Empowering Vehicle: The Impact of Deep and Reinforcement Learning in IoV - Motashim Rasool; Uvais Ahmad; Rizwan Akhtar; Shamim Ansari; Saumya Singh (International Journal of Advanced Robotics and Automation Technology, 2025)
Deep learning and reinforcement learning represent two pivotal pillars within the realm of artificial intelligence and machine learning, bearing transformative potential in the domain of the Internet of Vehicles (IoV). This abstract explores the multifaceted applications of these cutting-edge techniques within the IoV framework. Deep learning, exemplified by convolution neural networks (CNNs) and recurrent neural networks (RNNs), empowers IoV systems with the prowess to discern complex patterns in sensory data. This capability finds utility in tasks ranging from object recognition and lane detection for autonomous driving to emotion recognition in drivers. Furthermore, deep learning fuels driver assistance systems and enhances user interaction within the vehicle, encompassing voice and gesture control, facial recognition, and natural language interfaces. In parallel, reinforcement learning emerges as a potent paradigm for optimizing decision-making processes in IoV applications. Autonomous vehicles leverage reinforcement learning to navigate intricate traffic scenarios and make real-time driving decisions, mitigating risks and enhancing safety. Moreover, reinforcement learning drives energy-efficient routing for electric vehicles and facilitates dynamic pricing strategies for ride-sharing services, attuning them to ever-changing demand dynamics. As the IoV landscape evolves, deep learning and reinforcement learning stand as foundational cornerstones, propelling innovations that promise to reshape the future of transportation, rendering it safer, more efficient, and user-centric.
Keywords: : Internet of vehicle; Deep Learning; convolution neural network; generative adversial network; recurrent neural network; reinforcement learning; siamese network