Robust and safe self-driving in complex and dynamic environments is quite challenging due to the requirement of scalable driving policies against the wide variety of traffic scenarios (e,g., road topologies, traffic rules and interaction with road agents). In this area, traditional modular frameworks scale poorly in new environments, and require tremendous and iterative hand-tuning of rules and parameters to maintain  performance in all foreseeable scenarios. 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  methods are trained and evaluated in limited and simple environments with scattered tasks, such as lane-following, autonomous braking  and conditional driving. In this paper, we propose a graph-based deep network to achieve unified and scalable self-driving in diverse dynamic environments. The extensive evaluation results show that our model can safely navigate the vehicle in a large variety of urban, rural, and highway areas with dense traffic while obeying traffic rules . Specifically, more than 7, 500 km of closed-loop driving evaluation is conducted in dynamic simulation environments , in which our method can handle complex driving situations, and achieve higher success rates (73.5\%-83.2\%) and driving scores than the baselines .
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. This work is accompanied with some demonstration videos which are available at URL
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Robust and safe
Traditional modular
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in complex and dynamic environments is quite challenging due to the requirement of scalable driving policies against the wide variety of traffic scenarios (e,g., road topologies, traffic rules and interaction with road agents). In this area, traditional modular
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environments, and require tremendous and iterative
scenarios, which usually require tedious
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acceptable
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scenarios.
occasions. Therefore, robust and safe self-driving using traditional frameworks is still challenging, especially in complex and dynamic environments.
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learning-based
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and simple environments
driving scenarios
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,
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unified and
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in diverse dynamic environments. The extensive evaluation results show that our model can safely navigate the vehicle in a large variety of urban, rural, and highway areas with dense traffic while obeying traffic rules
that can handle massive traffic scenarios
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500 km of closed-loop driving
000 km of
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dynamic simulation environments
a high-fidelity driving simulator
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handle complex driving situations, and achieve higher success rates (73.5\%-83.2\%) and driving scores than the baselines .
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. This work is accompanied with some demonstration videos which are available at URL