Abnormal Driving Detection Based on Normalized Driving Behavior

Abnormal driving behavior may cause serious danger to both the driver and the public. In this study, we propose to detect abnormal driving by analyzing normalized driving behavior. Serving as the virtual driver, a personalized driver model is established for speed control purposes by using the locally designed neural network and the real-world vehicle test data. The driving behavior is normalized by employing the virtual driver to conduct the speed following task as defined by the standard driving cycle test, e.g., the FTP-72. Three typical abnormal driving behaviors are characterized and simulated, namely, the fatigue/drunk, the reckless, and the phone use while driving. An abnormality index is proposed based on the analysis of normalized driving behaviors and is applied to quantitatively evaluate the abnormity. Numerical experiments are conducted to verify the effectiveness of the proposed scheme.