PiP: Planning-informed Trajectory Prediction for Autonomous Driving

  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},
  • 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

    Figures, Tables, and Topics from this paper.


    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