Authors

Rajesh Singh

Dept. of ECE, B.R.A. Bihar University, Muzzaffarpur, India

Pankaj Gupta

Guru Nanak Dev Engineering College, Ludhiana, India

Syed Anwar Basha

Guru Nanak Dev Engineering College, Ludhiana, India

Abstract

The incorporation of numerous sensors in vehicles enables the examination of interactions among road users. This is crucial for a variety of applications within vehicle contexts. In this regard, we present a novel training technique known as Sequential Training. This technique segments the layers of the Neural Network (NN) within the Deep Neural Network (DNN) architecture into two distinct groups. One group is customized for the user, while the other is intended to collaborate, concentrating on the road environment. We implement deep learning in scenarios where vehicle operators, each exhibiting distinct driving habits and styles, engage with their surroundings. It is essential to develop tailored models for each individual vehicle operator in every setting. This undertaking necessitates the gathering of pertinent data to train the machine learning models. Such data acquisition can be costly and, in numerous instances, may even be unfeasible. This methodology seeks to incorporate dynamic road condition sensors, including weather and real-time traffic information, to enhance the adaptability of scenario-specific layers.

Keywords

Intelligent Transport Systems Road Sensors Weather Data Traffic Monitoring Cloud Computing AI ANN

Citation of this Article

Rajesh Singh, Pankaj Gupta, & Syed Anwar Basha. (2026). Employing Dynamic Road Condition Sensors to Enhance the Flexibility of Scenario-Specific Layers in Intelligent Transport Systems. International Current Journal of Engineering and Science (ICJES), 5(1), 11-17. Article DOI: https://doi.org/10.47001/ICJES/2026.501003

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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