# A Machine Learning Smartphone-based Sensing for Driver Behavior Classification

arXiv: https://arxiv.org/abs/2202.01893 · HTML: https://mlsanity.com/paper/2202.01893/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 1 of 1)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)

## Built on by (most cited first, 2 of 2)

- [Efficient Network Representation for GNN-based Intrusion Detection](https://mlsanity.com/paper/2310.05956/) (2023)
- [Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer](https://mlsanity.com/paper/2310.13906/) (2023)

## Abstract (arXiv)

Driver behavior profiling is one of the main issues in the insurance industries and fleet management, thus being able to classify the driver behavior with low-cost mobile applications remains in the spotlight of autonomous driving. However, using mobile sensors may face the challenge of security, privacy, and trust issues. To overcome those challenges, we propose to collect data sensors using Carla Simulator available in smartphones (Accelerometer, Gyroscope, GPS) in order to classify the driver behavior using speed, acceleration, direction, the 3-axis rotation angles (Yaw, Pitch, Roll) taking into account the speed limit of the current road and weather conditions to better identify the risky behavior. Secondly, after fusing inter-axial data from multiple sensors into a single file, we explore different machine learning algorithms for time series classification to evaluate which algorithm results in the highest performance.