An Optimization-based Time-optimal Velocity Planning for Autonomous Driving

Abstract

Velocity planning plays an important role in motion planning of automated driving as it must meet safety, comfort, and traffic regulation requirements. Therefore, it is necessary to consider Jerk constraint and dynamic obstacle constraint. However, the introduction of these constraints makes velocity planning a non-convex optimization problem, significantly increasing computational complexity. To address these challenges, this paper investigates an optimization-based timeoptimal velocity planning method. The non-convex and non-linear problems caused by Jerk constraint and dynamic obstacle constraint are addressed by realizing constraint linearization through velocity filtering with acceleration as the threshold. The linear programming (LP) method is then used twice to calculate a time-optimal velocity profile that satisfies the given constraints. Furthermore, when hard constraints are unable to satisfy obstacle avoidance planning, a dynamic constraint frame strategy is proposed to relax the hard constraints and fully utilize the dynamic performance of the ego-vehicle to avoid obstacles. Finally, simulations are conducted in various driving scenarios to validate the effectiveness of the proposed approach. The simulation results demonstrate that the approach proposed in this paper can quickly generate velocity profiles that meet safety and comfort constraints, within a short planning period. Additionally, the dynamic constraint frame strategy can improve the dynamic adaptability of the algorithm.