IntentNet: Learning to Predict Intention from Raw Sensor Data

    Abstract

    In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants. We define intent as a combination of discrete high level behaviors as well as continuous trajectories describing future motion. In this paper we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reduce reaction time in self-driving applications.

    Authors

    Sergio Casas, Wenjie Luo, Raquel Urtasun

    Conference

    CORL 2018

    Full Paper

    ‘IntentNet: Learning to Predict Intention from Raw Sensor Data’ (PDF)

    Uber ATG

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    Sergio Casas
    Sergio is a Masters' student at the University of Toronto with a variety of research interests including Deep Learning for Computer Vision, Reinforcement Learning and Imitation Learning. His supervisor is Prof. Raquel Urtasun. At the same time, he is working full-time at Uber ATG Toronto to apply his research to the development of Self-Driving Cars technology, focusing on Perception and Prediction problems. Before coming to UofT, he completed two Bachelor degrees at Universitat Politecnica de Catalunya (UPC), in Computer Science and Industrial Engineering. He is originally from Barcelona, Spain.
    Wenjie Luo
    Wenjie is a senior research scientist, founding member of the Uber ATG R&D team. His research interests include computer vision and machine learning, and his work spans the full autonomy stack including perception, prediction and planning. Previously, he did master in TTI-Chicago and continued to the PhD program in University of Toronto, both under Prof. Raquel Urtasun. He also spent some time at Apple SPG prior to join Uber.
    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