AI and the future of intelligent transportation: Safety, equity and system-level innovation

This article explores the transformative impact of artificial intelligence on automotive safety and efficiency, examining the multifaceted integration of AI technologies across the transportation ecosystem. The article provides a comprehensive analysis of AI-enhanced safety systems, including sensor fusion architectures, machine learning approaches in collision avoidance, and driver monitoring technologies, while quantifying their efficacy in reducing accidents across various driving environments. It further investigates how AI enables performance optimization through energy management systems in electric vehicles, predictive maintenance frameworks, and real-time navigation solutions. The technical challenges and solutions in autonomous driving are examined through the lens of deep learning architectures, reinforcement learning approaches, and simulation environments. The article also addresses critical regulatory and infrastructural considerations necessary for widespread AI adoption in transportation, highlighting the complex interplay between technical capabilities, legal frameworks, and ethical questions. Finally, it proposes integrated approaches for a sustainable mobility future, emphasizing interdisciplinary collaboration models, equity considerations in technological deployment, and identifying research gaps requiring further investigation. This article provides stakeholders with a holistic understanding of how AI is reshaping transportation while illuminating pathways toward more intelligent, efficient, and equitable mobility systems.