Machine Learning for Autonomous Vehicle Traffic Signal Prediction and Coordination
Keywords:
traffic signal schedule, CAVsAbstract
Recent research-grade literature posits developing novel traffic control policies that could better accommodate both human-driven vehicles and CAVs [1]. In addition to the traffic control strategies selected based on the general traffic characteristics, personalized traffic signal control strategies and timing policies may be determined according to the requirements of the CAVs waiting at the intersection in future smart cities. It is not clear how reinforcing the importance of informed platoon signal control can reduce the conflict of vehicles. By quantifying how traffic signal schedule feeds back the expected number of vehicles in a network, the threshold rates are derived, which demonstrate the importance of synergy between the left-turn movement and the platoon signal at an unsignalized intersection; a competitive characteristic exists between the through movement and left-turn movement at the signalized intersection; the majority of drivers may choose to rejoin the queue lane.
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