AI-BASED DECISION-MAKING MODELS FOR AUTONOMOUS DRIVING IN URBAN ENVIRONMENTS
Keywords:
Autonomous vehicles · Urban driving · AI-based decision-making · Deep learning · Reinforcement learning · Imitation learningAbstract
Autonomous driving in urban environments presents a uniquely complex challenge due to dynamic traffic patterns, dense infrastructure, and the unpredictability of human agents such as pedestrians and cyclists. This review explores the growing role of artificial intelligence (AI) in addressing the decision-making demands of urban autonomous vehicles (AVs). We categorize key AI-based approaches—deep learning, reinforcement learning, imitation learning, multi-agent models, and hybrid systems—and analyse their applications, strengths, and limitations in real-world scenarios. The paper also examines foundational tools, including simulation platforms (CARLA, SUMO, AirSim), benchmark datasets (KITTI, nuScenes, Waymo), and insights from industry leaders like Waymo, Baidu Apollo, and Tesla. Core challenges such as safety validation, data scarcity for rare events, interpretability, and ethical considerations are critically discussed. Finally, we outline future directions involving 5G/6G integration, digital twins, and human-centered AI to support scalable, reliable, and transparent decision-making in urban autonomy. This review serves as a comprehensive foundation for researchers and practitioners aiming to advance the next generation of intelligent urban mobility systems.
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