DSDNet: Deep Structured self-Driving Network

• URL: http://arxiv.org/abs/2008.06041v1
• Date: Thu, 13 Aug 2020 17:54:06 GMT
• Title: DSDNet: Deep Structured self-Driving Network
• Authors: Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun
• Abstract summary: We propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network. We develop a deep structured energy based model which considers the interactions between actors and produces socially consistent multimodal future predictions.
• Score: 92.9456652486422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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