Optimal Trajectory Generation for Autonomous Vehicles Under Centripetal Acceleration Constraints for In-lane Driving Scenarios
This work addresses trajectory generation for autonomous vehicles in specific in-lane scenarios, representing an incremental improvement focused on handling centripetal acceleration constraints.
The paper tackles the problem of generating optimal trajectories for autonomous vehicles in in-lane driving scenarios, particularly on curvy roads, by developing a two-phase optimization method that produces jerk and time optimal trajectories, resulting in dynamically feasible paths that accommodate centripetal acceleration limits.
This paper presents a noval method that generates optimal trajectories for autonomous vehicles for in-lane driving scenarios. The method computes a trajectory using a two-phase optimization procedure. In the first phase, the optimization procedure generates a close-form driving guide line with differetiable curvatures. In the second phase, the procedure takes the driving guide line as input, and outputs dynamically feasible, jerk and time optimal trajectories for vehicles driving along the guide line. This method is especially useful for generating trajectories at curvy road where the vehicles need to apply frequent accelerations and decelerations to accommodate centripetal acceleration limits.