Enhancing Transportation Safety: An integrated Approach Using FLFS and OSNCA for Advanced Driving Behavior Analysis
Journal Title: MindVanguard: Beyond Behavior - Year 2023, Vol 1, Issue 1
Abstract
The assessment of driving behavior, vital for ensuring passenger safety and optimizing resource utilization in transportation systems, faces challenges due to inherent unpredictability and complexity. This study addresses these challenges by introducing innovative methodologies for the extraction, classification, and prediction of diverse driving patterns, utilizing data from "On Board Diagnostics" (OBD) ports in modern vehicles. In this approach, a comprehensive suite of advanced Machine Learning (ML) and Deep Learning (DL) stechniques, including Convolutional Neural Networks (CNNs), Optimized Spectral Neural Classification (OSNCA), and Fuzzy Logical Feature Selection (FLFS), are employed. These techniques are instrumental in overcoming limitations of previous models, enhancing accuracy in driving behavior evaluation. The utilization of FLFS in conjunction with OSNCA represents a novel method in driver behavior analysis. By applying these techniques, driver characteristics and behaviors are systematically categorized into distinct classes, facilitating a nuanced understanding of driving dynamics. The integration of these advanced methodologies not only furthers the analysis of driver behavior but also significantly improves classification and prediction capabilities. This research contributes to the development of safer, more efficient transportation networks by offering a refined approach to the analysis, categorization, and prediction of driver behavior, thereby advancing the field of driving behavior analysis.
Authors and Affiliations
Meenakshi Malik, Preeti Sharma*, Chander Prabha
Keywords
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- EP ID EP731816
- DOI https://doi.org/10.56578/mvbb010101
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