Infrastructure-free NLoS Obstacle Detection for Autonomous Cars
Author(s)Naser, Felix M; Gilitschenski, Igor; Amini, Alexander A; Liao, Christina; Rosman, Guy; Karaman, Sertac; Rus, Daniela L; ... Show more Show less
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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.
DepartmentMassachusetts 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
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)
Institute of Electrical and Electronics Engineers (IEEE)
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.
Author's final manuscript