ARTIFICIAL INTELLIGENCE FOR AUTONOMOUS VEHICLES: ANALYZING SAFETY, EFFICIENCY, AND ETHICAL IMPLICATIONS IN URBAN TRANSPORTATION SYSTEMS

Abstract

The rapid advancement of Artificial Intelligence has accelerated the development of autonomous vehicles, positioning them as a key component of future urban transportation systems. This study investigates the impact of AI-driven perception, decision-making, and control mechanisms on the safety, efficiency, and reliability of autonomous vehicles operating in complex urban environments. A mixed-methods experimental approach was employed, combining quantitative simulation-based evaluations with qualitative scenario analysis to assess system performance under diverse traffic, environmental, and operational conditions. The results indicate substantial reductions in collision rates, faster response times, improved traffic flow, and enhanced energy efficiency when compared to conventional driving models. Multi-sensor fusion and learning-based algorithms demonstrated strong robustness and adaptability, maintaining stable performance even in high-density and adverse scenarios. The findings further highlight the role of AI in enabling real-time, data-driven decisions that surpass human consistency while raising important ethical and societal considerations. Collectively, the results confirm that AI-enabled autonomous vehicles offer a viable pathway toward safer, more efficient, and sustainable urban mobility, provided that technical advancements are accompanied by ethical governance and supportive infrastructure planning.