DAGMapper: Learning to Map by Discovering Lane Topology

    Abstract

    One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost. Current attempts to automate this process typically focus on simple scenarios, estimate independent maps per frame or do not have the level of precision required by modern self driving vehicles. In contrast, in this paper we focus on drawing the lane boundaries of complex highways with many lanes that contain topology changes due to forks and merges. Towards this goal, we formulate the problem as inference in a directed acyclic graphical model (DAG), where the nodes of the graph encode geometric and topological properties of the local regions of the lane boundaries. Since we do not know a priori the topology of the lanes, we also infer the DAG topology (i.e., nodes and edges) for each region. We demonstrate the effectiveness of our approach on two major North American Highways in two different states and show high precision and recall as well as 89% correct topology.

    Authors

    Namdar Homayounfar, Wei-Chiu Ma\*, Justin Liang\*, Xinyu Wu, Jack Fan, Raquel Urtasun

    Conference

    ICCV 2019

    Full Paper

    ‘DAGMapper: Learning to Map by Discovering Lane Topology’ (PDF)

    Uber ATG

    Comments
    Previous articleDMM-Net: Differentiable Mask-Matching Network for Video Instance Segmentation
    Next articleImproving Movement Prediction of Traffic Actors using Off-road Loss and Bias Mitigation
    Namdar Homayounfar
    I'm a research scientist at Uber ATG Toronto and a PhD student at Univeristy of Toronto under the supervision of Prof. Raquel Urtasun. I have broad research interests in deep learning and computer vision. My current focus is in development of deep structured models for the creation of HD maps required for the safe navigation of autonomous vehicles. Previously, I obtained my MSc degree in Statistics at University of Toronto and prior to that my BSc in Probalblity and Statistics from McGill University.
    Wei-Chiu Ma
    Wei-Chiu Ma is a PhD student at MIT advised by Prof. Antonio Torralba. His research interests lie in the intersection of computer vision and machine learning, in particular low-level vision and 3D vision. He also works part-time at Uber ATG Toronto with Prof. Raquel Urtasun to apply his research to self-driving vehicles.
    Justin Liang
    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.
    Xinyu Wu
    Xinyu Wu is a research engineer at Uber ATG. He works on several projects on map automation and lidar simulation. Before joining ATG, he worked on time prediction system in UberEats using machine learning model.
    Avatar
    Jack Fan is a research engineer at Uber ATG. He works on several projects on localization and lane extraction. He also designs and implements amazing machine learning training platform and pipeline for the reserach team.
    Raquel Urtasun
    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