# A Machine Learning Approach for Driver Identification Based on CAN-BUS Sensor Data

arXiv: https://arxiv.org/abs/2207.10807 · HTML: https://mlsanity.com/paper/2207.10807/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 16 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Know Your Master: Driver Profiling-based Anti-theft Method](https://mlsanity.com/paper/1704.05223/) (2017)
- [Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network](https://mlsanity.com/paper/1911.08030/) (2019)

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

- [TEMPEST: Temporal Embeddings for Scalable Driver Identification via Angular Margin Learning](https://mlsanity.com/paper/2610.06855/) (2026)

## Abstract (arXiv)

Driver identification is a momentous field of modern decorated vehicles in the controller area network (CAN-BUS) perspective. Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-BUS but there are some difficulties because of the variation of the protocol of different models of vehicle. Our aim is to identify the driver through supervised learning algorithms based on driving behavior analysis. To determine the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from the CAN-BUS sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II, drive identification is possible. However, we have gained two types of accuracy on a complete data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to the higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm