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DSDNet: Deep Structured Self-driving Network

  • Wenyuan ZengEmail author
  • Shenlong Wang
  • Renjie Liao
  • Yun Chen
  • Bin Yang
  • Raquel Urtasun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12366)

Abstract

In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network. Towards this goal, we develop a deep structured energy based model which considers the interactions between actors and produces socially consistent multimodal future predictions. Furthermore, DSDNet explicitly exploits the predicted future distributions of actors to plan a safe maneuver by using a structured planning cost. Our sample-based formulation allows us to overcome the difficulty in probabilistic inference of continuous random variables. Experiments on a number of large-scale self driving datasets demonstrate that our model significantly outperforms the state-of-the-art.

Keywords

Autonomous driving Motion prediction Motion planning 

Supplementary material

504479_1_En_10_MOESM1_ESM.pdf (1.9 mb)
Supplementary material 1 (pdf 1928 KB)

Supplementary material 2 (mp4 51724 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Wenyuan Zeng
    • 1
    • 2
    Email author
  • Shenlong Wang
    • 1
    • 2
  • Renjie Liao
    • 1
    • 2
  • Yun Chen
    • 1
  • Bin Yang
    • 1
    • 2
  • Raquel Urtasun
    • 1
    • 2
  1. 1.Uber ATGPittsburghUSA
  2. 2.University of TorontoTorontoCanada

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