Infrastructure-free NLoS Obstacle Detection for Autonomous Cars
Name
ShadowCam_for_Cars_at_Nights.pdf
Size
5.48 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Naser, Felix M
Gilitschenski, Igor
Amini, Alexander A
Liao, Christina
Rosman, Guy
Karaman, Sertac
Rus, Daniela L
Date Issued
November 2019
Journal
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Naser, Felix et al. "Infrastructure-free NLoS Obstacle Detection for Autonomous Cars."
IEEE/RSJ International Conference on Intelligent Robots and Systems, November 2019, Macau, China, Institute of Electrical and Electronics Engineers, forthcoming.
Version
Author's final manuscript
Abstract
Current perception systems mostly require direct line of sight to anticipate and ultimately prevent potential
collisions at intersections with other road users. We present a fully integrated autonomous system capable of detecting shadows or weak illumination changes on the ground caused by a dynamic obstacle in NLoS scenarios. This additional virtual sensor “ShadowCam” extends the signal range utilized so far by computer-vision ADASs. We show that (1) our algorithm maintains the mean classification accuracy of around 70% even when it doesn’t rely on infrastructure – such as AprilTags – as an image registration method. We validate (2) in real-world experiments that our autonomous car driving in night time conditions detects a hidden approaching car earlier with our virtual sensor than with the front facing 2-D LiDAR.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://www.iros2019.org/
