A Survey on Intersection Management of Connected Autonomous Vehicles

Abstract

Intersection management of Connected Autonomous Vehicles (CAVs) has the potential to improve safety and mobility. CAVs approaching an intersection can exchange information with the infrastructure or each other to schedule their cross times. By avoiding unnecessary stops, scheduling CAVs can increase traffic throughput, reduce energy consumption, and most importantly, minimize the number of accidents that happen in intersection areas due to human errors. We study existing intersection management approaches from following key perspectives: (1) intersection management interface, (2) scheduling policy, (3) existing wireless technologies, (4) existing vehicle models used by researchers and their impact, (5) conflict detection, (6) extension to multi-intersection management, (7) challenges of supporting human-driven vehicles, (8) safety and robustness required for real-life deployment, (9) graceful degradation and recovery for emergency scenarios, (10) security concerns and attack models, and (11) evaluation methods. We then discuss the effectiveness and limitations of each approach with respect to the aforementioned aspects and conclude with a discussion on tradeoffs and further research directions.