Probabilistic Risk Metrics for Navigating Occluded Intersections
Stephen G. McGill, Guy Rosman, Teddy Ort, Alyssa Pierson, Igor Gilitschenski, Brandon Araki, Luke Fletcher, Sertac Karaman
Abstract
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.
BibTeX
@inproceedings{ral2019_probabilisticris,
title = {Probabilistic Risk Metrics for Navigating Occluded Intersections},
author = {Stephen G. McGill and Guy Rosman and Teddy Ort and Alyssa Pierson and Igor Gilitschenski and Brandon Araki and Luke Fletcher and Sertac Karaman and Daniela Rus and John J. Leonard},
booktitle = {RA-L 2019},
year = {2019}
}