Searcharxiv⌕ Search

arXiv · 2610.28290

Distributed Platoon Control for Safe and Smooth Crossing at an Unsignalized Intersection-Part I: Stability Analysis

Abstract

We propose a distributed platoon controller that guarantees safe and smooth crossing of multi-vehicle systems at an unsignalized intersection. To support the stability analysis, we consider a multi-vehicle platoon maneuvering along a closed path with a single unsignalized intersection. Unlike traditional bi-level decoupled designs on open paths, which primarily focus on intersection control while neglecting its coupling with platoon convergence, and thus hindering rigorous convergence analysis, the closed-path setting allows us to analyze the asymptotic behavior of the platoon. The local exponential convergence analysis of the switched closed-loop system, induced by repeated vehicle switching at the intersection, is presented. Under some mild conditions on the controller gains, we show that safe intersection crossing is ensured during the transient phase and smooth crossing is achieved in steady state, where all vehicles travel at a common constant speed while avoiding collisions at the intersection. Finally, the effectiveness of the proposed distributed controller is demonstrated in a high-fidelity CARLA simulator using multiple commercial vehicle models.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bin-Bin Hu, Shuwei Pei, Saeed Ahmed, Bayu Jayawardhana. 2026-10-20. Distributed Platoon Control for Safe and Smooth Crossing at an Unsignalized Intersection-Part I: Stability Analysis. https://arxiv.org/abs/2610.28290

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

General-Sum Markov Games with Borel Spaces: Existence of Approximate Nash Equilibria via Finite Model Approximations under Setwise or Weak Continuity

Establishing the existence of exact or near Markov or stationary perfect Nash equilibria in nonzero-sum Markov games over Borel spaces is a challenging problem with limited positive results. Motivated by problems in multi-agent and Bayesian learning, this paper demonstrates the existence of approximate Markov and stationary Nash equilibria for such games under relatively mild regularity conditions; notably under setwise continuity. Our approach is constructive: For both compact and non-compact state spaces, we approximate the Borel model with finite state-action models and show that their equilibria correspond to \(ε\)-equilibria for the original game. Compared with previous results in the literature, which we comprehensively review, we provide more general conditions, along with explicit approximation models whose equilibria are $ε$-equilibria for the original model. For completeness, we also study the approximation of zero-sum Markov games and Markov teams to highlight the key differences between zero-sum and nonzero-sum settings. In particular, while for zero-sum and team games, joint weak (Feller) continuity of the transition kernel is sufficient (as the value function is continuous), this is not the case for general nonzero-sum games.

eess.SY↗

Estimating Density Functions for Probabilistic Power Flow Using Invertible Neural Networks

Probabilistic power flow (PPF) is essential for quantifying operational uncertainty in modern power systems with high penetrations of renewable generation and flexible loads. Conventional PPF methods primarily rely on Monte Carlo (MC)based power flow (PF) simulations or simplified approximations of voltage probability density functions. Although MC methods provide high accuracy, they incur substantial computational and data-storage costs, whereas simplified approximations, though lightweight, often sacrifice accuracy due to the reliance of certain assumption. In this paper, we propose a novel PPF density approximation framework that leverages the expressive power of neural networks (NNs) and can, in principle, approximate complex voltage distributions directly without restrictive assumptions. The core idea is to learn an explicit mapping between stochastic power injections and system voltages using invertible neural networks (INNs), and then to apply the change-of-variables theorem to obtain the voltage densities. Extensive numerical studies demonstrate that the proposed framework achieves stateof-the-art performance both as an accurate PF solver and as an efficient PPF density estimator

eess.SY↗

Implicit Predecessor-Based Region of Attraction Estimation and Robust Invariance Analysis for a Two-Wheeled Inverted Pendulum

Estimating the region of attraction (RoA) of nonlinear systems is fundamental for assessing closed-loop stability and ensuring safe operation. While Lyapunov-based approaches provide certified stability guarantees, they often yield conservative inner approximations of the RoA. This paper combines a certified Lyapunov-based positively invariant set with a predecessor-based implicit representation to compute a significantly less conservative inner approximation of the RoA while preserving formal stability guarantees. In addition, the robust positive invariance of the initial certified Lyapunov-based invariant set is analyzed under bounded additive input disturbances, providing formal robustness guarantees. The proposed methodology is demonstrated on a nonlinear two-wheeled inverted pendulum stabilized by a saturated linear quadratic regulator. The resulting RoA approximation is compared with the initial Lyapunov-certified invariant set and validated through Monte Carlo simulations and hardware experiments, showing a substantially enlarged certified operating region that matches the empirical closed-loop behavior. These results demonstrate the practical applicability of combining certified Lyapunov analysis with predecessor-based set propagation for RoA approximation and robustness assessment of nonlinear systems.

eess.SY↗