This paper presents a computationally-efficient fuel-economic control strategy for a group of connected vehicles in urban roads. We assume the availability of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Apart from fuel economy, the proposed higher-level controller also focuses on reducing red light idling, which improves traffic mobility and in turn improves vehicle emissions. The red light idling avoidance problem is formulated as a two-point boundary value problem and sampling-based approach is employed to evaluate a feasible solution in real-time. This leads to control solutions 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 higher-level control solution provides a good initial solution for any deterministic lower-level controller. Simulation results show the efficacy of the proposed method in terms of fuel economy and computational efficiency.
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