, VTD camera acquisition picture, VTD sensor original data, driver information, vehicle pose information, ADB controller SIL logic module and light type output;
the IO module is used for receiving information sent to the CANoe platform in the VTD simulation platform, and the IO module obtains the sensing information of the VTD sensor, the acquisition picture of the VTD camera, the original data of the VTD sensor, the driver information and the vehicle pose information through unpacking and analyzing the input information of the VTD simulation platform;
the IO module is in communication connection with the light type output;
the ADB controller SIL logic module is used for judging the perception information of the VTD sensor, the acquired picture of the VTD camera and the original data of the VTD sensor, and inputting the light type output information into the IO module for outputting to the VTD simulation platform after judging;
the IO module is in communication connection with the DYNA4 dynamic platform and is used for acquiring high-precision DYNA4 dynamic information;
and the IO module is in communication connection with an automatic flow execution module in the vtestudio automatic test module and is used for completing the automatic test of the ADB intelligent car lamp.
2. The VTD-based ADB intelligent car light SIL test system of claim 1, wherein the ADB controller SIL logic module comprises three test modes:
when the input is the perception information of the VTD sensor, the object information detected by the VTD simulation platform is directly sent to an ADB intelligent car lamp SIL logic module, and the relative positions and the relative speed information of the vehicle and the pedestrian which are identified and separated by the VTD sensor are directly sent to an ADB controller;
when the input is that a VTD camera collects images, the images collected by a VTD simulation platform are directly injected into an ADB intelligent car lamp SIL logic module, and the ADB intelligent car lamp SIL logic module firstly carries out object identification through a vision-oriented algorithm and then carries out light type output judgment;
when the input is the original data of the VTD sensor and the image is acquired by the VTD camera, the original data acquired by the VTD simulation platform is input into the ADB intelligent car lamp SIL logic module to sense the surrounding environment of the vehicle, the ADB intelligent car lamp SIL logic module further transmits the information of the sensed traffic participant to the light logic, and finally the car lamp light type output of the next frame is output.
3. The VTD-based ADB intelligent car light SIL test system of claim 1, wherein the scene library comprises a self-adaptive far and near light road condition, high speed road condition, gradient road condition, and curved road condition dedicated scene library.
4. The VTD-based ADB intelligent car light SIL test system of claim 1, wherein the camera sensor is configured to provide a simulation picture for an ADB controller SIL logic module in the host computer system and to test visual recognition logic of the ADB controller.
5. The VTD-based ADB intelligent car light SIL test system of claim 1, wherein the optomechanical model is configured to project corresponding lights upon receiving the light-type output of the ADB controller SIL logic module.
6. The VTD-based ADB intelligent car light SIL test system of claim 1, wherein the driver model is used to make corresponding steering, lane changing and acceleration actions on the vehicle through analysis of the road.
7. The VTD-based ADB intelligent car light SIL test system of claim 1, wherein the host vehicle model is used to model a real vehicle in simulation and provide vehicle information.
8. The VTD-based ADB intelligent vehicle lamp SIL test system of claim 1, wherein the radar sensor is configured to disassemble the detection information and send the detection information to an ADB controller SIL logic module of the host computer system for testing and analyzing logic of the ADB intelligent vehicle lamp.
9. The VTD-based ADB intelligent car light SIL test system of claim 1, wherein the DYNA4 dynamics platform further comprises a current frame state and dynamics calculation, and a next frame state, the current frame state is used for receiving driver information and vehicle pose information, the dynamics calculation is used for performing dynamics calculation on next frame pose information of a vehicle model, and the calculated information is transmitted to the IO module for communication through the next frame state.
10. The VTD-based ADB intelligent car light SIL test system of claim 1, wherein the vtestudio automation test module further comprises an ADB test writing, source file, execution log, and test report module.
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