Streaming Motion Forecasting for Autonomous Driving

Ziqi Pang, Deva Ramanan, Mengtian Li, Yu Xiong Wang

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

Abstract

Trajectory forecasting is a widely-studied problem for autonomous navigation. However, existing benchmarks evaluate forecasting based on independent snapshots of trajectories, which are not representative of real-world applications that operate on a continuous stream of data. To bridge this gap, we introduce a benchmark that continuously queries future trajectories on streaming data and we refer to it as 'streaming forecasting.' Our benchmark inherently captures the disappearance and re-appearance of agents, presenting the emergent challenge of forecasting for occluded agents, which is a safetycritical problem yet overlooked by snapshot-based benchmarks. Moreover, forecasting in the context of continuous timestamps naturally asks for temporal coherence between predictions from adjacent timestamps. Based on this benchmark, we further provide solutions and analysis for streaming forecasting. We propose a plug-and-play meta-algorithm called 'Predictive Streamer' that can adapt any snapshot-based forecaster into a streaming forecaster. Our algorithm estimates the states of occluded agents by propagating their positions with multi-modal trajectories, and leverages differentiable filters to ensure temporal consistency. Both occlusion reasoning and temporal coherence strategies significantly improve forecasting quality, resulting in 25% smaller endpoint errors for occluded agents and 10-20% smaller fluctuations of trajectories. Our work is intended to generate interest within the community by highlighting the importance of addressing motion forecasting in its intrinsic streaming setting. Code is available at https://github.com/ziqipang/StreamingForecasting.

Original languageEnglish (US)
Title of host publication2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7407-7414
Number of pages8
ISBN (Electronic)9781665491907
DOIs
StatePublished - 2023
Event2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023 - Detroit, United States
Duration: Oct 1 2023Oct 5 2023

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
Country/TerritoryUnited States
CityDetroit
Period10/1/2310/5/23

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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