ManeuverGPT

Agentic Control for Safe Autonomous Stunt Maneuvers

Agentic Control Diagram   |   Maneuver Phases Overview

📣 Announcement:

Paper Accepted to IROS 2025! 🎉

We are excited to announce that our paper has been accepted for publication at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2025, and it has been selected for an oral presentation! We appreciate your interest in our work!

Overview

ManeuverGPT is an Agentic framework for generating and executing high-dynamic stunt maneuvers in autonomous vehicles using Large Language Model (LLM)-based agents as controllers. This repository provides the implementation of ManeuverGPT, including its multi-agent architecture, control pipeline, and experimental evaluations in the CARLA simulator.

Key Features

• Agentic Architecture: Comprises three specialized LLM-driven agents:
  • Query Enricher Agent: Contextualizes user commands for maneuver generation.
  • Driver Agent: Generates maneuver parameters based on enriched queries.
  • Parameter Validator Agent: Enforces physics-based and safety constraints.
• High-Dynamic Maneuver Execution: Enables vehicles to perform complex stunt maneuvers such as J-turns with textual prompt-based control.
• Simulation-Based Evaluation: Tested in CARLA v0.9.14 to ensure maneuver feasibility across different vehicle models.
• Adaptive Prompting Mechanism: Allows maneuver refinement without requiring retraining of model weights.
• Multi-Agent Collaboration: Improves execution success and precision compared to single-agent approaches.

Installation

Prerequisites

• Python 3.10+
• CARLA Simulator v0.9.14
• Chat Completion-compatible LLM API (e.g., GPT-4o, etc.)

Setup

Clone the repository and install dependencies:

sh git clone https://github.com/SHi-ON/ManeuverGPT.git cd ManeuverGPT uv sync

Ensure CARLA is installed and running before executing the scripts.

Running Experiments

J-Turn Execution

To execute a J-turn maneuver in the CARLA simulation environment:

sh python src/maneuvergpt/carla/drive.py --iterations 100

For additional parameters, refer to the help documentation:

sh python src/maneuvergpt/carla/drive.py --help

Citation

If you use ManeuverGPT in your research, please cite:

bibtex @article{Azdam_ManeuverGPT_Agentic_Control_2025, author = {Azdam, Shawn and Doma, Pranav and Arab, Aliasghar Moj}, journal = {arXiv preprint arXiv:2503.09035}, title = {{ManeuverGPT Agentic Control for Safe Autonomous Stunt Maneuvers}}, url = {https://arxiv.org/abs/2503.09035}, year = {2025} }

License

This project is licensed under the CC BY 4.0.