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Sustainable Solutions for Autonomous Vehicles Using Deep Learning and IoT
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Author(s): Shantala Devi Patil (School of Computer Science and Engineering, REVA University, Bengaluru, India), Jerzy Ryszard Szymański (Department of Electrical Drives and Industrial Electronics, Casimir Pulaski University of Radom, Poland), Marta Żurek-Mortka (Department of Control Systems, Lukasiewicz Research Network Institute for Sustainable Technologies, Poland)and Mithileysh Sathiyanarayanan (MIT Square, London, UK)
Copyright: 2025
Pages: 26
Source title:
Enhancing Data-Driven Electronics Through IoT
Source Author(s)/Editor(s): Bhagwan Das (Centre for Artificial Intelligence Research and Optimization (AIRO), Torrens University Australia, Australia), Muhammad Zakir Shaikh (Universidad de Málaga, Spain), Samreen Hussain (Dawood University of Engineering and Technology, Pakistan)and Enrique Nava Baro (Universidad de Málaga, Spain)
DOI: 10.4018/979-8-3693-5448-3.ch016
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Abstract
The rise of self-driving cars shows promise to make getting around easier, but also brings big challenges when it comes to being eco-friendly. This paper lays out a plan to create lasting solutions for self-driving cars by combining deep learning, the internet of things (IoT), and car networks. This approach aims to boost efficiency cut down on energy use, and make driving safer and more productive. IoT allows cars, roads, and checkpoints to share info in real time, which helps streamline travel and reduce harm to the environment. The car network ensures that self-driving cars stay and linked to all parts of their ecosystem. This paper looks at how these technologies can work together in the self-driving car industry to tackle green issues like using less energy, lowering carbon output, and using materials more . It zeros in on how this tech can shape what's to come.
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