Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment
This work addresses the problem of real-time driver monitoring for automotive safety and insurance applications, though it is incremental in applying unsupervised learning to existing sensor data.
The paper tackled driver behavior classification using CAN bus data in uncontrolled environments, achieving robust clustering of drivers across diverse road scenarios without predetermined instructions.
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.