Tackling Existence Probabilities of Objects with Motion Planning for Automated Urban Driving

Abstract: Motion planners take uncertain information about the environment as an input. The environment information is often quite noisy and has a tendency to contain false positive object detection. State-of-the-art motion planners consider all objects alike, thus producing overcautious behavior. In this paper we present a planning approach that considers alternative maneuvers in a combined fashion and plans a motion that is formed by the probabilities of those alternatives. The proposed planner can smoothly react to objects with low existence probability while remaining collision-free in case their existence substantiates. In this way, it tolerates the faults arising from perception and prediction, thus reducing their impact on operational reliability.