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Diverse and Admissible Trajectory Forecasting Through Multimodal Context Understanding

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12356)

Abstract

Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable decision-making. Due to partial observability in these dynamical scenes, directly obtaining the posterior distribution over future agent trajectories remains a challenging problem. In realistic embodied environments, each agent’s future trajectories should be both diverse since multiple plausible sequences of actions can be used to reach its intended goals, and admissible since they must obey physical constraints and stay in drivable areas. In this paper, we propose a model that synthesizes multiple input signals from the multimodal world|the environment’s scene context and interactions between multiple surrounding agents|to best model all diverse and admissible trajectories. We compare our model with strong baselines and ablations across two public datasets and show a significant performance improvement over previous state-of-the-art methods. Lastly, we offer new metrics incorporating admissibility criteria to further study and evaluate the diversity of predictions. Codes are at: https://github.com/kami93/CMU-DATF.

Keywords

Trajectory forecasting Diversity Admissibility Generative modeling Autonomous driving 

Notes

Acknowledgements

This work was supported in part by the Technology Innovation Program under Grant 10083646 (Development of Deep Learning-Based Future Prediction and Risk Assessment Technology considering Inter-vehicular Interaction in Cut-in Scenario), funded by the Ministry of Trade, Industry, and Energy, South Korea. We also acknowledge the anonymous reviewers for their constructive comments.

Supplementary material

504452_1_En_17_MOESM1_ESM.pdf (2.4 mb)
Supplementary material 1 (pdf 2434 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Hanyang UniversitySeoulKorea
  2. 2.Yonsei UniversitySeoulKorea
  3. 3.Korea UniversitySeoulKorea
  4. 4.Carnegie Mellon UniversityPittsburghUSA
  5. 5.Sogang UniversitySeoulKorea
  6. 6.Bosch Research PittsburghPittsburghUSA

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