Predictable Trajectory Planner in Time-domain and Hierarchical Motion Controller for Intelligent Vehicles in Structured Road

As basic modules of intelligent vehicle, path planning and its tracking have been developed rapidly. However, the trajectory generated by the traditional path-speed decoupled planning method is not feasible in the time domain. In this paper, a method based on improved RRT is proposed to achieve efficient planning and smoothing. In order to realize the matching of track points in space and time domain, closed-loop prediction is adopted to know the actually tracked path more accurately. Taking the nonlinear characteristics of lateral and longitudinal dynamics of the vehicle and the saturation of actuators into account, a unified conditional integral control law is designed to guarantee the global asymptotic stability of the tracking error and to avoid the degradation of actuators performance due to the divergence of the integral operation. Simulations and experiments prove that the proposed planning method is more efficient, the control algorithm can effectively track the planned trajectory, and the prediction method can accurately predict the actual driving trajectory, which is very important for collision detection.