The use of artificial intelligence (AI) in transportation systems is a revolution towards addressing some of the issues of urbanization, road safety, and sustainability. The present review critically examines the technology, infrastructure, and societal dimensions of transformation of AI-based transportation systems. On the technology front, we explore autonomous vehicle systems development (SAE Levels 0-5), with a focus on multi-sensor fusion progress, real-time edge computing for decision-making, and path prediction algorithms based on deep learning. The paper critically discusses facilitative technologies like next-gen ADAS, V2X comms protocols, and AI-optimized energy management systems for EV fleets. Going beyond the tech breakthroughs, the research investigates the systemic impacts of intelligent transportation solutions. We present empirical evidence on: (1) traffic stream optimization through adaptive traffic control systems lowering congestion by 15-30% in pilot cities, (2) enhanced safety through collision prediction systems being >90% accurate in simulation conditions, and (3) environmental benefits from AI-optimized routing lowering fuel consumption by 8-12%. The analysis also addresses implementation issues like sensor faithfulness in adverse weather, vehicular networks' latency constraint, and the "black box" problem in AI decision-making. The review also identifies urgent policy questions regarding data governance frameworks, cyber threats to internet-of-things infrastructure, and workforce transition mechanisms for affected industries. Through global case studies of Singapore's intelligent transportation system to California's self-driving car test roads, we extract best practices for responsible rollout. The report concludes with an agenda for research into explainable AI for safety-critical application, extensive testing practice for edge cases, and inclusive design principles to guarantee equal access for next-generation mobility solutions.
Ali et al. (Wed,) studied this question.
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