Cars can nowadays record several thousands of signals through the controller area network (CAN) bus technology and potentially provide real-time information on the car, the driver, and the surrounding environment. This paper proposes a new methodology for near-real-time 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, longitudinal and lateral acceleration. Data have been collected in a completely uncontrolled experiment involving 54 people, where over 2000 trips have been recorded without any type of predetermined driving instruction on a wide variety of road scenarios. While only few works have analyzed the driving behavior of more than 50 drivers using CAN bus data, 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, showing that by properly choosing a subsampling strategy it is possible to reduce the size of the database as much as 99% without impairing the clustering performance.
Fugiglando et al. REF developed a new methodology for near-real-time classification of driver behavior in an uncontrolled setting.
Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Engineering Mathematics Computer Science Physics