Distributed scenario model predictive control for driver aided intersection crossing

Abstract

The automation of road intersections has significant potential to improve traffic throughput and efficiency. While the related control problem is usually addressed assuming fully automated vehicles, we focus on the problem of issuing appropriate speed advices to the driver in order to optimize traffic flow in intersections without any traffic lights or signs. Therefore, a distributed scenario-based model predictive control regime is proposed which accounts for uncertainties in the driver reaction to speed advices issued by the control system. In the scenario approach, we draw independently and identically distributed samples from a bounded uncertainty set and optimize over scenarios which reflect a potential driver reaction. Based on the number of samples, we can give guarantees on avoiding collisions under acting uncertainties. Simulation results demonstrate that the scenario approach is capable of avoiding collisions when the driver reacts uncertain while the nominal approach is not