Deriving Spatial Policies for Overtaking Maneuvers with Autonomous Vehicles

Abstract

Planning an accurate and safe trajectory is a crucial element in autonomous driving. To execute complex driving maneuvers like overtaking, motion planning requires an enhanced decision-making algorithm that decides the when, where and how of the overtaking maneuver. This paper proposes an algorithm that increases the likelihood of a safe overtaking maneuver by learning spatial information. Here, spatial information refers to the track portion/curve and the position of the ego vehicle with reference to that. The technique is applied to an autonomous racing setup where vehicles have to detect and operate at the limits of dynamic handling. To learn the spatial information, offline experiments of a 2-player race are conducted to generate probability distributions of overtaking maneuvers conditioned on speed and relative-position of the ego vehicle with respect to the opponent. Furthermore, a Switched Model Predictive Contouring Controller (SMPCC) is proposed for incorporating the policy learning algorithm into the path planning and control setup. Extensive simulations show that the proposed algorithm is able to achieve an increased number of overtakes at different track portions on known and unknown race tracks.