AI-BASED DECISION-MAKING MODELS FOR AUTONOMOUS DRIVING IN URBAN ENVIRONMENTS

Аннотация

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. Keywords: Autonomous vehicles · Urban driving · AI-based decision-making · Deep learning · Reinforcement learning · Imitation learning 1.Introduction The rapid advancement in autonomous vehicle (AV) technologies has positioned intelligent mobility at the core of future transportation systems. Urban environments—dense, dynamic, and multifaceted—demand sophisticated systems that gobeyond rule-based driving. In such contexts, decision-making plays a pivotal role in ensuring safety, efficiency, and adaptability. As urbanization accelerates, the integration of artificial intelligence (AI) in AVs becomes essential forhandling thehigh variability oftraffic, infrastructure, and human behavior incities [1]. Urban drivingis uniquely challenging due to theinherent unpredictability ofpedestrian behavior, unstructured road scenarios, occlusions, frequent intersections, and inconsistent signage. Unlike controlled highway environments, cities involve dense traffic, vulnerable road users, and complex legal constraints. Traditional methods, such as finite state machines or rule-based logic, fail to generalizewell in these scenarios [2-3]. To navigate such complexity, AI-based decision-making models—including reinforcement learning, neuro-symbolic reasoning, and deep imitation learning—have emerged as powerful tools for enabling context-aware, real-time decisions in AVs. These models can learn optimal driving strategies through simulations, adapt to unfamiliar environments, and incorporate ethical and safety constraints [4-5]. Furthermore, the integration of IoT and edge computing allows decision-making modules to be decentralized, responsive, and adaptive to hyper-local changes [6]. This review aims to provide a comprehensive overview of the state-of-the-art AI-based decision- making approaches for autonomous driving in urban contexts. It highlights foundational models, http://www.internationaljournal.co.in/index.php/jasass 402 Volume15 Issue 08, August 2025 Impact factor: 2019: 4.679 2020: 5.015 2021: 5.436, 2022: 5.242, 2023: 6.995, 2024 7.75 emerging hybrid methods, benchmarking environments, and open research challenges, with the goal ofguiding futureresearch and deployment strategies for intelligent urban mobility systems. 2.Decision-Makingin UrbanAutonomous Driving Autonomous driving systems rely on multi-layered decision-making frameworks, typically categorized into three levels: strategic, tactical, and operational. The strategic level focuses on high-level planning such as destination selection and route optimization across a city. The tactical level handles short- to mid-range planning tasks like overtaking, lane changes, and intersection behavior. Lastly, the operational level involves real-time control, such as maintaining lane position, obeying traffic signals, and reacting to nearby vehicles or pedestrians [7-8]. Urban driving presents unique challenges for decision-making systems. Common tasks include lane changing in dense traffic, handling intersections with ambiguous right-of-way rules, and ensuring pedestrian and cyclist safety in dynamic environments. These scenarios often involve partial observability, delayed feedback, and multiple agents interacting concurrently [9]. As such, AVs mustoperatewith both predictive foresight and real-time adaptability. Traditional rule-based systems, while interpretable and easier to verify, struggle in urban contexts due to their rigidity and inability to generalize across novel situations. They are heavily reliant on pre-programmed logic and often fail in scenarios involving uncertainty or incomplete information. In contrast, AI-based approaches, including deep reinforcement learning, imitation learning, and neu