A collision avoidance system at intersections using Robust Model Predictive Control

Collisions at intersections account for about 40% of all car accidents and for about 20% of all traffic fatalities in the United States. The main cause is human error in recognition and decision making. Active safety systems have thus a great potential for increasing vehicle safety at intersections. They may issue warnings to the driver or assume control of the vehicle in critical situations. Most approaches in current research rely on the assumption that all vehicles at the intersection are controllable, and/or they can be coordinated by a central intersection manager. This paper considers the case of a single controllable ego vehicle surrounded by several uncontrollable target vehicles, without communication. Only a map with the current position and velocity of the target vehicles are assumed to be known, but no pre-defined crossing order is given. A Robust Model Predictive Control strategy is designed for finding safe gaps in the crossing traffic, and for planning optimal trajectories to maximize the ego vehicle's efficiency and driver comfort. It is shown that its performance can be enhanced by Affine Disturbance Feedback. The algorithm is tested in several simulation scenarios and implemented on a test vehicle for experimental validation.