  This letter introduces an intelligent Real-time Dual-functional
Radar-Communication (iRDRC) system for autonomous vehicles (AVs). This system
enables an AV to perform both radar and data communications functions to
maximize bandwidth utilization as well as significantly enhance safety. In
particular, the data communications function allows the AV to transmit data,
e.g., of current traffic, to edge computing systems and the radar function is
used to enhance the reliability and reduce the collision risks of the AV, e.g.,
under bad weather conditions. The problem of the iRDRC is to decide when to use
the communication mode or the radar mode to maximize the data throughput while
minimizing the miss detection probability of unexpected events given the
uncertainty of surrounding environment. To solve the problem, we develop a deep
reinforcement learning algorithm that allows the AV to quickly obtain the
optimal policy without requiring any prior information about the environment.
Simulation results show that the proposed scheme outperforms baseline schemes
in terms of data throughput, miss detection probability, and convergence rate.
