Through integrating advanced communication and data processing technologies into smart vehicles and roadside infrastructures, the Intelligent Transportation System(ITS) has evolved as a promising paradigm for improving safety, efficiency of the transportation system. However, the strict delay requirement of the safety-related applications is still a great challenge for the ITS, especially in dense traffic environment. In this paper, we introduce the metric called Perception-Reaction Time(PRT), which reflects the time consumption of safety-related applications and is closely related to road efficiency and security. With the integration of the incorporating information-centric networking technology and the fog virtualization approach, we propose a novel fog resource scheduling mechanism to minimize the PRT. Furthermore, we adopt a deep reinforcement learning approach to design an on-line optimal resource allocation scheme. Numerical results demonstrate that our proposed schemes is able to reduce about 70% of the RPT compared with the traditional approach.
