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Published: 2025-03-24

Deep Learning-Based Detection Systems for Autonomous Vehicles in Challenging Weather Conditions

Assistant Professor, Department of CSE-AIML, Guru Nanak Institutions Technical Campus, Telangana
Lecturer, Department of Computer Science, St. Francis College for Women, Begumpet, Hyderabad
3Associate Professor, Department of Electronics and Communication Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, Telangana
Assistant Professor, Department of ECE, Narayana Engineering College, Nellore.
Assistant Professor, Dept of CSE, Sri Padmavati Mahila Visva Vidyalayam, Tirupati - 517 502, AP
Senior Computer Vision Scientist, ChiStats Labs Private Limited, Pune.
Autonomous vehicles Weather detection systems Deep learning (DL) Transfer learning DAWN2020 dataset MCWRD2018 dataset Adverse weather conditions

Abstract

Weather detection systems (WDS) are essential in enhancing decision-making for autonomous vehicles, specifically under challenging and adverse weather conditions. Autonomous systems can effectively classify outdoor weather scenarios using deep learning (DL) techniques, allowing seamless adaptation to dynamic environmental changes. This study introduces a robust DL-driven framework to classify diverse weather conditions and aid autonomous vehicle navigation in typical and extreme scenarios. The proposed framework utilizes advanced transfer learning methods alongside a high-performance Nvidia GPU to evaluate the efficiency of three convolutional neural networks (CNNs): MobileNetV2, DenseNet121, and VGG-16. The experiments were conducted using two comprehensive weather imaging datasets, DAWN2020 and MCWRD2018, combined to classify six distinct weather categories: cloudy, rainy, snowy, sandy, sunny, and sunrise. Experimental outcomes showed outstanding performance for all models, with the MobileNetV2-based system performing the highest detection accuracy, precision, and sensitivity of 97.92%, 97.88%, and 97.95%, respectively. Furthermore, the framework achieved a rapid inference time, with an average processing speed of 7 milliseconds per inference using the GPU. Comparative analysis with existing models of the effectiveness of the presented approach showcases advancements in classification accuracy by a margin of 0.3% to 19.8%. These outcomes provide the framework's practicality classification to facilitate reliable decision-making for autonomous vehicles in diverse conditions.

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How to Cite

Teresa, M., Sreelakshmi Induri, DR. V. Kishen Ajay Kumar, Rayapudi Prashanthi, Dr. L. Jayasree, & M. Sudhakara. (2025). Deep Learning-Based Detection Systems for Autonomous Vehicles in Challenging Weather Conditions. International Journal of Interpreting Enigma Engineers (IJIEE), 2(1), 22–31. Retrieved from https://ejournal.svgacademy.org/index.php/ijiee/article/view/137

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