CorrA: Leveraging Large Language Models for Dynamic Obstacle Avoidance of Autonomous Vehicles

CorrA: Leveraging Large Language Models for Dynamic Obstacle A voidance of Autonomous V ehicles Shanting Wang 1, Panagiotis Typaldos 2 and Andreas A. Malikopoulos 3 Abstract -- In this paper, we present Corridor-Agent (CorrA), a framework that integrates large language models (LLMs) with model predictive control (MPC) to address the challenges of dynamic obstacle avoidance in autonomous vehicles. Our approach leverages LLM reasoning ability to generate appropriate parameters for sigmoid-based boundary functions that define safe corridors around obstacles, effectively reducing the state-space of the controlled vehicle. The proposed framework adjusts these boundaries dynamically based on real-time vehicle data that guarantees collision-free trajectories while also ensuring both computational efficiency and trajectory optimality. The problem is formulated as an optimal control problem and solved with differential dynamic programming (DDP) for constrained optimization, and the proposed approach is embedded within an MPC framework. Extensive simulation and real-world experiments demonstrate that the proposed framework achieves superior performance in maintaining safety and efficiency in complex, dynamic environments compared to a baseline MPC approach. I NTRODUCTION The rapid development of advanced sensing, computation, and artificial intelligence technologies has made autonomous vehicles (A Vs) more realistic and made related studies unprecedented. However, the complexity, dynamics, and unpredictability of real-world environments have impeded the deployment of A V applications. Until A Vs dominate the transportation market, we face the challenge of mixed autonomy systems where A Vs and human-driven vehicles (HDVs) must coexist safely.