A-DRIVE: Autonomous Deadlock Detection and Recovery at Road Intersections for Connected and Automated Vehicles

Abstract

Connected and Automated Vehicles (CAVs) are highly expected to improve traffic throughput and safety at road intersections, single-track lanes, and construction zones. However, multiple CAVs can block each other and create a mutual deadlock around these road segments (i) when vehicle systems have a failure, such as a communication failure, control failure, or localization failure and/or (ii) when vehicles use a long shared road segment. In this paper, we present an Autonomous Deadlock Detection and Recovery Protocol at Intersections for Automated Vehicles named A-DRIVE that is a decentralized and timesensitive technique to improve traffic throughput and shorten worst-case recovery time. To enable the deadlock recovery with automated vehicles and with human-driven vehicles, A-DRIVE includes two components: V2V communication-based A-DRIVE and Local perception-based A-DRIVE. V2V communication-based A-DRIVE is designed for homogeneous traffic environments in which all the vehicles are connected and automated. Local perception-based A-DRIVE is for mixed traffic, where CAVs, non-connected automated vehicles, and human-driven vehicles co-exist and cooperate with one another. Since these two components are not exclusive, CAVs inclusively and seamlessly use them in practice. Finally, our simulation results show that A-DRIVE improves traffic throughput compared to a baseline protocol.

Adaptive Vehicular Communication using ALOHA CSMA with Modified Logistic Function (ALOHAMLF)

“…Vehicle-to-vehicle (V2V) communication has the potential to wirelessly transmit data regarding the speed and location using basic safety messages simultaneous use of services that reduce travel time, allow for cooperative autonomous driving, lower maintenance and infrastructure costs, improve energy efficiency, among other benefits. Data storage, privacy and security concerns, IoT sensor energy optimization in vehicles, legal uncertainty, and realtime big data analytics are main issues of developments nowadays [25][26][27][28][29][30].…”