A Sampling-Based Local Trajectory Planner for Autonomous Driving along a Reference Path

In this paper, a state space sampling-based local trajectory generation framework for autonomous vehicles driving along a reference path is proposed. The presented framework employs a two-step motion planning architecture. In the first step, a Support Vector Machine based approach is developed to refine the reference path through maximizing the lateral distance to boundaries of the constructed corridor while ensuring curvature-continuity. In the second step, a set of terminal states are sampled aligned with the refined reference path. Then, to satisfy system constraints, a model predictive path generation method is utilized to generate multiple path candidates, which connect the current vehicle state with the sampling terminal states. Simultaneously the velocity profiles are assigned to guarantee safe and comfort driving motions. Finally, an optimal trajectory is selected based on a specified objective function via a discrete optimization scheme. The simulation results demonstrate the planner's capability to generate dynamically-feasible trajectories in real time and enable the vehicle to drive safely and smoothly along a rough reference path while avoiding static obstacles.