驾驶员注意涣散检测技术研究

通过对注意涣散时驾驶员头部运动及面部表情变化特征的分析,系统实时监测驾驶员眼睛、嘴巴位置和运动状态信息,构建驾驶员注意涣散特征表征参量,实现对驾驶员注意涣散状态信息的检测与提取。驾驶员注意涣散表征量具有复杂的非线性特征,利用BP神经网络非线性识别的优势对驾驶员注意特征进行模式分类,实现驾驶员不同注意涣散状态下的特征捕捉。同时采用Dempster-Shafer证据推理技术,对驾驶注意涣散多源表征信息进行决策融合,实现对驾驶员注意涣散状态的判断。结果表明,BP神经网络与D-S规则多源信息决策融合技术的运用提高了驾驶员注意涣散特征检测的准确性和可靠性。

Study on Detection Technique for Drivers' Distraction

Drivers' distraction in driving is one of the major causes of road accidents.The abnormal behaviors of drivers'head and their facial expressions were studied in detail so as to get the status information characteristics about drivers'distraction.Through making a real-time monitoring of the information on driver's facial expressions: eyes,mouth position and movement status information,the detection mechanisms was established to capture the scattered mental state of drivers and determine the driver's distracted state.Based on this achievement and with the help of the eyes and mouth region detection,BP neural network was used to estimate the different modes of driver's distraction.Meanwhile,Dempster-Shafer evidence theory was integrated to fulfill the determination of driver's distraction state by making a multi-information fusion of driver's distraction.Experimental result suggests that the technique based on BP neural network and multi-source information fusion technique with D-S rule improves the reliability and accuracy of detecting drivers' distraction state.