R²IM- Robust and Resilient Intersection Management of Connected Autonomous Vehicles

Intersection management of Connected Autonomous Vehicles (CAVs) has the potential to significantly improve safety and mobility. While numerous intersection management designs have been proposed in the past few decades, most of them assume that the CAVs will precisely follow the directions of the Intersection Manager (IM) and prove the safety and demonstrate the efficiency based on this assumption. In real life, however, a CAV that is crossing the intersection may break down, accelerate out-of-control or lie about its information (e.g. intended outgoing lane) and cause an accident. In this paper, we first define a fault model called 'rogue vehicle', which is essentially a CAV that either is dishonest or does not follow the IM's directions and then, propose a novel management algorithm (R2IM) that will ensure safe operation, even if a CAV becomes 'rogue' at any point in time. We prove that there can be no accidents inside the intersection, as long as there is no more than one 'rogue vehicle' at a time. We demonstrate the safety of R2IM by performing experiments on 1/10 scale model CAVs and in simulation. We also show that our approach can recover after the rogue vehicle leaves/is removed.