On-road Trajectory Planning with Spatio-temporal RRT* and Always-feasible Quadratic Program

Abstract

On-road trajectory planning is a critical module in an autonomous driving system. Instead of using a path-velocity decomposition or longitudinal-lateral decomposition strategy, this work aims to find a trajectory directly. We adopt a sampleand-search planner to get a coarse trajectory and then polish it via numerical optimization. Among the predominant sampleand-search planners, most of the sampling operations are not flexible, which inevitably lead to a solution failure if the sampling density is low, and suffer from the curse of dimensionality if the sampling density is set high. This work proposes a modified RRT* for trajectory search, aiming to promote the sampling flexibility and to get rid of the search randomness. A quadratic program (QP) based smoother is proposed to refine the coarse trajectory. Herein, the scale of the QP problem is fixed and tractable, and the feasibility of the QP problem is always guaranteed.