Safety-Aware and Data-Driven Predictive Control for Connected Automated Vehicles at a Mixed Traffic Signalized Intersection

TL;DR

This paper introduces a predictive control framework for connected automated vehicles at signalized intersections, focusing on safety and real-time behavior prediction of human-driven vehicles to avoid collisions.

Contribution

It develops a real-time, safety-aware predictive control method that models human-driven vehicle behavior for improved CAV navigation at intersections.

Findings

Effective in avoiding rear-end collisions in simulations

Robust to variations in human driver behavior

Real-time behavior approximation enhances safety

Abstract

A typical urban signalized intersection poses significant modeling and control challenges in a mixed traffic environment consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs). In this paper, we address the problem of deriving safe trajectories for CAVs in a mixed traffic environment that prioritizes rear-end collision avoidance when the preceding HDVs approach the yellow and red signal phases of the intersection. We present a predictive control framework that employs a recursive least squares algorithm to approximate in real time the driving behavior of the preceding HDVs and then uses this approximation to derive safety-aware trajectory in a finite horizon. We validate the effectiveness of our proposed framework through numerical simulation and analyze the robustness of the control framework.