Cooperative driving of connected autonomous vehicles using responsibility-sensitive safety (RSS) rules
Journal
ICCPS 2021 - Proceedings of the 2021 ACM/IEEE 12th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2021)
Pages
11月20日
Date Issued
2021
Author(s)
Abstract
Connected Autonomous Vehicles (CAVs) are expected to enable reliable and efficient transportation systems. Most motion planning algorithms for multi-agent systems are not completely safe because they implicitly assume that all vehicles/agents will execute the expected plan with a small error. This assumption, however, is hard to keep for CAVs since they may have to slow down (e.g., to yield to a jaywalker) or are forced to stop (e.g. break down), sometimes even without a notice. Responsibility-Sensitive Safety (RSS) defines a set of safety rules for each driving scenario to ensure that a vehicle will not cause an accident irrespective of other vehicles' behavior. RSS rules, however, are hard to evaluate for merge, intersection, and unstructured road scenarios. In addition, deadlock situations can happen that are not considered by the RSS. In this paper, we propose a generic version of RSS rules for CAVs that can be applied to any driving scenario. We integrate the proposed RSS rules with the CAV's motion planning algorithm to enable cooperative driving of CAVs. Our approach can also detect and resolve deadlocks in a decentralized manner. We have conducted experiments to verify that a CAV does not cause an accident no matter when other CAVs slow down or stop. We also showcase our deadlock detection and resolution mechanism. Finally, we compare the average velocity and fuel consumption of vehicles when they drive autonomously but not connected with the case that they are connected. © 2021 ACM.
Subjects
city-wide traffic management; connected autonomous vehicles; intelligent transportation systems
SDGs
Other Subjects
Accidents; Embedded systems; Internet of things; Motion planning; Multi agent systems; Average velocity; Break down; Cooperative driving; Deadlock detection and resolution; Motion planning algorithms; Safety rules; Transportation system; Autonomous vehicles
Type
conference paper