Parking Like a Human: A Direct Trajectory Planning Solution

Parking control problems remain to be fully solved for autonomous vehicles. Existing approaches usually first design a reference parking trajectory that does not exactly match vehicle dynamic constraints and then apply certain online negative feedback control to make the vehicle roughly track this reference trajectory. In this paper, we propose a novel trajectory planning method that directly links the actual parking trajectories and the steering actions to find the best parking trajectory. Tests show that this new approach has high reliability and less computation cost. Moreover, we also discuss how to counter with trajectory planning errors that are caused by model uncertainty in this paper. We show that an appropriate combination of feedforward trajectory planning and online feedback control can solve such problems.