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End-to-End Deep Structured Models for Drawing Crosswalks

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Abstract

In this paper we address the problem of detecting crosswalks from LiDAR and camera imagery. Towards this goal, given multiple Li-DAR sweeps and the corresponding imagery, we project both inputs onto the ground surface to produce a top down view of the scene. We then leverage convolutional neural networks to extract semantic cues about the location of the crosswalks. These are then used in combination with road centerlines from freely available maps (e.g., OpenStreetMaps) to solve a structured optimization problem which draws the final crosswalk boundaries. Our experiments over crosswalks in a large city area show that 96.6% automation can be achieved.

Authors

Justin Liang, Raquel Urtasun

Conference

ECCV 2018

Full Paper

‘End-to-End Deep Structured Models for Drawing Crosswalks’ (PDF)

Uber ATG

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Justin Liang is a research scientist at Uber ATG Toronto. His research focuses on computer vision and machine learning for mapping and detection in self driving vehicles. Before joining ATG, he completed a MSc in Computer Science, supervised by Raquel Urtasun at the University of Toronto. He also has a BASc in Mechanical Engineering from the University of British Columbia.
Raquel Urtasun is the Chief Scientist for Uber ATG and the Head of Uber ATG Toronto. She is also a Professor at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI. She is a recipient of an NSERC EWR Steacie Award, an NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, a Connaught New Researcher Award, a Fallona Family Research Award and two Best Paper Runner up Prize awarded CVPR in 2013 and 2017. She was also named Chatelaine 2018 Woman of the year, and 2018 Toronto’s top influencers by Adweek magazine