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arXiv · 2008.06041

DSDNet: Deep Structured self-Driving Network

Wenyuan Zeng·Shenlong Wang·Renjie Liao·Yun Chen·Bin Yang·Raquel Urtasun
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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.

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Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun. 2020-08-13. DSDNet: Deep Structured self-Driving Network. https://arxiv.org/abs/2008.06041

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