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Before reaching full autonomy, vehicles will gradually be equipped with moreand more advanced driver assistance systems (ADAS), effectively rendering themsemi-autonomous. However, current ADAS technologies seem unable to handlecomplex traffic situations, notably when dealing with vehicles arriving fromthe sides, either at intersections or when merging on highways. The high rateof accidents in these settings prove that they constitute difficult drivingsituations. Moreover, intersections and merging lanes are often the source ofimportant traffic congestion and, sometimes, deadlocks. In this article, wepropose a cooperative framework to safely coordinate semi-autonomous vehiclesin such settings, removing the risk of collision or deadlocks while remainingcompatible with human driving. More specifically, we present a supervisedcoordination scheme that overrides control inputs from human drivers when theywould result in an unsafe or blocked situation. To avoid unnecessaryintervention and remain compatible with human driving, overriding only occurswhen collisions or deadlocks are imminent. In this case, safe overridingcontrols are chosen while ensuring they deviate minimally from those originallyrequested by the drivers. Simulation results based on a realistic physicssimulator show that our approach is scalable to real-world scenarios, andcomputations can be performed in real-time on a standard computer for up to adozen simultaneous vehicles.

An Algorithm for Supervised Driving of Cooperative Semi-Autonomous Vehicles (Extended)