PiP: Planning-informed Trajectory Prediction for Autonomous Driving

@inproceedings{Song2020PiPPT,
  title={PiP: Planning-informed Trajectory Prediction for Autonomous Driving},
  author={H. Song and Wenchao Ding and Yuxuan Chen and Shaojie Shen and M. Wang and Qifeng Chen},
  booktitle={ECCV},
  year={2020}
}
  • H. Song, Wenchao Ding, +3 authors Qifeng Chen
  • Published in ECCV 2020
  • Computer Science
  • It is critical to predict the motion of surrounding vehicles for self-driving planning, especially in a socially compliant and flexible way. However, future prediction is challenging due to the interaction and uncertainty in driving behaviors. We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. Our approach is differentiated from the traditional manner of prediction, which is only based on historical information and decoupled… CONTINUE READING

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    References

    SHOWING 1-10 OF 35 REFERENCES
    Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning
    • L. Sun, Wei Zhan, M. Tomizuka
    • Computer Science, Mathematics
    • 2018 21st International Conference on Intelligent Transportation Systems (ITSC)
    • 2018
    • 29
    • PDF
    Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios
    • 18
    • PDF
    Planning and Decision-Making for Autonomous Vehicles
    • 117
    • PDF
    PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings
    • 72
    • PDF
    Optimal trajectory generation for dynamic street scenarios in a Frenét Frame
    • 249
    • PDF
    TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
    • 106
    • PDF
    Motion planning for autonomous driving with a conformal spatiotemporal lattice
    • 180
    • PDF
    A Belief State Planner for Interactive Merge Maneuvers in Congested Traffic
    • 15
    Multi-Agent Tensor Fusion for Contextual Trajectory Prediction
    • T. Zhao, Yifei Xu, +5 authors Y. Wu
    • Computer Science
    • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    • 2019
    • 87
    • Highly Influential
    • PDF
    Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network
    • 12
    • PDF