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Title	Iot-Based Autonomous Vehicle System For Maintaining Driving Safety And Comfortability Based On Machine Learning Techniques
ID_Doc	33954
Authors	Kavitha D.; Ravikumar S.; Naghul Pranav K.R.
Year	2025
Published	Digital Twin and Blockchain for Smart Cities
DOI	http://dx.doi.org/10.1002/9781394303564.ch26
Abstract	The increase of autonomous vehicle (AV) in modern time makes smart digital world and technologies to enhance the performance of driving and self-driving through safety and security. AVs have become a heart of smart city and smart highway, but still, multiple challenges occurs during the designing of autonomous system. Internet traffic (IOV) defines comprehensive communication between drivers and innocent vehicles. Allows the IOV machine to share multiple data such as touch data, risk data, environment vision data, and localization data. The road accident leads there is no more safe and proper distance between the AVs. In this paper, we emerge to speak to some technological challenge such as latency, network bandwidth, and security issues on AVs. We proposed a novel machine learning techniques for IoT-based autonomous vehicle system (MLAV) to improve the performance and quality in safest driving. First, we propose a modified social group optimization-based data analytics technique to make proper data collection and grouping, which improves the predication rate. Second, we illustrate an improved swarm-based decision tree (ISDT) technique to detect abnormality on road such as objects on the road, obstacles, traffic, traffic lights, emergency working areas, pedestrians, nearby vehicles information and weather report. Then, a binary dragonfly optimization algorithm with deep neural network (MO- DNN) is proposed to provide optimal decisions as control strategy for AVs to compute the safe, convenient and economical path for travel. Finally, the performance of proposed MLAV system can compare with existing state-of-art systems in terms of accuracy, precession, sensitivity, specificity, recall and F-calculate. © 2024 Scrivener Publishing LLC.
Author Keywords	autonomous vehicles; Bigdata analytics; IoT; safety; security

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