Probabilistic Risk Metrics for Navigating Occluded Intersections

作者：Stephen G. McGill, Guy Rosman, Teddy Ort, Alyssa Pierson, Igor Gilitschenski, Brandon Araki, Luke Fletcher, Sertaç Karaman, Daniela Rus, John J. Leonard · 发表于：IEEE Robotics and Automation Letters · 年份：2019 · DOI：10.1109/lra.2019.2931823 · 被引用次数：40 · 研究领域：Risk and Safety Analysis、Traffic and Road Safety、Autonomous Vehicle Technology and Safety

Among traffic accidents in the USA, 23% of fatal and 32% of non-fatal incidents occurred at intersections. For driver assistance systems, intersection navigation remains a difficult problem that is critically important to increasing driver safety. In this letter, we examine how to navigate an unsignalized intersection safely under occlusions and faulty perception. We propose a realtime, probabilistic, risk assessment for parallel autonomy control applications for occluded intersection scenarios. The algorithms are implemented on real hardware and are deployed in a variety of turning and merging topologies. We show phenomena that establish go/no-go decisions, augment acceleration through an intersection and encourage nudging behaviors toward intersections.