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Overview
Overview
Traditional modular self-driving frameworks scale poorly in new scenarios, which usually require tedious hand-tuning of rules and parameters to maintain acceptable performance in all foreseeable occasions. Therefore, robust and safe self-driving using traditional frameworks is still challenging, especially in complex and dynamic environments.
Recently, deep-learning based self-driving methods 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. The results also show that our method achieves better performance over the baselines in terms of success rate.
Qualitative Results
Qualitative Results
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