Computationally - Efficient Fuel-Economic High-Level Control Strategy for Connected Vehicles with Probabilistic Collision Avoidance

This paper presents a computationally-efficient fuel-economic control strategy for connected vehicles in urban roads in the presence of uncertainty in shared information. The proposed high-level controller also focuses on reducing red-light idling, which improves traffic mobility and vehicle emissions. We model the red-light idling avoidance problem with a terminal constraint and evaluate the equivalent deterministic constraints that ensure probabilistic collision avoidance. We then employ a sampling-based approach to evaluate a feasible solution in real-time. This leads to control solutions -if a feasible solution is obtained- that can ensure avoidance of red-light idling despite the number of vehicles in front of it. We have shown that sampling from a Gaussian distribution whose mean depends on the target velocity can improve fuel economy to a good extent. This high-level controller provides a good initial solution for any deterministic low-level controller. Simulation results show the efficacy of the proposed method in terms of fuel economy and computational efficiency.