Bridging the Gap Between Theory and Implementation for CAV-Based Mixed Traffic Smoothing

Principal Investigator(s):

Raphael Stern, Assistant Professor of Civil, Environmental, and Geo-Engineering – University of Minnesota
Ziran Wang, Assistant Professor of Civil Engineering – Purdue University

Project Abstract:
In the last decade, dozens of algorithms have been published that rely on using individual automated vehicles (AVs) to smooth traffic flow, reducing congestion and emissions. However, none of these algorithms have been implemented on production vehicles, begging the question, “Why?” One possibility is that much of the AV-based traffic flow smoothing theory is based on simplistic traffic models that do not hold up to reality, while another possibility is that vehicle-level delays make these strategies impractical to implement.

This project focuses on developing and validating advanced traffic-smoothing controllers for connected and automated vehicles (CAVs) to address critical safety and mobility challenges in mixed traffic environments. The research will address uncertainties in vehicle dynamics, actuation delays, and human compliance, leveraging high-resolution traffic data, cutting-edge simulation tools, and real-world testing on a state-of-the-art CAV platform at a professional test track in Indiana.

Institution(s): University of Minnesota
Purdue University

Award Year: 2025

Research Focus: Mobility

Project Form(s):