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1.INTRODUCTIONAutomotive safety is a constant theme in the course of the history of automotive development, and passive safety, pedestrian protection and active safety have all received widespread attention from the automotive community. Passive safety systems are systems that provide protection for vehicle occupants as well as pedestrians in the event of a traffic accident, such as a collision, to avoid secondary contact with the body’s interior components and minimize injury to people [1]. However, the injury reduction through freezing safety is ultimately limited, and with the rapid development of the automotive industry and the increasing speed of design vehicles, it is difficult to rely on passive safety alone to solve the safety problems of automobiles. In this context, active safety research application was born, according to statistics, the vast majority of traffic accidents in China are caused by improper driver operation [2]. The purpose of the vehicle automatic emergency braking system (Autonomous Emergency Braking, AEB) is to avoid the vehicle in the process of driving collision or reduce collision damage, and now with the rapid progress of sensor technology and the continuous optimization of various algorithms, the vehicle automatic emergency automatic system more and more mature, in the car configuration rate is also increasingly high [3,4].According to relevant reports, if a vehicle is equipped with an AEB system, the probability of rear-end collisions can be reduced by 27% [5]. In the active safety section of C-NCAP version 2021 [6], new test scenarios for longitudinal target objects, including longitudinal pedestrian and longitudinal bicycle scenarios, have been added, which have attracted the key attention of vehicle manufacturers, sensor suppliers and testing agencies. This paper proposes a longitudinal target object test method based on a dual-band arrangement, which is of reference value to various organizations conducting AEB-related tests. 2.PRINCIPLE OF OPERATION OF AUTOMATIC EMERGENCY BRAKING SYSTEMSThe automatic emergency braking system is an L0-level function of autonomous driving, which uses cameras, radar and other sensors to identify obstacles in the vehicle’s direction of travel, including but not limited to pedestrians and vehicles, and to alert the driver and control the vehicle’s braking system in an emergency to avoid or mitigate the consequences of a collision. The working principle of the automatic emergency braking system is shown in Figure 1. The accurate decision of the automatic emergency braking system depends on the detection result of the sensor to the target in front, and the information of the target detected in front is fed back to the decision layer, and the decision layer processes the information and executes the corresponding action[7]. Compared with a single sensor, the multi-sensor fusion automatic emergency braking system can give full play to the advantages of each sensor, obtain more accurate information, and make accurate decisions[8]. At present, there are common ways of laser radar and camera fusion, millimeter wave radar and camera fusion[9,10]. 3.LONGITUDINAL TEST SCENARIO FOR AUTOMATIC EMERGENCY BRAKING SYSTEMS3.1Automatic emergency braking system Target traction systemThe main targets selected for testing of automatic emergency braking systems include 3D soft-target passenger vehicles, pedestrians and two-wheeled vehicles (including bicycles and scooters). 3D soft-target passenger vehicles are mostly attached to a movable platform, which controls the target passenger vehicle for following braking and slowing actions. Pedestrians and two-wheeled vehicles can be attached to the small mobile flatbed for movement, or towed by means of a tow strap. The small mobile platform is easy to install and operate, but it is difficult to grasp the precise position of the collision point because the positioning accuracy of the equipment is prone to change during movement and the heading angle adjustment distance is too long. The traction band towing method is a mature solution in the horizontal pedestrian and two-wheeled vehicle test scenarios, but it is tedious to recover and build the scenarios. In the vertical pedestrian test, although the single-band towing system can travel a set distance at the required speed, it is affected by the road surface and the diverging direction, causing the target to deviate from the intended travel route, and each scenario has a long recovery period, which seriously affects the test efficiency and the test results validity of the test results. The connection of the single-band traction is shown schematically in Figure 2. The use of a dual-band arrangement for longitudinal test scenes effectively enables the target to travel along the desired trajectory of movement, recovering the scene conveniently without collision and with less influence from wind speed and ground unevenness. The dual-band traction connection is shown schematically in Figure 3. 3.2Longitudinal test scenario for automatic emergency braking systemsThe targets chosen for the test were the target pedestrians and two-wheeled vehicles as specified in the CNCAP Administrative Rules (2021 Edition) to determine the performance of the dual-band towing approach with different targets. The speed requirement for pedestrians in the Car-to-Pedestrian Longitudinal Adult (CPLA) scenario is 5km/h and the speeds of the test vehicles are 20km/h, 30km/h, 40km/h, 50km/h and 60km/h. In this paper, only the speed point of 30km/h was chosen for the test vehicle. scenario. The scenario is shown in Figure 4. In the figure, A-A is the test vehicle travel route, B-B is the pedestrian travel route, G is the pedestrian acceleration distance, H is the pedestrian uniform distance, and C is the expected collision point between the test vehicle and the pedestrian. The speed of the bicycle in the CBLA (Car-to-Bicyclist Longitudinal Adult) scenario is 15km/h and the speed of the test vehicle is 30km/h, 40km/h, 50km/h and 60km/h. In this paper, only the speed point of the test vehicle is 30km/h is chosen to build the test scenario. The test scenario is shown in Figure 2-4. The scenario is shown in Figure 5. In the figure, A-A is the driving route of the test vehicle, B-B is the driving route of the bicycle, G is the acceleration distance of the bicycle, H is the distance of the bicycle at constant speed, and C is the expected collision point between the test vehicle and the bicycle. 4.LONGITUDINAL TESTING TECHNOLOGY OF AUTOMATIC EMERGENCY BRAKING SYSTEM4.1Main test equipmentThe main roommate instrumentation for the longitudinal test of the automatic emergency braking system includes an automatic driving robot and an inertial GPS combined test system. The automatic driving robot is mainly used for the lateral and longitudinal control of the vehicle, which is used to ensure the speed and path of the vehicle test, while the automatic driving robot is used to pull the target carrying platform to move and control the speed of the moving platform. The inertial GPS combined test system mainly feeds the position information and speed information of the vehicle and pedestrian to the respective connected autopilot robot, which is used to judge whether the acceleration, speed and relative position during the test meet the test requirements and pass the requirements. 4.2Longitudinal test method validationThe test selected a certain type of vehicle as the test vehicle, and the speed of the test vehicle was set to 30km/h to test the automatic emergency braking system of CPLA and CBLA.
5.EXPERIMENTS RESULT5.1Vehicle-to-pedestrian longitudinal scenario test resultsThe vehicle-to-pedestrian longitudinal AEB test scenario was conducted in accordance with 3.2, with the test vehicle travelling at a speed of 30km/h. The test result data is shown in Figure 7. In the figure, the black curve shows the speed of the test vehicle, which is maintained at 30 km/h by the driving robot; the green curve shows the speed of the pedestrian target, which is maintained at 5 km/h after acceleration by the robot; the red curve shows the lateral deviation of the test vehicle in the field coordinate system, which is not significantly different from the desired trajectory; the purple curve shows the relative longitudinal distance between the test vehicle and the desired collision point. At the beginning of the test, the relative longitudinal distance between the test vehicle and the expected collision point and the pedestrian target object showed a linear change because the pedestrian target object did not reach the motion trigger condition; when the traction system received the trigger signal, the slope of the blue curve had a significant change. After the test vehicle’s AEB function intervenes, the slope of the blue curve will change again. When the test vehicle is completely braked and prohibited or at a speed lower than the pedestrian’s speed, the test ends and the blue curve will produce an obvious inflection point, and if the pedestrian keeps moving, the blue curve will have a tendency to increase. 5.2Car-to-bike longitudinal scenario test resultsThe vehicle-to-pedestrian longitudinal AEB test scenario was carried out in accordance with 3.2, with the test vehicle travelling at 30km/h. The test results are shown in Figure 8. In the figure, the black curve shows the speed of the test vehicle, which is maintained at 30 km/h by the driving robot; the green curve shows the speed of the bicycle target, which is maintained at 15 km/h after acceleration by the robot; the red curve shows the lateral deviation of the test vehicle in the field coordinate system, which is not significantly different from the desired trajectory; the purple curve shows the relative longitudinal distance between the test vehicle and the desired collision point. At the beginning of the test, the relative longitudinal distance between the test vehicle and the expected collision point and the bicycle target showed a linear change because the bicycle target had not reached the motion trigger condition; when the traction system received the trigger signal, the slope of the blue curve had a significant change. The slope of the blue curve changes again after the test vehicle’s AEB function intervenes. The test ends when the test vehicle is completely braked and prohibited or at a speed no higher than the pedestrian’s speed, and the blue curve produces a clear inflection point, which tends to increase if the bicycle keeps moving. The method has been verified to be able to obtain the relative position of the vehicle as well as accurately collecting the alarm signal, and can meet the requirements of most vehicle blind zone monitoring regulations. 6.CONCLUSIONSThis paper proposes an active safety test method for longitudinal objects based on dual-band arrangement, and sorts out the test process and test steps. By constructing longitudinal test scenes for pedestrians and bicycles, and conducting real vehicle tests, the effectiveness and feasibility of this method are verified. At the same time, this test method provides a reference idea for the vehicle testing industry in the relevant regulations testing, provides a reference direction for the research and development of testing equipment, and has obvious practical application value. REFERENCESLu Kai,
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