Driver cognitive distraction is a hazard state, which can easily lead to traffic accidents. This study focuses on detecting the driver cognitive distraction state based on driving performance measures. Characteristic parameters could be directly extracted from Controller Area Network-(CAN-)Bus data, without depending on other sensors, which improves real-time and robustness performance. Three cognitive distraction states (no cognitive distraction, low cognitive distraction, and high cognitive distraction) were defined using different secondary tasks. NLModel, NHModel, LHModel, and NLHModel were developed using SVMs according to different states. The developed system shows promising results, which can correctly classify the driver’s states in approximately 74%. Although the sensitivity for these models is low, it is acceptable because in this situation the driver could control the car sufficiently. Thus, driving performance measures could be used alone to detect driver cognitive state.

This paper presents a solution for the license plate recognition problem in residential community administrations in China. License plate images are pre-processed through gradation, middle value filters and edge detection. In the license plate localization module the number of edge points, the length of license plate area and the number of each line of edge points are used for localization. In the recognition module, the paper applies a statistical character method combined with a structure character method to obtain the characters. In addition, more models and template library for the characters which have less difference between each other are built. A character classifier is designed and a fuzzy recognition method is proposed based on the fuzzy decision-making method. Experiments show that the recognition accuracy rate is up to 92%.

Driver sleepiness is a hazard state, which can easily lead to traffic accidents. To detect driver sleepiness in real time, a novel driver sleepiness detection system using support vector machine (SVM) based on eye movements is proposed. Eye movements data are collected using SmartEye system in a driving simulator experiment. Characteristic parameters, which include blinking frequency, gaze direction, fixation time, and PERCLOS, are extracted based on the data using a statistical method. 13 sleepiness detection models including 12 specific models and 1 general model are developed based on SVM. Experimental results demonstrate that eye movements can be used to detect driver sleepiness in real time. The detecting accuracy of the specific models significantly exceeds the general model ( < 0.001), suggesting that individual differences are an important consideration when building detection algorithms for different drivers.

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