StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving

Jinkyu Kim, Reza Mahjourian, Scott Ettinger, Mayank Bansal, Brandyn White, Ben Sapp, Dragomir Anguelov

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    9 Citations (Scopus)

    Abstract

    We introduce a motion forecasting (behavior prediction) method that meets the latency requirements for autonomous driving in dense urban environments without sacrificing accuracy. A whole-scene sparse input representation allows StopNet to scale to predicting trajectories for hundreds of road agents with reliable latency. In addition to predicting trajectories, our scene encoder lends itself to predicting whole-scene probabilistic occupancy grids, a complementary output representation suitable for busy urban environments. Occupancy grids allow the AV to reason collectively about the behavior of groups of agents without processing their individual trajectories. We demonstrate the effectiveness of our sparse input representation and our model in terms of computation and accuracy over three datasets. We further show that co-training consistent trajectory and occupancy predictions improves upon state-of-the-art performance under standard metrics.

    Original languageEnglish
    Title of host publication2022 IEEE International Conference on Robotics and Automation, ICRA 2022
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages8957-8963
    Number of pages7
    ISBN (Electronic)9781728196817
    DOIs
    Publication statusPublished - 2022
    Event39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States
    Duration: 2022 May 232022 May 27

    Publication series

    NameProceedings - IEEE International Conference on Robotics and Automation
    ISSN (Print)1050-4729

    Conference

    Conference39th IEEE International Conference on Robotics and Automation, ICRA 2022
    Country/TerritoryUnited States
    CityPhiladelphia
    Period22/5/2322/5/27

    Bibliographical note

    Funding Information:
    Acknowledgments. J. Kim is partially supported by the National Research Foundation of Korea grant (NRF-2021R1C1C1009608), Basic Science Research Program (NRF-2021R1A6A1A13044830), and ICT Creative Consilience program (IITP-2022-2022-0-01819).

    Publisher Copyright:
    © 2022 IEEE.

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Electrical and Electronic Engineering
    • Control and Systems Engineering

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