 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 .
<clarity>  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 .
 In this paper, we propose a graph-based deep network to achieve unified and scalable self-driving that can handle massive traffic scenarios . 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 .