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Traditional decision and planning frameworks for self-driving vehicles (SDVs)scale poorly in new scenarios, thus they require tedious hand-tuning of rulesand parameters to maintain acceptable performance in all foreseeable cases.Recently, self-driving methods based on deep learning have shown promisingresults with better generalization capability but less hand engineering effort.However, most of the previous learning-based methods are trained and evaluatedin limited driving scenarios with scattered tasks, such as lane-following,autonomous braking, and conditional driving. In this paper, we propose agraph-based deep network to achieve scalable self-driving that can handlemassive traffic scenarios. Specifically, more than 7,000 km of evaluation isconducted in a high-fidelity driving simulator, in which our method can obeythe traffic rules and safely navigate the vehicle in a large variety of urban,rural, and highway environments, including unprotected left turns, narrowroads, roundabouts, and pedestrian-rich intersections. Demonstration videos areavailable at https://caipeide.github.io/dignet/.

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