Combined Longitudinal And Lateral Motion Control For Vehicular Autonomous Driving System

As a major component of Intelligent Transportation Systems (ITS), Intelligent Vehicle (IV) utilizes environmental perception, information fusion and intelligent control technologies to realize the automation of driving tasks. The intelligent vehicle autonomous driving system offers the great potential for significant enhancements in active safety and traffic capability.Aiming at the motion control and environmental perception problems of the IV's autonomous driving system, a relatively complete vehicle dynamic model is developed and a combined longitudinal and lateral motion control system is designed in this dissertation. The performances of vehicular autonomous driving system are verified in different driving conditions by offline simulation and hardware-in-the-loop technology. In addition, the Interacting Multiple Model algorithm is introduced to track the leading vehicle on the road. The dissertation is organized as follows:①After analyzing vehicle's translational and rotational behaviors in real driving conditions, a vehicle dynamic model with ten degree-of-freedom is developed based on three-dimensional rigid body dynamics and kinematics. A vehicular powertrain model that includes engine, torque converter, automatic transmission and final drive is developed, and the nonlinear characteristics of major components are analyzed simultaneously in this model. Considering the demand of tire forces for the intelligent vehicle autonomous driving system, this dissertation sets up a TMeasy tire model which can reflect the nonlinear force characteristics in saturation condition, and analyzes the coupling effects between longitudinal and lateral tire forces.②In order to realize the autonomous driving functions and improve stability of intelligent vehicle, fuzzy logic and sliding model control methods are used to design a combined longitudinal and lateral motion control system which has a hierarchical structure consisting of two layers. The system can control throttle angle, brake pressure and front wheel angle coordinately so as to make the intelligent vehicle achieve such autonomous driving functions as lane keeping, vehicle following, and lane changing. At the same time, it can improve vehicle's stability during autonomous driving process by adjusting active differential's distribution ratio of the driving torque so as to yield active yaw moment.③The performances of the combined longitudinal and lateral motion control system are examined in various driving conditions like velocity changing, road curvature changing and lane changing by off-line simulation, the stability of intelligent vehicle and motion errors in longitudinal and lateral directions are analyzed. At the same time, considering key factors such as lateral acceleration and lateral jerk in lane changing simulation experiment, a virtual desired trajectory is designed; this dissertation also validates the judgment basis of the active yaw moment control in coordinated control module.④As to the vehicle following control problem during autonomous driving process, this dissertation utilizes Interacting Multiple Model algorithm to track the leading vehicle with multiple states of motion. In this way, it can provide the Intelligent Vehicle with accurate and reliable information of leading vehicle. In simulation experiment, it uses uniform motion model with constant velocity and maneuvering motion model with acceleration to describe the motion states of the leading vehicle. In the meantime, a sensor model for such experiment is built and the initialization problem of maneuvering target model is studied in this dissertation.⑤In order to verify the effectiveness and robustness of intelligent vehicle autonomous driving system, a real-time visual autonomous driving experimental platform is developed, which is on the basis of hardware-in-the-loop and virtual reality technologies. On this platform, a series of hardware-in-the-loop simulation experiments are undertaken to test the autonomous driving functions in different driving conditions and the influence of human factors on vehicular autonomous driving system are considered. Besides, it analyzes the control system's robustness by adjusting the parameters of the vehicle model and the control system.