Huacai Xian

License Plate Recognition Algorithm for Passenger Cars in Chinese Residential Areas

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 Cognitive Distraction Detection Using Driving Performance Measures

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.

Driver Sleepiness Detection System Based on Eye Movements Variables

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 (P < 0.001), suggesting that individual differences are an important consideration when building detection algorithms for different drivers.

Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements

This study was designed to gain insight into the influence of performing different types of secondary task while driving on driver eye movements and to build a safety evaluation model for secondary task driving. Eighteen young drivers were selected and completed the driving experiment on a driving simulator. Measures of fixations, saccades, and blinks were analyzed. Based on measures which had significant difference between the baseline and secondary tasks driving conditions, the evaluation index system was built. Method of principal component analysis (PCA) was applied to analyze evaluation indexes data in order to obtain the coefficient weights of indexes and build the safety evaluation model. Based on evaluation scores, the driving safety was grouped into five levels (very high, high, average, low, and very low) using K-means clustering algorithm. Results showed that secondary task driving severely distracts the driver and the evaluation model built in this study could estimate driving safety effectively under different driving conditions.

The Effects of Using In-Vehicle Computer on Driver Eye Movements and Driving Performance

This paper examined the effects of performing an e-mail receiving and sending task using in-vehicle computer (iPad4) on driving performance and driver eye movements to determine if performance decrements decreased with practice. Eighteen younger drivers completed the driving on driving simulator while interacting with or without an e-mail task. Measures of fixations, saccades, vehicle control, and completion time of the secondary task were analyzed. Results revealed that using in-vehicle computer featured “large touch-screen” to receive and send e-mail greatly weakened drivers distraction and decreased their ability to control the vehicle. There was also evidence that, however, drivers attempted to regulate their behavior when distracted by decreasing their driving speed and taking a large number of short fixations and a quick saccades towards the computer. The results suggest that performing e-mail receiving and sending tasks while driving is problematic and steps to prohibit this activity should be taken.

Research on safety evaluation model for in-vehicle secondary task driving

This paper presents a new method for evaluating in-vehicle secondary task driving safety. There are five in-vehicle distracter tasks: tuning the radio to a local station, touching the touch-screen telephone menu to a certain song, talking with laboratory assistant, answering a telephone via Bluetooth headset, and finding the navigation system from Ipad4 computer. Forty young drivers completed the driving experiment on a driving simulator. Measures of fixations, saccades, and blinks are collected and analyzed. Based on the measures of driver eye movements which have significant difference between the baseline and secondary task driving conditions, the evaluation index system is built. The Analytic Network Process (ANP) theory is applied for determining the importance weight of the evaluation index in a fuzzy environment. On the basis of the importance weight of the evaluation index, Fuzzy Comprehensive Evaluation (FCE) method is utilized to evaluate the secondary task driving safety. Results show that driving with secondary tasks greatly distracts the drivers attention from road and the evaluation model built in this study could estimate driving safety effectively under different driving conditions.

Analyzing Effects of Pressing Radio Button on Driver’s Visual Cognition

An approach is presented based on driver simulator and SmarteyeII eye tracking system to examine the effects of pressing in-vehicle radio button on driver’s visual cognition. Parameters of glance frequency, glance duration, eye movement speed, and visual line moving in different regions of interest (ROIs) in task of pressing the radio button, closely related with driver’s visual cognition, were collected and analyzed. Based on the experimental data, driver’s visualization model with secondary tasks was built by CogTool. Driver’s vision, eye movement, cognition, and hand motion were tracked and recorded by the model. Results of experiment and running model show that pressing the in-vehicle radio button while driving has adverse influence on driver’s visual cognition and occupies a lot of the driver’s visual resources.

Effects of driver behavior style differences and individual differences on driver sleepiness detection

Driving sleepiness is still a major causes of traffic accidents. Individual drivers, under various conditions, act and respond in different manners. This article presents the attempt of a straight-line driving simulator study that examined the effects of driver behavior style differences and individual differences on driver sleepiness detection which is based on driving performance measures. A total of 15 drivers who were classified into two categories through subjective assessment based on a Driver Behavior Questionnaire participated in driving simulator experiments. A total of 18 detection models, including 15 SE models for each subject, an A model for the aggressive drivers, an NA model for the non-aggressive drivers, and a G model for all experiment participants, were developed using support vector machine method based on driving performance characteristic parameters. The results show that the G model is not suitable for all drivers due to its lower mean accuracy of 69.88% (standard deviation = 7.70%) and higher standard deviation. The SE models for each subject show the best detection accuracy performance of 84.26% (standard deviation = 5.38%); however, it is impossible to set up a special detection model for every individual driver. The SD models on different style categories show an accuracy value of 77.54% (standard deviation = 5.78%). The results demonstrate that driver style differences as well as individual differences have great effects on driver sleepiness detection (F = 19.148, p < 0.000).

RESEARCH ON IN-VEHICLE SECONDARY TASK ANALYSIS MODEL BASED ON PREVIEW THEORY

The behavior of secondary task has an important influence on driving safety. It is necessary to build a precise driving model for accurate analysis of the influence of a secondary task on driving performance. The vehicle preview trajectory curve and steering control strategy are based on two different preliminary aim modes: the far point and the near one. Through the adjustment of a vehicle’s acceleration and deceleration according to the distance between the vehicle and the lead vehicle, a controllable method of vehicle running speed is determined, which regards the lead vehicle tail center as its tracking target. Finally, the driving model is built and the experiment shows that the model developed in this study conforms to reality better. Based on the model and the software Distract-R, successful analysis of the effect of secondary task on driving performance is achieved.

Driver Fatigue Detection System Based on Eye Movements

Driver fatigue is a hazard which can easily lead to traffic accidents. To detect driver fatigue in real time, a novel fatigue detection system using support vector machine (SVM) based on eye movements is proposed. Eye movement data was collected using the SmartEye system in a driving simulator experiment. Characteristic parameters which include blinking frequency, gaze direction, fixation time and PERCLOS were extracted based on the data using statistical method. The characteristic parameters were used to train and test SVM model. Experimental results show that this system achieves a satisfied performance, which demonstrate that eye movements can be used to detect driver fatigue in real time.