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

arXiv: https://arxiv.org/abs/2109.08996 · HTML: https://mlsanity.com/paper/2109.08996/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 88 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Characterizing Driving Styles with Deep Learning](https://mlsanity.com/paper/1607.03611/) (2016)
- [Recurrent Neural Networks: An Embedded Computing Perspective](https://mlsanity.com/paper/1908.07062/) (2019)
- [Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic](https://mlsanity.com/paper/1902.05772/) (2019)
- [Spatial-temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition](https://mlsanity.com/paper/1812.00615/) (2018)

## Abstract (arXiv)

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.