A Barrier-Certified Optimal Coordination Framework for Connected and Automated Vehicles

TL;DR

This paper presents a comprehensive, barrier-certified control framework for connected and automated vehicles at intersections, ensuring safety constraints are maintained through real-time optimization and control barrier functions.

Contribution

It introduces a safety layer using control barrier functions integrated into an existing coordination framework, enabling real-time safe vehicle trajectory tracking.

Findings

Effective safety guarantees demonstrated in simulations

Real-time quadratic program efficiently enforces safety constraints

Framework extends previous methods with barrier certification

Abstract

In this paper, we extend a framework that we developed earlier for coordination of connected and automated vehicles (CAVs) at a signal-free intersection by integrating a safety layer using control barrier functions. First, in our motion planning module, each CAV computes the optimal control trajectory using simple vehicle dynamics. The trajectory does not make any of the state, control, and safety constraints active. A vehicle-level tracking controller employs a combined feedforward-feedback control law to track the resulting optimal trajectory from the motion planning module. Then, a barrier-certificate module, acting as a middle layer between the vehicle-level tracking controller and physical vehicle, receives the control law from the vehicle-level tracking controller and using realistic vehicle dynamics ensures that none of the state, control, and safety constraints becomes active. The latter is achieved through a quadratic program, which can be solved efficiently in real time. We demonstrate the effectiveness of our extended framework through a numerical simulation.