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arXiv · 1710.04133

Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment

Umberto Fugiglando·Emanuele Massaro·Paolo Santi·Sebastiano Milardo·Kacem Abida·Rainer Stahlmann·Florian Netter·Carlo Ratti
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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.

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Umberto Fugiglando, Emanuele Massaro, Paolo Santi, Sebastiano Milardo, Kacem Abida, Rainer Stahlmann, Florian Netter, Carlo Ratti. 2017-10-09. Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment. https://arxiv.org/abs/1710.04133

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