Dynamic and Systematic Survey of Deep Learning Approaches for Driving Behavior Analysis
This is an incremental survey paper that organizes existing research for researchers in driving behavior analysis.
The authors conducted a dynamic survey of 58 articles to review and classify deep learning methods for driving behavior analysis, aiming to provide a framework for future research to optimize driving and prevent issues like fatalities and energy waste.
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.