Probabilistic Risk Metrics for Navigating Occluded Intersections
Name
mcgill2019ral.pdf
Description
Accepted version
Size
4.74 MB
Format
Adobe PDF
Checksum (MD5)
b69a1873240ec20a8517c443236e3f1c
Author(s)
Ort, Moses Teddy
Pierson, Alyssa
Gilitschenski, Igor
Araki, Brandon
Karaman, Sertac
Rus, Daniela L
Leonard, John J
Date Issued
July 2019
Journal
IEEE robotics and automation letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
McGill, Stephen G. et al. “Probabilistic Risk Metrics for Navigating Occluded Intersections.” IEEE robotics and automation letters, vol. 4, no. 4, 2019, pp. 4322 - 4329 © 2019 The Author(s)
Version
Author's final manuscript
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 real-time, 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/LRA.2019.2931823
