Decentralized Optimal Coordination of Connected and Automated Vehicles for Multiple Traffic Scenarios

Abstract

Connected and automated vehicles (CAVs) provide the most intriguing opportunity to optimize energy consumption and travel time. Several approaches have been proposed in the literature that allow CAVs to coordinate in situations where there is a potential conflict, for example, in signalized intersections, merging at roadways and roundabouts, to reduce energy consumption and optimize traffic flow. In this paper, we consider the problem of coordinating CAVs in a corridor consisting of multiple traffic scenarios. We formulate a two-level optimization problem in which we maximize traffic throughput in the upper-level problem, and derive a closed-form analytical solution that yields the optimal control input for each CAV, in terms of fuel consumption, in the low-level problem. We validate the effectiveness of the solution through simulation under 100% CAVpenetration rate. Fuel consumption and travel time for the vehicles are significantly reduced compared to a baseline scenario consisting of human-driven vehicles.

arXiv admin note: text overlap with arXiv:1909.10106

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• Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarios
• Distributed Cooperative Driving in Multi-Intersection Road Networks
• Optimal Cooperative Driving at Signal-Free Intersections with Polynomial-Time Complexity
• Performance Analysis of Optimally Coordinated Connected and Automated Vehicles in a Mixed Traffic Environment