ITS Berkeley

This paper examines the optimal velocity follow-the-leader dynamics, a microscopic traffic model, and explores different aspects of the dynamical model, with particular emphasis on collision analysis. More precisely, we present a rigorous boundary-layer analysis of the model which provides a careful understanding of the behavior of the dynamics in trade-off with the singularity of the model at collision.

This article provides an overview of the classical and new techniques in traffic flow control and estimations. The overview begins with a description of the most used traffic flow models for estimation and control. Then, it shifts towards using those models for traffic flow estimation using physics-informed machine learning techniques. Lastly, it focuses on traffic flow control describing the most classical techniques and the new advancement in traffic control using autonomous vehicles.

In this letter, we propose an integrated dynamical model of Connected and Automated Vehicles (CAVs) which incorporates CAV technologies and a microscopic car-following model to improve safety, efficiency, and convenience. We rigorously investigate the analytical properties such as well-posedness, maximum principle, perturbation, and stability of the proposed model in some proper functional spaces. Furthermore, we prove that the model is collision-free and derive an explicit lower bound on the distance as a safety measure.

The integration of Automated Vehicles (AVs) into traffic flow holds the potential to significantly improve traffic congestion by enabling AVs to function as actuators within the flow. This paper introduces an adaptive speed controller tailored for scenarios of mixed autonomy, where AVs interact with human-driven vehicles. We model the traffic dynamics using a system of strongly coupled Partial and Ordinary Differential Equations (PDE-ODE), with the PDE capturing the general flow of human-driven traffic and the ODE characterizing the trajectory of the AVs. A speed policy for AVs is derived...