# Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment

arXiv: https://arxiv.org/abs/1710.04133 · HTML: https://mlsanity.com/paper/1710.04133/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2017.

## Built on by (most cited first, 8 of 8)

- [Using Visual and Vehicular Sensors for Driver Behavior Analysis: A Survey](https://mlsanity.com/paper/2308.13406/) (2023)
- [Unsupervised Driver Behavior Profiling leveraging Recurrent Neural Networks](https://mlsanity.com/paper/2108.05079/) (2021)
- [Driving Anomaly Detection Using Conditional Generative Adversarial Network](https://mlsanity.com/paper/2203.08289/) (2022)
- [Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training](https://mlsanity.com/paper/2205.07870/) (2022)
- [A Machine Learning-based Algorithm for Automated Detection of Frequency-based Events in Recorded Time Series of Sensor Data](https://mlsanity.com/paper/2310.10841/) (2023)
- [Flash-Fusion: Enabling Expressive, Low-Latency Queries on IoT Sensor Streams with LLMs](https://mlsanity.com/paper/2511.11885/) (2025)
- [Hybrid Approach for Driver Behavior Analysis with Machine Learning, Feature Optimization, and Explainable AI](https://mlsanity.com/paper/2601.03477/) (2026)
- [Foundation CAN LM: A Pretrained Language Model For Automotive CAN Data](https://mlsanity.com/paper/2602.00866/) (2026)

## Abstract (arXiv)

Cars can nowadays record several thousands of signals through the CAN bus technology and potentially provide real-time information on the car, the driver and the surrounding environment. This paper proposes a new method for the analysis and classification of driver behavior using a selected subset of CAN bus signals, specifically gas pedal position, brake pedal pressure, steering wheel angle, steering wheel momentum, velocity, RPM, frontal and lateral acceleration. Data has been collected in a completely uncontrolled experiment, where 64 people drove 10 cars for or a total of over 2000 driving trips without any type of pre-determined driving instruction on a wide variety of road scenarios. We propose an unsupervised learning technique that clusters drivers in different groups, and offers a validation method to test the robustness of clustering in a wide range of experimental settings. The minimal amount of data needed to preserve robust driver clustering is also computed. The presented study provides a new methodology for near-real-time classification of driver behavior in uncontrolled environments.