ARTIFICIAL INTELLIGENCE IN AUTONOMOUS VEHICLES: A COMPREHENSIVE REVIEW
Keywords:
Artificial Intelligence (AI), Autonomous Vehicles, Self-Driving Cars, Machine Learning, Deep Learning, Computer Vision, Sensor Fusion, LIDAR, Path Planning, Decision-Making Algorithms, Neural Networks, Vehicle ControlAbstract
The integration of Artificial Intelligence (AI) in autonomous vehicles represents a transformative leap in the transportation industry, enabling the development of self-driving systems capable of navigating complex environments with minimal human intervention. This comprehensive review explores the critical role of AI in advancing autonomous vehicles, focusing on key technologies such as machine learning, computer vision, sensor fusion, and decision-making algorithms. It highlights the synergy between AI and sensor systems, including LIDAR, radar, and cameras, to enhance real-time perception, obstacle detection, and vehicle control. Moreover, the review delves into AI-driven algorithms responsible for decision-making, path planning, and motion control, emphasizing how deep learning models and neural networks contribute to improved safety, efficiency, and user experience. It also addresses challenges such as regulatory frameworks, ethical concerns, and data privacy issues, which shape the development and deployment of autonomous vehicles. Furthermore, the paper discusses the implications of AI in reducing accidents, lowering carbon emissions, and reshaping urban mobility. Finally, the review anticipates future trends in AI-driven autonomous vehicles, such as advancements in vehicle-to-everything (V2X) communication, the incorporation of 5G technology, and the potential for fully autonomous systems in public transportation and delivery services. This review serves as a foundation for understanding the current state of AI in autonomous vehicles and its potential to revolutionize the future of transportation.
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