Optimal Trajectory Generation for Autonomous Vehicles Under Centripetal Acceleration Constraints for In-lane Driving Scenarios

TL;DR

This paper introduces a two-phase optimization method for generating optimal, dynamically feasible trajectories for autonomous vehicles in curvy in-lane scenarios, respecting centripetal acceleration constraints.

Contribution

The paper proposes a novel two-phase approach combining a closed-form guide line generation with a dynamic trajectory optimization for in-lane driving.

Findings

Effective trajectory generation on curvy roads with acceleration constraints

Produces jerk and time optimal trajectories

Applicable to real-world autonomous driving scenarios

Abstract

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.