Cooperative Driving of Connected Autonomous vehicle using Responsibility Sensitive Safety Rules: A Control Barrier Functions Approach

Abstract

Connected Autonomous Vehicles (CAVs) are expected to enable reliable, efficient, and intelligent transportation systems. Most motion-planning algorithms for multi-agent systems implicitly assume that all vehicles/agents will execute the expected plan with a small error and evaluate their safety constraints based on this fact. This assumption, however, is hard to keep for CAVs since they may have to change their plan (e.g., to yield to another vehicle) or are forced to stop (e.g., a CAV may break down). While it is desired that a CAV never gets involved in an accident, it may be hit by other vehicles and, sometimes, preventing the accident is impossible (e.g., getting hit from behind while waiting at a red light). Responsibility-Sensitive Safety (RSS) is a set of safety rules that defines the objective of CAVs to blame, instead of safety. Thus, instead of developing a CAV algorithm that will avoid any accident, it ensures that the ego vehicle will not be blamed for any accident it is a part of. Original RSS rules, however, are hard to evaluate for merge, intersection, and unstructured road scenarios, plus RSS rules do not prevent deadlock situations among vehicles. In this article, we propose a new formulation for RSS rules 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. We use Control Barrier Functions to enforce safety constraints and compute the energy optimal trajectory for the ego CAV. Finally, to ensure liveness, our approach detects and resolves deadlocks in a decentralized manner. We have conducted different experiments to verify that the ego CAV does not cause an accident no matter when other CAVs slow down or stop. We also showcase our deadlock detection and resolution mechanism using our simulator. Finally, we compare the average velocity and fuel consumption of vehicles when they drive autonomously with the case that they are autonomous and connected.