A Safety-Driven Interpretable Model for Vehicle Control With Impact on Traffic

This paper proposes the utilization of a multi-mode ACC based on interpretable Finite State Machine (FSM) to address challenges in infrastructure and surrounding condition changes while meeting macroscopic safety and comfort requirements in traffic flow. Specifically, the paper designs and simulates a merge yield control mode, a dynamic speed change mode, and a safety and monitoring mode switching under defined state transitions with the traditional car-following mode. Advanced ACC algorithms have been applied to improve traffic efficiency and have demonstrated energy savings. Yet they have typically been deployed in a single-use case: car-following mode. In this mode, where the Autonomous Vehicle (AV) maintains an appropriate distance or time gap with the preceding vehicle, decelerating when the gap is small and accelerating when it is large, the system may struggle to guarantee safety and comfort in complex and variable driving scenarios. Although there exist mode-switching ACC and merge mode controllers, in which even involving latitude direction control have been proposed, their dynamics when driving alongside other controlled or human-driving vehicles under a Connected and Autonomous Vehicles (CAVs) traffic environment remains unclear. The paper includes results from an implementation that was successfully tested on the open road, and simulation results that show dampened disturbances from the mode-switching approach, compared to single-mode use of an ACC controller in the same scenarios.