Autonomous Street Crossing with City Navigation Robots

城市自主导航带来了各种各样的难题。其中，最具挑战性的一项就是自动过街。虽然过去人们对城市导航车辆过街的问题进行了深入的研究，但最近的大部分工作都集中在过有信号的人行横道这一具体任务上，或者需要人类的协助。在这个项目中，我们将开发一种完全自主的过街方法，即使在没有人行横道或信号控制的行人过马路的情况下，机器人也可以过马路。为此，我们将采用基于图像、激光雷达和声音数据的多式联运方法，以识别横过马路的机会和追踪其他交通参与者。此外，我们将使用反向强化学习来根据对人类过街活动的观察来推断过街策略。

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.