基于整车在环仿真的自动驾驶汽车室内快速测试平台

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.