Connected and Automated Vehicles

This paper develops an optimization-based control framework for a microscopic nonlinear car-following model. The controller is obtained from a Control Lyapunov Function-Control Barrier Function-Quadratic Programming framework that enforces stability, velocity feasibility, and collision-avoidance constraints while minimizing control effort. The resulting controller mitigates the limitations of the spacing-dependent singularity-based car-following models and guarantees closed-loop safety and stability.

Scaling pedestrian re-identification for autonomous driving is limited by the cost of identity labeling across large camera networks. Inspired by CLIP-based uncertainty modal modeling, this paper proposes an active learning approach that selects labeling candidates using uncertainty in the joint vision–language embedding space. The method combines (i) uncertainty sampling for ambiguous matches, (ii) diversity sampling based on embedding coverage, and (iii) batch acquisition with redundancy control. Experiments are conducted on a large-scale dataset with 400,000 images and 50,000 identities...

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...