Beaune, Charlotte (LS2N (UMR CNRS 6004) École Centrale de Nantes), Héry, Elwan (LS2N (UMR CNRS 6004) École Centrale de Nantes), FREMONT, Vincent (Ecole Centrale de Nantes, CNRS, LS2N, UMR 6004)

Time-To-Collision Constrained NMPC for Connected and Autonomous Vehicles

Scheduled for presentation during the Regular Session "S41b-Motion Planning, Trajectory Optimization, and Control for Autonomous Vehicles" (FR-EA-T41), Friday, November 21, 2025, 13:50−14:10, Broadbeach 1&2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

This paper presents a Time-to-Collision (TTC) constrained Nonlinear Model Predictive Control (NMPC) framework for Connected and Autonomous Vehicles (CAVs) navigating in dynamic urban environments. The proposed method integrates V2X (Vehicle-to-Everything) communication and probabilistic obstacle modeling to enhance safety and reactivity in the presence of uncertain dynamic obstacles. Trajectories communicated by CAVs are directly incorporated into the NMPC optimization loop, while non-connected agents are modeled with uncertainty-aware predictions. The controller incorporates both geometric and time-based safety constraints, notably through TTC metrics and confidence ellipses. We validate our approach through a series of Hardware-in-the-Loop (HIL) real-world experiments using ROSBOT 2R platforms within a centralized ROS2 architecture.