Connected and Automated Vehicles

This paper proposes a deep learning-based Kazantzis–Kravaris–Luenberger (KKL) observer design to estimate position and speed in mixed-autonomy traffic environments. The approach relies on position measurements of vehicles surrounding the autonomous vehicle (AV), obtained through remote sensing, resulting in a subsequent time delay due to communication latency. The proposed deep learning KKL observer is designed to compensate for this delay and to ensure global asymptotic convergence of the estimation of position and speed by using a chain of sub-observers. We employ an unsupervised...

In this paper we propose a model for mixed traffic composed of few Connected and Automated Vehicles (CAVs) in the bulk flow. We rely on a multi-scale approach, coupling a Partial Differential Equation describing the overall traffic flow and Ordinary Differential Equations accounting for CAV trajectories, which act as moving bottlenecks on the surrounding flux. In our framework, CAVs are allowed to overtake (if on different lanes) or merge (if on the same lane). Controlling CAV desired speeds allows to act on the system to minimize any traffic density dependent cost function. More precisely...

The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the...

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.