Enables interactive design, execution, and analysis of SUMO traffic simulations through natural language, providing tools for scenario generation, policy experimentation, result analysis, and visualization.
Integrates SUMO traffic simulation with the Model Context Protocol for autonomous driving applications, enabling simulation environment interaction and model context management.
Universal bridge between AI agents and Autoware autonomous driving stack, enabling AI-driven mission planning, real-time vehicle control, and adaptive decision-making.
Enables natural language-driven creation and execution of autonomous-vehicle scenarios in the CARLA simulator, with validated primitives and replay support.