TY - GEN
T1 - StopNet
T2 - 39th IEEE International Conference on Robotics and Automation, ICRA 2022
AU - Kim, Jinkyu
AU - Mahjourian, Reza
AU - Ettinger, Scott
AU - Bansal, Mayank
AU - White, Brandyn
AU - Sapp, Ben
AU - Anguelov, Dragomir
N1 - 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.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85127817940&partnerID=8YFLogxK
U2 - 10.1109/ICRA46639.2022.9811830
DO - 10.1109/ICRA46639.2022.9811830
M3 - Conference contribution
AN - SCOPUS:85127817940
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 8957
EP - 8963
BT - 2022 IEEE International Conference on Robotics and Automation, ICRA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 May 2022 through 27 May 2022
ER -