LEARNING AND COORDINATION FOR AUTONOMOUS INTERSECTION CONTROL

Future urban road traffic management is an example of a socially relevant problem that can be modeled as a large-scale, open, distributed system, composed of many autonomous interacting agents, which need to be controlled in a decentralized manner. In this context, advanced, reservation-based, intersection control—where autonomous vehicles controlled entirely by agents interact with a coordination facility that controls an intersection, to avoid collisions and minimize delays—will be a possible scenario in the near future. In this article, we seize the opportunities for multiagent learning offered by such a scenario, studying i) how vehicles, when approaching a reservation-based intersection, can coordinate their actions in order to improve their crossing times, and therefore, speed up the traffic flow through the intersection, and ii) how a set of reservation-based intersections can cooperatively act over an entire network of intersections in order to minimize travel times.