Autonomous Street Crossing with City Navigation Robots

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Autonomous urban navigation poses a variety of difficult problems. Among them, one of the most challenging ones is that of autonomously crossing streets. While the problem of crossing streets with city navigating vehicles has been studied intensively in the past, the majority of recent work focused on the specific task of crossing signalized crosswalks or required the assistance of humans. In this project, we will develop a completely autonomous approach for crossing streets that allows a robot to cross a street even when there is no crosswalk nor a signalized pedestrian crossing. To achieve this, we will follow multimodal approaches based on images, lidar and sound data for the identification of crossing opportunities and the tracking of other traffic participants. In addition, we will employ inverse reinforcement learning to infer crossing policies from observations of human street crossing activities.