# Using Visual and Vehicular Sensors for Driver Behavior Analysis: A Survey

arXiv: https://arxiv.org/abs/2308.13406 · HTML: https://mlsanity.com/paper/2308.13406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 57 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Driver Distraction Identification with an Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/1901.09097/) (2019)
- [Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset](https://mlsanity.com/paper/2004.05973/) (2020)
- [Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment](https://mlsanity.com/paper/1710.04133/) (2017)

## Built on by (most cited first, 1 of 1)

- [V-SenseDrive: A Privacy-Preserving Road Video and In-Vehicle Sensor Fusion Framework for Road Safety & Driver Behaviour Modelling](https://mlsanity.com/paper/2509.18187/) (2025)

## Abstract (arXiv)

Risky drivers account for 70% of fatal accidents in the United States. With recent advances in sensors and intelligent vehicular systems, there has been significant research on assessing driver behavior to improve driving experiences and road safety. This paper examines the various techniques used to analyze driver behavior using visual and vehicular data, providing an overview of the latest research in this field. The paper also discusses the challenges and open problems in the field and offers potential recommendations for future research. The survey concludes that integrating vision and vehicular information can significantly enhance the accuracy and effectiveness of driver behavior analysis, leading to improved safety measures and reduced traffic accidents.