Infrastructure-free NLoS Obstacle Detection for Autonomous Cars

Infrastructure-free NLoS Obstacle Detection for Autonomous Cars

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 dynami...

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Bibliographic Details
Main Authors: Naser, Felix M, Gilitschenski, Igor, Amini, Alexander A, Liao, Christina, Rosman, Guy, Karaman, Sertac, Rus, Daniela L
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Published: Institute of Electrical and Electronics Engineers (IEEE) 2019
Online Access:https://hdl.handle.net/1721.1/122984
Description
Summary: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.

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