Real Time Driving Data Collection and Driver Verification using CMAC-MFCC

There have been numerous studies in understanding driver behavior for purpose of understanding contributing factors to high accident rates. Driving abnormalities could be one of the many factors affecting accidents and if it can be detected this will help prevent accidents. In this paper we present simple and effective methods for an in-car data acquisition in collecting real time driving data. These data will be used to investigate the effectiveness of driver behavior, focusing on driver’s response to the brake and gas pedals as well as its rate of change. From these data, we will demonstrate simple yet effective technique in driver verification. Driver profiles were created using the cerebellum model articulation controller (CMAC) feature map taking inputs from the brake and gas pedals pressure signals. From the CMAC outputs, relevant features were extracted using Mel-Frequency Cepstral Coefficient (MFCC). These features were used to verify drivers using multi layer perceptron (MLP) as classifiers. The performance of the driver verification indicates positive development in the area of intelligent vehicle driver verification system that may enhance the driver’s security, safety and comfort in driving.