Autonomous traffic at intersections : an optimization-based analysis of possible time, energy, and CO 2 savings

Abstract

In the field of autonomous driving, traffic-light-controlled intersections are of special interest. We analyze how much an optimized coordination of vehicles and infrastructure can contribute to efficient transit through these bottlenecks, depending on traffic density and certain regulations of traffic lights. To this end, we develop a mixed-integer linear programming model to describe the interaction between traffic lights and discretized traffic flow. It is based on a microscopic traffic model with centrally controlled autonomous vehicles. We aim to determine a globally optimal traffic flow for given scenarios on a simple, but extensible, urban road network. The resulting models are very challenging to solve, in particular when involving additional realistic traffic-light regulations such as minimum red and green times. While solving times exceed real-time requirements, our model allows an estimation of the maximum performance gains due to improved communication and serves as a benchmark for heuristic and decentralized approaches.