Trajectory optimization for autonomous overtaking with visibility maximization

Abstract

In this paper we present a trajectory generation method for autonomous overtaking of static obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example, the autonomous car may have to move slightly into the opposite lane in order to cleanly see in front of a car ahead. Once it has gathered enough information about the road ahead, then the autonomous car can safely overtake. We generate safe trajectories by solving, in real-time, a non-linear constrained optimization, formulated as a Receding Horizon planner. The planner is guided by a high-level state machine, which determines when the overtake maneuver should begin. Our main contribution is a method that can maximize visibility, prioritizes safety and respects the boundaries of the road while executing the maneuver. We present experimental results in simulation with data collected during real driving.