  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/.
