DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks

• URL: http://arxiv.org/abs/2011.06775v3
• Date: Fri, 30 Jul 2021 00:56:58 GMT
• Title: DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks
• Authors: Peide Cai, Hengli Wang, Yuxiang Sun, Ming Liu
• Abstract summary: We propose a graph-based deep network to achieve scalable self-driving that can handle massive traffic scenarios. More than 7,000 km of evaluation is conducted in a high-fidelity driving simulator. Our method can obey the traffic rules and safely navigate the vehicle in a large variety of urban, rural, and highway environments.
• Score: 26.558394047144006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in new scenarios, thus they require tedious hand-tuning of rules and parameters to maintain acceptable performance in all foreseeable cases. Recently, self-driving methods based on deep learning have shown promising results with better generalization capability but less hand engineering effort. However, most of the previous learning-based methods are trained and evaluated in limited driving scenarios with scattered tasks, such as lane-following, autonomous braking, and conditional driving. In this paper, we propose a graph-based deep network to achieve scalable self-driving that can handle massive traffic scenarios. Specifically, more than 7,000 km of evaluation is conducted in a high-fidelity driving simulator, in which our method can obey the traffic rules and safely navigate the vehicle in a large variety of urban, rural, and highway environments, including unprotected left turns, narrow roads, roundabouts, and pedestrian-rich intersections. Demonstration videos are available at https://caipeide.github.io/dignet/.

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