Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment

TL;DR

This paper introduces an unsupervised method to classify driver behavior using CAN bus data collected from diverse, uncontrolled driving scenarios, enabling real-time driver analysis without prior instructions.

Contribution

It presents a novel unsupervised clustering approach for driver behavior classification using a minimal subset of CAN signals in uncontrolled environments.

Findings

Robust driver clustering achieved with minimal data

Method validated across diverse road scenarios

Provides near-real-time driver behavior classification

Abstract

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.