Dynamic and Systematic Survey of Deep Learning Approaches for Driving Behavior Analysis

TL;DR

This paper systematically reviews deep learning methods for analyzing driving behavior, aiming to enhance safety and efficiency by classifying driving styles and their consequences through a comprehensive survey of existing research.

Contribution

It provides a dynamic, structured survey of 58 articles on deep learning approaches for driving behavior analysis, offering a classification framework and identifying research trends.

Findings

Classification of deep learning methods for driving behavior

Framework for analyzing driving behavior data

Identification of research trends and future directions

Abstract

Improper driving results in fatalities, damages, increased energy consumptions, and depreciation of the vehicles. Analyzing driving behaviour could lead to optimize and avoid mentioned issues. By identifying the type of driving and mapping them to the consequences of that type of driving, we can get a model to prevent them. In this regard, we try to create a dynamic survey paper to review and present driving behaviour survey data for future researchers in our research. By analyzing 58 articles, we attempt to classify standard methods and provide a framework for future articles to be examined and studied in different dashboards and updated about trends.