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.

本研究提出了一种基于CAN总线信号的驾驶行为分析和分类的新方法，使用非监督学习技术来实现驾驶员不同群体的聚类，并提供了一种验证方法来测试聚类的稳健性，以及计算保存稳健驾驶员群体的最小数据量，该研究为无控制环境下驾驶员行为的近实时分类提供了新的方法。

基于CAN总线数据的无控制环境下驾驶行为分析