ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control

ReasonLight introduces a multimodal AI framework that enhances reinforcement learning for traffic signal control by integrating camera feeds, sensor data, and foundation models to handle rare events unseen during training. The system demonstrates zero-shot adaptation capabilities, reducing emergency vehicle response times by up to 88.7% without requiring model retraining.

ReasonLight addresses a fundamental limitation in applying reinforcement learning to real-world traffic systems: brittleness when encountering events outside the training distribution. Traditional RL-based traffic signal control relies on predefined state spaces, making these systems unable to respond intelligently to unexpected scenarios like emergency vehicles or temporary road closures. This research represents meaningful progress in making AI systems more robust and adaptable to open-world conditions.

The framework's innovation lies in its hybrid approach. Rather than replacing the RL backbone entirely, ReasonLight uses a foundation model to interpret multimodal sensor inputs—combining structured traffic data, camera imagery, and candidate RL decisions—to refine actions contextually. This architecture preserves the computational efficiency of RL while adding semantic reasoning capabilities. The fallback mechanism that validates actions against available phases ensures operational safety, addressing a critical requirement for autonomous traffic infrastructure.

For the transportation and smart city sectors, this work has substantial implications. Municipal infrastructure operators struggle with system adaptability, and solutions that improve safety without requiring frequent retraining reduce deployment friction and maintenance costs. The demonstrated 88.7% reduction in emergency response time directly translates to public safety improvements.

The research signals broader industry momentum toward hybrid AI systems that combine multiple neural architectures for reliability. Future development should focus on scaling this approach across diverse intersection types and geographic contexts, testing robustness against adversarial scenarios, and reducing computational overhead for real-time deployment at scale.

• →ReasonLight combines foundation models with RL to enable zero-shot adaptation to unseen traffic events without retraining.
• →Emergency vehicle waiting times decrease by up to 88.7% through semantic-guided action refinement and vision-based scene understanding.
• →The framework uses fallback mechanisms to reject invalid actions, ensuring operational safety and regulatory compliance.
• →Multimodal integration of camera feeds, sensor data, and RL decisions addresses the brittleness of traditional RL-based traffic control.
• →The approach reduces deployment friction by achieving adaptation without model retraining, lowering operational costs for municipalities.