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

**Authors:** Shunsuke Aoki,  Ragunathan,  Rajkumar  
**arXiv:** [2204.04910](https://arxiv.org/abs/2204.04910)  
**Date:** 2022-04-11  
**Summary page:** https://www.summarizepaper.com/fr/paper/2204.04910v1/

## 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 time-sensitive 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.