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Cloud-Based Data Analytics for Autonomous Vehicle Performance Using Neural Networks
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Author(s): Delshi Howsalya Devi (Karpaga Vinayaga College of Engineering and Technology, India), P. Santhosh Kumar (SRM Institute of Science and Technology, Ramapuram, India), M. Aruna (SRM Institute of Science and Technology, India)and S. Sharmila (Karpaga Vinayaga College of Engineering and Technology, India)
Copyright: 2024
Pages: 23
Source title:
Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs)
Source Author(s)/Editor(s): Sivaram Ponnusamy (Sandip University, Nashik, India), Jilali Antari (Ibn Zohr Agadir University, Morocco), Pawan R. Bhaladhare (Sandip University, Nashik, India), Amol D. Potgantwar (Sandip University, Nashik, India)and Swaminathan Kalyanaraman (Anna University, Trichy, India)
DOI: 10.4018/979-8-3693-3597-0.ch005
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Abstract
By utilizing powerful analytical tools and remote computing capabilities, cloud-based data analytics significantly improve the operational efficiency of autonomous cars. Under this model, sensor readings, position data, and system diagnostics among the massive volumes of data produced by autonomous vehicles are sent to a cloud network for immediate analysis. This makes it possible to extract insightful information and trends that improve efficiency, safety, and performance of vehicles. Cloud-based methodology provides scalability, which enables smooth management of substantial datasets, and fosters cooperative endeavours in optimizing algorithms and models for self-governing systems. Analysis of information, machine learning algorithms, and communication are important components of this architecture that work together to enable the ongoing development and enhancement of autonomous vehicle capabilities. In the end, this cutting-edge method enables self-driving cars to negotiate intricate situations with improved decision-making skills, resulting in safer and more dependable driving.
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