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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Xehetasun bibliografikoak
Egile Nagusiak: Naser, Felix M, Gilitschenski, Igor, Amini, Alexander A, Liao, Christina, Rosman, Guy, Karaman, Sertac, Rus, Daniela L
Beste egile batzuk: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Formatua: Artikulua
Argitaratua: Institute of Electrical and Electronics Engineers (IEEE) 2019
Sarrera elektronikoa:https://hdl.handle.net/1721.1/122984
Deskribapena
Gaia: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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