arXiv:2607.03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control. However, in safety-critical infrastructure like traffic control, the opaque, black-box nature of deep RL models poses challenges for transportation agency acceptance, regulatory compliance, operational trust, troubleshooting, and fine-tuning. To bridge this gap between high-performance optimization and human-comprehensible interpretability, this effort introduces a novel, explainable entity centric RL framework for safe and transparent traffic sign
The increasing sophistication of AI models for critical infrastructure demands explainability, aligning with a broader regulatory push towards transparent AI.
Improving transparency in AI systems for public infrastructure will accelerate adoption and trust, addressing key hurdles for practical deployment in sensitive areas.
Traffic management systems can now leverage advanced AI while simultaneously meeting human interpretability and regulatory compliance requirements.
- · Transportation agencies
- · Smart city technology providers
- · AI explainability researchers
- · Urban commuters
- · Developers of opaque black-box AI for critical infrastructure
- · Legacy traffic control systems
Wider deployment of AI in critical public infrastructure due to increased trust and accountability.
Establishment of new regulatory frameworks and certification standards for explainable AI in safety-critical applications.
Extension of explainable AI requirements to other public services, fostering a new standard for AI integration in government operations.
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Read at arXiv cs.AI