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

arXiv: https://arxiv.org/abs/2503.02076 · HTML: https://mlsanity.com/paper/2503.02076/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Large language models for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Placed in

- End-to-end autonomous driving / [Large language models for driving](https://mlsanity.com/driving/large-language-models-for-driving/)

## Builds on (most cited first, 24 of 24)

- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models](https://mlsanity.com/paper/2402.12289/) (2024)
- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving](https://mlsanity.com/paper/2310.01957/) (2023)
- [DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2309.16292/) (2023)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020)
- [Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles](https://mlsanity.com/paper/2309.10228/) (2023)
- [Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving](https://mlsanity.com/paper/2310.02251/) (2023)
- [Driving Everywhere with Large Language Model Policy Adaptation](https://mlsanity.com/paper/2402.05932/) (2024)
- [TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models](https://mlsanity.com/paper/2309.06719/) (2023)
- [A Decentralized Energy-Optimal Control Framework for Connected Automated Vehicles at Signal-Free Intersections](https://mlsanity.com/paper/1602.03786/) (2016)
- [Control of Connected and Automated Vehicles: State of the Art and Future Challenges](https://mlsanity.com/paper/1804.03757/) (2018)
- [On Team Decision Problems with Nonclassical Information Structures](https://mlsanity.com/paper/2101.10992/) (2021)
- [A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways](https://mlsanity.com/paper/2109.11672/) (2021)
- [Optimal Time Trajectory and Coordination for Connected and Automated Vehicles](https://mlsanity.com/paper/2003.12183/) (2020)
- [Distributed Differential Dynamic Programming Architectures for Large-Scale Multi-Agent Control](https://mlsanity.com/paper/2207.13255/) (2022)
- [Optimization-Based Path-Planning for Connected and non-Connected Automated Vehicles](https://mlsanity.com/paper/2104.06778/) (2021)
- [A Research and Educational Robotic Testbed for Real-time Control of Emerging Mobility Systems: From Theory to Scaled Experiments](https://mlsanity.com/paper/2109.05370/) (2021)
- [Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization](https://mlsanity.com/paper/2403.04881/) (2024)
- [A Decentralized Time- and Energy-Optimal Control Framework for Connected Automated Vehicles: From Simulation to Field Test](https://mlsanity.com/paper/1911.01380/) (2019)
- [Optimal Weight Adaptation of Model Predictive Control for Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimization](https://mlsanity.com/paper/2210.00700/) (2022)
- [Modified Dynamic Programming Algorithms for GLOSA Systems with Stochastic Signal Switching Times](https://mlsanity.com/paper/2211.12159/) (2022)

## Built on by (most cited first, 1 of 1)

- [Closed-Loop Refinement and Execution for Learned Driving Planners](https://mlsanity.com/paper/2610.00992/) (2026)

## Abstract (arXiv)

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.