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

4 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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