LAUSR

As the operational domain of autonomous vehicles expands, encountering occlusions during navigation becomes unavoidable. Most of the existing research on occlusion-aware motion planning focuses only on the longitudinal motion of the ego vehicle and neglects its lateral motion, resulting in output motion that can be overly conservative. This paper proposes a motion planner capable of actively adjusting the ego vehicle’s lateral position to minimize occlusions. The proposed planner is applicable in various scenarios and can function under perception uncertainty. This work also extends our previously proposed 3D visibility estimation approach for addressing occlusions caused by objects which are not present in HD maps. The proposed planner first generates candidate trajectories. The current and future visibility of each trajectory is then estimated using live LiDAR data and HD maps. These estimated visibilities are converted into visibility costs, which are then used to determine the optimal output trajectory in conjunction with other planning costs. The proposed planner is tested in three scenarios using the CARLA simulator: an occluded T-junction crossing, turning at a low-visibility corner and preparing to pass a parked vehicle, using live localization and object detection results. The experimental results reveal that the proposed planner allows the ego vehicle to minimize occlusions by diverging from the center of the lane and, consequently, to discover occluded vehicles earlier than a baseline planner in most situations. Moreover, occlusions caused by a parked vehicle not present in the HD maps were estimated correctly using our extended visibility estimation method.