ADB intelligent headlamp real vehicle open road test method

Abstract

The present application relates to the intelligent network connected vehicle testing technical field, specifically to a kind of ADB intelligent headlamp real vehicle open road test method, comprising the following steps: install the second test data acquisition equipment of collection road environment data on test vehicle;First test data acquisition equipment for collecting test vehicle's own data is installed on test vehicle;Road test data acquisition step: collection first test data and second test data;Road test data analysis step: trigger time of trigger signal is analyzed and judged, and vehicle state information in first test data and environmental state information in second test data in trigger time period are recorded, then separately stored;ADB function trigger condition is compared and analyzed;After all trigger signals are analyzed, test report is generated by summarizing.This application can solve the problem that the test scene built by prior art is very limited, only a small amount of typical scene verification can be done, and the test cost is expensive, and a large number of user real use scene cannot be covered.

Description

Technical Field

[0001] This invention relates to the field of intelligent connected vehicle testing technology, specifically to a method for testing ADB intelligent headlights on open roads in a real vehicle. Background Technology

[0002] The Adaptive Driving Beam (ADB) system is an intelligent high beam system that uses video camera signals to determine the position and distance of oncoming vehicles and adjusts the beam accordingly to avoid glare and maximize the driver's visibility.

[0003] Intelligent ADB headlights are a relatively new product that has emerged in recent years, and related testing technologies and methods are still under research. Traditional testing of lights mainly relies on laboratory testing methods for lighting components, while whole-vehicle testing technology for lights is not mature. The activation of the ADB function is closely related to the vehicle's driving scenarios, and the original testing methods that purely tested the light distribution performance of the lights cannot verify the traffic environment adaptability of the ADB function.

[0004] Existing headlight component and whole-vehicle darkroom testing methods mainly test the light distribution performance of headlights under static vehicle conditions, lacking functional and performance testing under dynamic vehicle application scenarios. Some ADB headlight proving ground road testing methods use high-precision positioning, illuminance sensors, and other equipment to verify the functionality and performance of the whole vehicle under dynamic conditions in professional test sites. However, due to limitations in test sites (road type, road length, flatness, etc.), scenario equipment simulation (background vehicles, bicycles, etc.), and environment (generally with strict requirements for climate, lighting, and other parameters), the test scenarios that can be built are very limited. Only a small number of typical scenario verifications can be performed, and the testing costs are expensive, which cannot cover a large number of real-world user scenarios. Summary of the Invention

[0005] The purpose of this invention is to provide a real-vehicle open road testing method for ADB intelligent headlights, which solves the problem that the existing technology has very limited test scenarios, can only perform verification of a small number of typical scenarios, and is expensive to test, and cannot cover a large number of real-world user scenarios.

[0006] To achieve the above objectives, a method for real-vehicle open road testing of ADB intelligent headlights is provided, including the following steps:

[0007] A second test data acquisition device for collecting road environment data is installed on the test vehicle. The second test data acquisition device includes a radar, a high-definition camera, a high-definition industrial camera, and a luminance imager installed on the exterior of the test vehicle. The radar is used to collect motion parameters of traffic participants in front of the test vehicle, including speed and position. The high-definition camera is used to collect video image information of the surroundings of the test vehicle. The high-definition industrial camera is used to capture information on the brightness changes of the headlights in front of the test vehicle. The luminance imager is used to capture a photograph of the light intensity distribution in front of the test vehicle when a trigger signal is received.

[0008] A first test data acquisition device is installed on the test vehicle to collect data about the vehicle itself. The first test data acquisition device includes a communication module, a camera, a positioning inertial navigation module, and a trigger module. The communication module collects vehicle CAN network data and obtains vehicle status information, including ADB working status, vehicle gear position, and steering status information. The camera is used to capture the ADB status displayed on the vehicle's internal instrument panel. The positioning inertial navigation module is used to acquire motion status data such as the position, speed, and acceleration of the test vehicle. The trigger is used to generate a trigger signal to trigger the brightness imager to take pictures.

[0009] Road test data collection steps: Collect first test data corresponding to the test vehicle's own data and second test data corresponding to the road environment data during the test vehicle's driving process;

[0010] Road test data analysis steps: Based on the collected first and second test data, analyze and determine the trigger time of the trigger signal, and record the vehicle status information in the first test data and the environmental status information in the second test data during the trigger time period, and then store them separately; compare and analyze the triggering situation of the ADB function; after all trigger signals have been analyzed, summarize and generate a test report.

[0011] Furthermore, the test report includes the accuracy, missed trigger rate, and false trigger rate of ADB function activation; the rationality of the trigger and deactivation times of ADB function; and vehicle and environmental status information when ADB function malfunctions.

[0012] Furthermore, the radar includes millimeter-wave radar or lidar.

[0013] Furthermore, the communication module collects vehicle status information via CAN bus and Ethernet.

[0014] Principles and advantages:

[0015] This solution addresses the lack of open road testing methods for ADB headlights. During open road testing, it simultaneously records a large amount of vehicle and traffic participant motion data, as well as traffic environment data, when the ADB function is triggered or missed. This allows product development engineers to identify the causes of various problems during testing through data playback. Based on the data analysis of ADB function triggering, missed triggering, and false triggering during testing, an overall evaluation of the ADB headlights can be achieved. This method is conducted on real-world roads, offering advantages over test track testing, such as lower cost, higher efficiency, and a wider range of testing scenarios. Attached Figure Description

[0016] Figure 1 This is a diagram illustrating the overall architecture of an ADB intelligent headlight real-vehicle open road testing method according to an embodiment of the present invention.

[0017] Figure 2 This is a logical block diagram of the functional modules of the road testing system.

[0018] Figure 3 A flowchart for road testing;

[0019] Figure 4 A flowchart for road test data analysis;

[0020] Figure 5 This is a schematic diagram of a road scenario for testing ADB headlights. Detailed Implementation

[0021] The following detailed description illustrates the specific implementation method:

[0022] Example

[0023] A method for real-vehicle open road testing of ADB intelligent headlights, basically as follows: Figures 1-5 As shown, it includes the following steps:

[0024] The road testing system is constructed, including installing a second test data acquisition device on the test vehicle to collect road environment data and a first test data acquisition device on the test vehicle to collect data of the test vehicle itself.

[0025] The second test data acquisition equipment includes radar, high-definition camera, high-definition industrial camera and luminance imager mounted on the exterior of the test vehicle; the radar includes millimeter-wave radar or lidar.

[0026] The radar is used to collect motion parameters of traffic participants in front of the test vehicle, including speed and position; the high-definition camera is used to collect video image information around the test vehicle; the high-definition industrial camera is used to capture brightness change information of the headlights in front of the test vehicle and record whether the ADB headlight function is activated; the luminance imager is used to capture a photo of the light intensity distribution in front of the test vehicle when a trigger signal is received; in this embodiment, the luminance imager photo capture function adopts an event-triggered method, which can be automatically triggered by the ADB working status CAN signal or manually triggered by a button to capture a photo of the light intensity distribution in front of the test vehicle.

[0027] The first test data acquisition device includes a communication module, a camera, a positioning inertial navigation module, and a trigger module; the communication module acquires vehicle status information via CAN bus and Ethernet.

[0028] The communication module collects vehicle CAN network data to obtain vehicle ADB working status, gear position, steering, and other status information; the camera is used to capture the ADB status displayed on the vehicle's interior instrument panel; the positioning inertial navigation module is used to acquire motion status data such as the test vehicle's position, speed, and acceleration; the trigger is used to generate a trigger signal to trigger the brightness imager to take pictures; (the on or off status of the ADB lights has a corresponding CAN message signal, for example, a signal value of 1 indicates that the ADB lights are on, and a signal value of 0 indicates that the ADB lights are off. The specific definition of this signal value is defined by each manufacturer).

[0029] Road test data collection steps: Collect first test data corresponding to the test vehicle's own data during the test vehicle's driving process and second test data corresponding to the road environment data; the collected and analyzed data information includes: vehicle network information, motion information of traffic participants (vehicles, two-wheeled vehicles and pedestrians related speeds, distances, etc.) in front of and behind the test vehicle, and road environment information (curves, rain, brightness distribution, etc.).

[0030] Road test data analysis steps: Based on the collected first and second test data, analyze and determine the trigger time of the trigger signal, and record the vehicle status information in the first test data and the environmental status information in the second test data during the trigger time period, and then store them separately; compare and analyze the triggering situation of the ADB function; after all trigger signals have been analyzed, summarize and generate a test report. The test report includes the accuracy, missed trigger rate, and false trigger rate of the ADB function activation; the rationality of the ADB function triggering and deactivation times (rationality judgment should be based on enterprise standards. For example, some enterprise standards require that when the distance to the oncoming vehicle is ≥120m, the ADB should turn off the high beams of the area illuminated by the oncoming vehicle. If, during testing, it is found that the ADB only turns off part of the high beam area when the distance to the oncoming vehicle is 100m, this is considered unreasonable); the rationality of the illumination area controlled by the light group after the ADB function is triggered (it is possible that too much or too little illumination area is turned off when there is an oncoming vehicle. For example, when there is an oncoming vehicle at a distance, the ADB only needs to turn off one light group, but if the ADB turns off all the light groups of the left headlight, resulting in the left high beam being completely turned off, this is an unreasonable situation); and vehicle status information and environmental status information when the ADB function is abnormal. For scenarios where the ADB function is abnormal during the analysis process, all data under the relevant triggering conditions are delivered to the product development engineers. The development engineers optimize the ADB algorithm and adjust various parameters based on the data before and after the ADB trigger.

[0031] The ADB intelligent headlights are primarily used at night in road environments with poor roadside lighting. When oncoming vehicles are present or when following another vehicle, the ADB headlight system will shut off some of the headlight modules. For road selection, priority will be given to conducting trials on poorly lit roads in mountainous areas and urban and rural areas.

[0032] After completing the open road test, the test vehicle data, traffic participant motion status data, and test road environment data are copied to the data analysis computer, and the test data are analyzed using road test data analysis software.

[0033] The trigger is used to generate a signal that initiates a photo taken by the brightness imager; or to play back and analyze approximately 20 seconds of road test data recorded by a manual button trigger. This allows for simultaneous playback of multiple data streams to analyze and determine whether the ADB function triggering is correct and whether there are any missed or false triggers. Based on various data related to false and missed trigger scenarios, product parameters are optimized for specific problem scenarios. For correctly triggered events, further analysis using photos of light intensity distribution is used to determine the rationality of parameters such as the ADB control area for the light group's switching, function trigger sensitivity, and trigger distance.

[0034] This solution addresses the lack of open road testing methods for ADB headlights. During open road testing, it simultaneously records a large amount of vehicle and traffic participant motion data, as well as traffic environment data, when the ADB function is triggered or missed. This allows product development engineers to identify the causes of various problems during testing through data playback. Based on the data analysis of ADB function triggering, missed triggering, and false triggering during testing, an overall evaluation of the ADB headlights can be achieved. This method is conducted on real-world roads, offering advantages over test track testing, such as lower cost, higher efficiency, and a wider range of testing scenarios.

[0035] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, based on the guidance provided in this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for open road testing of ADB intelligent headlights on real vehicles, characterized in that, Includes the following steps: A second test data acquisition device for collecting road environment data was installed on the test vehicle; The second test data acquisition equipment includes a radar, a high-definition camera, a high-definition industrial camera, and a brightness imager installed on the exterior of the test vehicle. The radar is a millimeter-wave radar. The millimeter-wave radar is used to collect motion parameters of traffic participants in front of the test vehicle, including speed and position. High-definition cameras are used to collect video images of the area surrounding the test vehicle; High-definition industrial cameras are used to capture information on the brightness changes of the headlights on test vehicles; A luminance imager is used to take a picture of the luminance distribution in front of a test vehicle when a trigger signal is received; A first test data acquisition device is installed on the test vehicle to collect data about the vehicle itself. The first test data acquisition device includes a communication module, a camera, a positioning inertial navigation module, and a trigger module. The communication module collects vehicle CAN network data and obtains vehicle status information, including ADB working status, vehicle gear position, and steering status information. The camera is used to capture the ADB status displayed on the vehicle's internal instrument panel. The positioning inertial navigation module is used to obtain the position, speed, and acceleration of the test vehicle. The trigger module is used to generate a trigger signal to trigger the brightness imager to take pictures. Road test data collection steps: Conduct tests on real-world open mountainous and urban roads, collect first test data corresponding to the test vehicle's own data during the test vehicle's driving process and second test data corresponding to the road environment data. The collected data includes on-board network information, motion information of traffic participants in front of and behind the test vehicle, and road environment information. Road test data analysis steps: Based on the collected first and second test data, analyze and determine the trigger time of the trigger signal, and record the vehicle status information in the first test data and the environmental status information in the second test data during the trigger time period, and then store them separately; compare and analyze the triggering of the ADB function, and analyze whether the ADB function triggering is correct and whether there are any missed or false triggers through multi-channel data synchronous playback; after all trigger signals have been analyzed, a test report is generated, which includes the accuracy rate of ADB function activation, missed trigger rate and false trigger rate, the rationality of the ADB function triggering time and closing time, and the vehicle status information and environmental status information when the ADB function is abnormal; for ADB function abnormal scenarios, optimize the ADB algorithm and parameters based on the data before and after triggering.

2. The method for real-vehicle open road testing of ADB intelligent headlights according to claim 1, characterized in that: The millimeter-wave radar has been replaced with a lidar.

3. The method for conducting open road testing of ADB intelligent headlights on a real vehicle according to claim 2, characterized in that: The communication module collects vehicle status information via CAN bus and Ethernet.

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