Safe Optimal Interactions Between Automated and Human-Driven Vehicles in Mixed Traffic with Event-triggered Control Barrier Functions

TL;DR

This paper presents a novel framework using event-triggered Control Barrier Functions to enable safe and robust interactions between automated and human-driven vehicles in mixed traffic, even with unknown human driver behaviors.

Contribution

It introduces an online model estimation method and data-driven safety controllers for CAVs, transforming control problems into event-triggered quadratic programs for safety assurance.

Findings

Ensures collision-free interactions in mixed traffic scenarios.

Demonstrates robustness across different human driver behaviors.

Guarantees safety with human-in-the-loop interactions.

Abstract

This paper studies safe driving interactions between Human-Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) in mixed traffic where the dynamics and control policies of HDVs are unknown and hard to predict. In order to address this challenge, we employ event-triggered Control Barrier Functions (CBFs) to estimate the HDV model online, construct data-driven and state-feedback safety controllers, and transform constrained optimal control problems for CAVs into a sequence of event-triggered quadratic programs. We show that we can ensure collision-free between HDVs and CAVs and demonstrate the robustness and flexibility of our framework on different types of human drivers in lane-changing scenarios while guaranteeing safety with human-in-the-loop interactions.