Causally Explainable and Responsible AI: Safety-Critical Intelligent Transportation Systems

Abstract

Artificial intelligence (AI) plays a critical role in improving safety across modern transportation systems, enabling collision prediction, cooperative perception, autonomous navigation, and real-time traffic control. However, many AI-enabled safety functions rely on closed-box models that create a significant responsibility gap in which automated decisions cannot be explained or justified. This gap is especially concerning in the safety applications of autonomous vehicles (AVs) and smart public transit systems, where incorrect or unjustified AI decisions may result in severe accidents or loss of life. Traditional interpretability approaches provide limited post hoc explanations that are insufficient for transportation safety certification or incident analysis. This article proposes a causal AI-driven framework for responsible, auditable, and safety-aligned intelligence in transportation systems. Using structural causal models (SCMs), counterfactual reasoning, and causal intervention analysis, the proposed framework provides root-cause attribution, fairness assurance, and audit-ready explanations across land, maritime, and air transportation systems. Two representative use cases are presented: the causal evaluation of vehicle-to-everything (V2X)-assisted collision avoidance and the causal justification of autonomous driving policies. Experimental validation confirms that the proposed causal framework reduces collision rates by 80.6% and maintains safety even under severe communication latencies and sensor noise. Furthermore, compared to existing probabilistic causal approaches, our proposed method achieves an optimal balance between safety and travel efficiency while operating at submillisecond latency. These results demonstrate that causally grounded intelligence is essential for public safety, system reliability, and regulatory compliance in next-generation transportation systems.