Artificial Intelligence Applications in Autonomous Vehicles Navigating the Future of Transportation Systems

¹ Assistant Professor, Department of Electrical Engineering, Yeshwatrao Chavan College of Engineering, Wanadongri, Hingna, Maharashtra, India
² Professor, Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, Puduvoyal, Thiruvallur, Tamil Nadu, India
³ Professor/Mechanical, J.J.College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India
⁴ Associate Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
⁵ Assistant Professor, School of Electrical & Electronics Engineering, REVA University Bengaluru, Karnataka, India
⁶ Assistant Professor, Department of EEE, New Prince Shri Bhavani College of Engineering and Technology Chennai, Tamil Nadu, India

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Abstract

AI technology is advancing rapidly and is being applied to the autonomous vehicle industry to provide safer, more efficient, and more sustainable transport. While advancements have been substantial, there remain issues surrounding validation in the real world, computational expense, adaptability and cybersecurity and ethical issues. The authors of the study “Applications of Artificial Intelligence in Autonomous Vehicles to Overcome Existing Limitations and Improve Future Transportation Systems” suggest an innovative AI-oriented route optimization, motion tracking framework for autonomous vehicles. The framework brings autonomous driving from highways to cities by combining deep learning with reinforcement learning and hybrid AI models. Accurate perception, object detection, and collision avoidance in complex traffic situations depend on sensor fusion of LiDAR, radar, and high-tech computer vision. Additionally, AI-powered cybersecurity mechanisms fortify shields against cyber-attacks, ensuring ethical and transparent decision-making in driving automation applications. The study additionally discusses resource-efficient AI models tailored for low-power hardware, broadening the reach and sustainability of autonomous technology. Unlike prior works which are conditioned on simulations, this work transitions to freeway test data and multiple datasets to increase adaptability to different geographies and weather conditions. Another predictive AI-based traffic management system reduces the congestion and optimizes the V2V and V2I communication. This work is, ultimately, what will enable Level 4 and Level 5 autonomy, bringing theoretical AI into practical applications in road operations. These results have major implications for smart cities, autonomous logistics, ride-sharing services and sustainable mobility leading to a vision of the future where AI powered vehicles will make roads safer, reduce congestion and dramatically reshape global transportation systems.