基于整车在环仿真的自动驾驶汽车室内快速测试平台
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摘要
整车在环仿真测试方法可以安全、高效地验证复杂环境和极端工况等场景下自动驾驶汽车性能的有效性,基于此研发一种基于整车在环仿真的自动驾驶汽车室内快速测试平台,该平台由前轴可旋转式转鼓试验台、试验台测控子系统、虚拟场景自动生成子系统、虚拟传感器模拟子系统、驾驶模拟器、自动驾驶汽车和测试结果自动分析评价子系统组成。通过在试验台滚筒上独立加载转矩模拟车辆行驶阻力,可动态模拟不同的路面附着系数,同时利用坡度、侧倾和转向随动机构可模拟车辆俯仰角、侧倾角和航向角3个自由度;采用虚拟现实技术柔性集成车辆动力学模型、传感器仿真、复杂道路交通环境及测试用例仿真,模拟多种道路交通场景,并通过传感器仿真及数据融合等技术快速测试自动驾驶汽车智能感知与行为决策等性能指标。将自动驾驶汽车、虚拟仿真场景和试验台耦合构建一个闭环系统,完成了多项关键技术研发,包括:多自由度高动态试验台结构设计、虚拟测试场景自动重构方法和传感器数据模拟及注入方法,可满足在各种场景下测试自动驾驶汽车整车性能的需求。此外,为验证快速测试平台的有效性,以U-turn轨迹跟踪控制为研究实例,基于简化的车辆运动学模型和模型预测控制算法,在平台上搭建U-turn场景并对自动驾驶汽车的轨迹跟踪控制算法性能进行大量测试。结果表明:自动驾驶汽车室内快速测试平台可以真实地模拟汽车在道路上的运行工况,自动驾驶汽车在虚拟场景中的轨迹跟踪效果良好,与参考轨迹的偏差小于8%,证明了该测试平台检测方法的有效性。
The vehicle-in-the-loop simulation test method can validate the performance of autonomous vehicles safely and efficiently in complex environments and extreme conditions. This paper develops an indoor rapid-testing platform for autonomous vehicles based on vehicle-in-the-loop. This platform is comprised of seven subsystems: front-axle rotatable drum bench, automobile test bench detection system, virtual scenario automatic generation subsystem, virtual sensor simulation subsystem, driving simulators, autonomous vehicle, and result analyzing and evaluating automatically subsystem. The driving resistance was simulated by independently loading torque on the rollers supporting the four wheels of the automobile, so that different road adhesion coefficients were simulated. Slope, roll, and yaw follower mechanisms were used to simulate three degrees-of-freedom such as the pitch angle, roll angle, and course angle. Virtual reality technology was adopted to simulate various road traffic scenarios to verify performance indicators, such as intelligent perception and decision-making behavior of the autonomous vehicle, by means of flexible integration of the vehicle dynamics model, sensor simulators, simulations of complex traffic environments, and test cases. A closed loop system was modeled by coupling the automobile, the virtual simulation scenarios and the test bench in order to research and develop a number of key technologies, including the structural design of multi degrees-of-freedom and high-dynamic detection bench, automatic reconstruction method of virtual test scenes, and methods of simulation and injection for sensor data, which can meet the need to test the performance of autonomous vehicles in various scenarios. Further, to verify the effectiveness of the test platform, U-turn trajectory tracking control was taken as a research example. Based on a simplified vehicle kinematics model and the Model Predictive Control algorithm, a large amount of experiments was performed in virtual scenarios of U-turns to test the efficiency of the trajectory tracking control algorithm on the autonomous vehicle. The results showed that the platform could simulate automobile driving conditions on the road realistically, and the trajectory tracking effect of the autonomous vehicle in the virtual scenario is satisfactory. The deviation from the predicted trajectory is less than 8%, which demonstrates the effectiveness of the test platform.
The vehicle-in-the-loop simulation test method can validate the performance of autonomous vehicles safely and efficiently in complex environments and extreme conditions. This paper develops an indoor rapid-testing platform for autonomous vehicles based on vehicle-in-the-loop. This platform is comprised of seven subsystems: front-axle rotatable drum bench, automobile test bench detection system, virtual scenario automatic generation subsystem, virtual sensor simulation subsystem, driving simulators, autonomous vehicle, and result analyzing and evaluating automatically subsystem. The driving resistance was simulated by independently loading torque on the rollers supporting the four wheels of the automobile, so that different road adhesion coefficients were simulated. Slope, roll, and yaw follower mechanisms were used to simulate three degrees-of-freedom such as the pitch angle, roll angle, and course angle. Virtual reality technology was adopted to simulate various road traffic scenarios to verify performance indicators, such as intelligent perception and decision-making behavior of the autonomous vehicle, by means of flexible integration of the vehicle dynamics model, sensor simulators, simulations of complex traffic environments, and test cases. A closed loop system was modeled by coupling the automobile, the virtual simulation scenarios and the test bench in order to research and develop a number of key technologies, including the structural design of multi degrees-of-freedom and high-dynamic detection bench, automatic reconstruction method of virtual test scenes, and methods of simulation and injection for sensor data, which can meet the need to test the performance of autonomous vehicles in various scenarios. Further, to verify the effectiveness of the test platform, U-turn trajectory tracking control was taken as a research example. Based on a simplified vehicle kinematics model and the Model Predictive Control algorithm, a large amount of experiments was performed in virtual scenarios of U-turns to test the efficiency of the trajectory tracking control algorithm on the autonomous vehicle. The results showed that the platform could simulate automobile driving conditions on the road realistically, and the trajectory tracking effect of the autonomous vehicle in the virtual scenario is satisfactory. The deviation from the predicted trajectory is less than 8%, which demonstrates the effectiveness of the test platform.
引文
[1] 李克强,戴一凡,李升波,等.智能网联汽车(ICV)技术的发展现状及趋势[J].汽车安全与节能学报,2017,8(1):1-14.LI Ke-qiang,DAI Yi-fan,LI Sheng-bo,et al.State-of-the-art and Technical Trends of Intelligent and Connected Vehicles [J].Journal of Automotive Safety and Energy,2017,8 (1):1-14.
[2] GORDO N,LIDBERG M.Automated Driving and Autonomous Functions on Road Vehicles [J].Vehicle System Dynamics,2015,53 (7):958-994.
[3] MOLINA C B S T,DE ALMEIDA J R,VISMARI L F,et al.Assuring Fully Autonomous Vehicles Safety by Design:The Autonomous Vehicle Control (AVC) Module Strategy [C] // IEEE.2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W).New York:IEEE,2017:16-21.
[4] LI L,HUANG W L,LIU Y,et al.Intelligence Testing for Autonomous Vehicles:A New Approach [J].IEEE Transactions on Intelligent Vehicles,2017,1 (2):158-166.
[5] FEILHAUER M,HAERING J,WYATT S,et al.Current Approaches in HIL-based ADAS Testing [J].SAE International Journal of Commercial Vehicles,2016,9 (2):63-69.
[6] GUO J,DENG H,ZHANG S,et al.A Novel Method of Radar Modeling for Vehicle Intelligence [J].SAE International Journal of Passenger Cars - Electronic and Electrical Systems,2016,10 (1):50-56.
[7] BROGGI A,BUZZONI M,DEBATTISTI S,et al.Extensive Tests of Autonomous Driving Technologies [J].IEEE Transactions on Intelligent Transportation Systems,2013,14 (3):1403-1415.
[8] HUANG W L,WANG K,LV Y,et al.Autonomous Vehicles Testing Methods Review [C] // IEEE.Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).New York:IEEE,2016:163-168.
[9] GELBAL ? Y,TAMILARASAN S,CANTAS M R,et al.A Connected and Autonomous Vehicle Hardware-in-the-loop Simulator for Developing Automated Driving Algorithms [C] // IEEE.Proceedings of the 2017 IEEE International Conference on Systems,Man,and Cybernetics (SMC).New York:IEEE,2017:3397-3402.
[10] BHADANI R K,SPRINKLE J,BUNTING M.The CAT Vehicle Testbed:A Simulator with Hardware in the Loop for Autonomous Vehicle Applications [EB/OL].[2019-01-28].https://arxiv.org/pdf/1804.04347.pdf.
[11] DENG W,LEE Y H,ZHAO A.Hardware-in-the-loop Simulation for Autonomous Driving [C] // IEEE.Proceedings of the 2008 34th Annual Conference of IEEE Industrial Electronics.New York:IEEE,2008:1742-1747.
[12] MA J,ZHOU F,HUANG Z,et al.Hardware-in-the-loop Testing of Connected and Automated Vehicle Applications:A Use Case for Queue-aware Signalized Intersection Approach and Departure [J].Transportation Research Record,2018 (2672):36-46.
[13] CHEN Y,CHEN S,ZHANG T,et al.Autonomous Vehicle Testing and Validation Platform:Integrated Simulation System with Hardware in the Loop [C] // IEEE.Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV).New York:IEEE,2018:949-956.
[14] 郝茹茹,赵祥模,周洲.整车防抱死制动系统台架检测与道路对比试验[J].农业机械学报,2013,44(4):21-26.HAO Ru-ru,ZHAO Xiang-mo,ZHOU zhou.Bench Detection and Road Comparison Test for Auto Anti-lock Braking System [J].Transactions of the Chinese Society for Agricultural Machinery,2013,44 (4):21-26.
[15] 郝茹茹,赵祥模,马建,等.一种新型汽车ABS整车检测系统[J].交通运输工程学报,2011,11(5):69-75.HAO Ru-ru,ZHAO Xiang-mo,MA-Jian,et al.A Novel Detection System of Automobile ABS [J].Journal of Traffic and Transportation Engineering,2011,11 (5):69-75.
[16] 郝茹茹.汽车ABS整车台架检测方法与试验研究[D].西安:长安大学,2013.HAO Ru-ru.Bench Detection Approach and Experimental Study for Auto Anti-lock Braking System [D].Xi’an:Chang’an University,2013.
[17] ZHANG C,LIU Y,ZHAO D,et al.RoadView:A Traffic Scene Simulator for Autonomous Vehicle Simulation Testing [C] // IEEE.Proceeding of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).New York:IEEE,2014:1160-1165.
[18] LI L,WANG X,WANG K,et al.Parallel Testing of Vehicle Intelligence Via Virtual-real Interaction [J].Science Robotics,2019,4 (28):4106-4116.
[19] PUNZO V,CIUFFO B.Integration of Driving and Traffic Simulation:Issues and First Solutions [J].IEEE Transactions on Intelligent Transportation Systems,2011,12 (2):354-363.
[20] ZHAO D,LAM H,PENG H,et al.Accelerated Evaluation of Automated Vehicles Safety in Lane-change Scenarios Based on Importance Sampling Techniques [J].IEEE Transactions on Intelligent Transportation Systems,2017,18 (3):595-607.
[21] ZHAO D,HUANG X,PENG H,et al.Accelerated Evaluation of Automated Vehicles in Car-following Maneuvers [J].IEEE Transactions on Intelligent Transportation Systems,2018,19 (3):733-744.
[22] KARL I,BERG G,RUGER F,et al.Driving Behavior and Simulator Sickness while Driving the Vehicle in the Loop:Validation of Longitudinal Driving Behavior [J].IEEE Intelligent Transportation Systems Magazine,2013,5 (1):42-57.
[23] GIETELINK O J,PLOEG J,SCHUTTER B D,et al.Development of a Driver Information and Warning System with Vehicle Hardware-in-the-loop Simulations [J].Mechatronics,2009,19 (7):1091-1104.
[24] YANG S,LI M,LIN Y,et al.Electric Vehicle’s Electricity Consumption on a Road with Different Slope [J].Physica A:Statistical Mechanics and Its Applications,2014,402 (1):41-48.
[25] 闫晓雷,邵毅明,曾俊延.基于相似度数据融合的车辆航向角研究[J].汽车工程学报,2018,8(3):212-217.YAN Xiao-lei,SHAO Yi-ming,ZENG Jun-yan.Vehicle Heading Angle Research Based on Similarity Data Fusion [J].Chinese Journal of Automotive Engineering,2018,8 (3):212-217.
[26] 刘天洋,余卓平,熊璐,等.智能网联汽车试验场发展现状与建设建议[J].汽车技术,2017,2017(1):7-11.LIU Tian-yang,YU Zhuo-ping,XIONG Lu,et al.Current Development Status and Construction Advice for Proving Ground of Intelligent and Connected Vehicles [J].Automobile Technology,2017 (1):7-11.
[27] ULBRICH S,MENZEL T,RESCHKA A,et al.Defining and Substantiating the Terms Scene,Situation,and Scenario for Automated Driving [C] // IEEE.2015 IEEE 18th International Conference on Intelligent Transportation Systems.New York:IEEE,2015:982-988.
[28] DE GELDER E,PAARDEKOOPER J P.Assessment of Automated Driving Systems Using Real-life Scenarios [C] // IEEE.2017 IEEE Intelligent Vehicles Symposium (IV).New York:IEEE,2017:589-594.
[29] ROCKLAGE E,KRAFT H,KARATAS A,et al.Automated Scenario Generation for Regression Testing of Autonomous Vehicles [C] // IEEE.2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).New York:IEEE,2017:476-483.
[30] XU Z,WANG M,ZHANG F,et al.PaTAVTT:A Hardware-in-the-loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking [J].Journal of Advanced Transportation,2017,2017:1-11.
[31] REN T,CHEN T,CHEN C.Motion Control for a Two-wheeled Vehicle Using a Self-tuning PID Controller [J].Control Engineering Practice,2008,16 (3):365-375.
[32] CHEN Y,WANG J.Adaptive Vehicle Speed Control with Input Injections for Longitudinal Motion Independent Road Frictional Condition Estimation [J].IEEE Transactions on Vehicular Technology,2011,60 (3):839-848.
[33] GRANCHAROVA A,GR?TLI E,HO D,et al.UAVs Trajectory Planning by Distributed MPC Under Radio Communication Path Loss Constraints [J].Journal of Intelligent & Robotic Systems,2015,79 (1):115-134.
[2] GORDO N,LIDBERG M.Automated Driving and Autonomous Functions on Road Vehicles [J].Vehicle System Dynamics,2015,53 (7):958-994.
[3] MOLINA C B S T,DE ALMEIDA J R,VISMARI L F,et al.Assuring Fully Autonomous Vehicles Safety by Design:The Autonomous Vehicle Control (AVC) Module Strategy [C] // IEEE.2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W).New York:IEEE,2017:16-21.
[4] LI L,HUANG W L,LIU Y,et al.Intelligence Testing for Autonomous Vehicles:A New Approach [J].IEEE Transactions on Intelligent Vehicles,2017,1 (2):158-166.
[5] FEILHAUER M,HAERING J,WYATT S,et al.Current Approaches in HIL-based ADAS Testing [J].SAE International Journal of Commercial Vehicles,2016,9 (2):63-69.
[6] GUO J,DENG H,ZHANG S,et al.A Novel Method of Radar Modeling for Vehicle Intelligence [J].SAE International Journal of Passenger Cars - Electronic and Electrical Systems,2016,10 (1):50-56.
[7] BROGGI A,BUZZONI M,DEBATTISTI S,et al.Extensive Tests of Autonomous Driving Technologies [J].IEEE Transactions on Intelligent Transportation Systems,2013,14 (3):1403-1415.
[8] HUANG W L,WANG K,LV Y,et al.Autonomous Vehicles Testing Methods Review [C] // IEEE.Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).New York:IEEE,2016:163-168.
[9] GELBAL ? Y,TAMILARASAN S,CANTAS M R,et al.A Connected and Autonomous Vehicle Hardware-in-the-loop Simulator for Developing Automated Driving Algorithms [C] // IEEE.Proceedings of the 2017 IEEE International Conference on Systems,Man,and Cybernetics (SMC).New York:IEEE,2017:3397-3402.
[10] BHADANI R K,SPRINKLE J,BUNTING M.The CAT Vehicle Testbed:A Simulator with Hardware in the Loop for Autonomous Vehicle Applications [EB/OL].[2019-01-28].https://arxiv.org/pdf/1804.04347.pdf.
[11] DENG W,LEE Y H,ZHAO A.Hardware-in-the-loop Simulation for Autonomous Driving [C] // IEEE.Proceedings of the 2008 34th Annual Conference of IEEE Industrial Electronics.New York:IEEE,2008:1742-1747.
[12] MA J,ZHOU F,HUANG Z,et al.Hardware-in-the-loop Testing of Connected and Automated Vehicle Applications:A Use Case for Queue-aware Signalized Intersection Approach and Departure [J].Transportation Research Record,2018 (2672):36-46.
[13] CHEN Y,CHEN S,ZHANG T,et al.Autonomous Vehicle Testing and Validation Platform:Integrated Simulation System with Hardware in the Loop [C] // IEEE.Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV).New York:IEEE,2018:949-956.
[14] 郝茹茹,赵祥模,周洲.整车防抱死制动系统台架检测与道路对比试验[J].农业机械学报,2013,44(4):21-26.HAO Ru-ru,ZHAO Xiang-mo,ZHOU zhou.Bench Detection and Road Comparison Test for Auto Anti-lock Braking System [J].Transactions of the Chinese Society for Agricultural Machinery,2013,44 (4):21-26.
[15] 郝茹茹,赵祥模,马建,等.一种新型汽车ABS整车检测系统[J].交通运输工程学报,2011,11(5):69-75.HAO Ru-ru,ZHAO Xiang-mo,MA-Jian,et al.A Novel Detection System of Automobile ABS [J].Journal of Traffic and Transportation Engineering,2011,11 (5):69-75.
[16] 郝茹茹.汽车ABS整车台架检测方法与试验研究[D].西安:长安大学,2013.HAO Ru-ru.Bench Detection Approach and Experimental Study for Auto Anti-lock Braking System [D].Xi’an:Chang’an University,2013.
[17] ZHANG C,LIU Y,ZHAO D,et al.RoadView:A Traffic Scene Simulator for Autonomous Vehicle Simulation Testing [C] // IEEE.Proceeding of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).New York:IEEE,2014:1160-1165.
[18] LI L,WANG X,WANG K,et al.Parallel Testing of Vehicle Intelligence Via Virtual-real Interaction [J].Science Robotics,2019,4 (28):4106-4116.
[19] PUNZO V,CIUFFO B.Integration of Driving and Traffic Simulation:Issues and First Solutions [J].IEEE Transactions on Intelligent Transportation Systems,2011,12 (2):354-363.
[20] ZHAO D,LAM H,PENG H,et al.Accelerated Evaluation of Automated Vehicles Safety in Lane-change Scenarios Based on Importance Sampling Techniques [J].IEEE Transactions on Intelligent Transportation Systems,2017,18 (3):595-607.
[21] ZHAO D,HUANG X,PENG H,et al.Accelerated Evaluation of Automated Vehicles in Car-following Maneuvers [J].IEEE Transactions on Intelligent Transportation Systems,2018,19 (3):733-744.
[22] KARL I,BERG G,RUGER F,et al.Driving Behavior and Simulator Sickness while Driving the Vehicle in the Loop:Validation of Longitudinal Driving Behavior [J].IEEE Intelligent Transportation Systems Magazine,2013,5 (1):42-57.
[23] GIETELINK O J,PLOEG J,SCHUTTER B D,et al.Development of a Driver Information and Warning System with Vehicle Hardware-in-the-loop Simulations [J].Mechatronics,2009,19 (7):1091-1104.
[24] YANG S,LI M,LIN Y,et al.Electric Vehicle’s Electricity Consumption on a Road with Different Slope [J].Physica A:Statistical Mechanics and Its Applications,2014,402 (1):41-48.
[25] 闫晓雷,邵毅明,曾俊延.基于相似度数据融合的车辆航向角研究[J].汽车工程学报,2018,8(3):212-217.YAN Xiao-lei,SHAO Yi-ming,ZENG Jun-yan.Vehicle Heading Angle Research Based on Similarity Data Fusion [J].Chinese Journal of Automotive Engineering,2018,8 (3):212-217.
[26] 刘天洋,余卓平,熊璐,等.智能网联汽车试验场发展现状与建设建议[J].汽车技术,2017,2017(1):7-11.LIU Tian-yang,YU Zhuo-ping,XIONG Lu,et al.Current Development Status and Construction Advice for Proving Ground of Intelligent and Connected Vehicles [J].Automobile Technology,2017 (1):7-11.
[27] ULBRICH S,MENZEL T,RESCHKA A,et al.Defining and Substantiating the Terms Scene,Situation,and Scenario for Automated Driving [C] // IEEE.2015 IEEE 18th International Conference on Intelligent Transportation Systems.New York:IEEE,2015:982-988.
[28] DE GELDER E,PAARDEKOOPER J P.Assessment of Automated Driving Systems Using Real-life Scenarios [C] // IEEE.2017 IEEE Intelligent Vehicles Symposium (IV).New York:IEEE,2017:589-594.
[29] ROCKLAGE E,KRAFT H,KARATAS A,et al.Automated Scenario Generation for Regression Testing of Autonomous Vehicles [C] // IEEE.2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).New York:IEEE,2017:476-483.
[30] XU Z,WANG M,ZHANG F,et al.PaTAVTT:A Hardware-in-the-loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking [J].Journal of Advanced Transportation,2017,2017:1-11.
[31] REN T,CHEN T,CHEN C.Motion Control for a Two-wheeled Vehicle Using a Self-tuning PID Controller [J].Control Engineering Practice,2008,16 (3):365-375.
[32] CHEN Y,WANG J.Adaptive Vehicle Speed Control with Input Injections for Longitudinal Motion Independent Road Frictional Condition Estimation [J].IEEE Transactions on Vehicular Technology,2011,60 (3):839-848.
[33] GRANCHAROVA A,GR?TLI E,HO D,et al.UAVs Trajectory Planning by Distributed MPC Under Radio Communication Path Loss Constraints [J].Journal of Intelligent & Robotic Systems,2015,79 (1):115-134.