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Simultaneous analysis of driver behaviour and road condition for driver distraction detection

机译:同时分析驾驶员行为和路况以检测驾驶员的注意力

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摘要

The design of intelligent driver assistance systems is of increasing importance for the vehicle-producing industry and road-safety solutions. This article starts with a review of road-situation monitoring and driver's behaviour analysis. This article also discusses lane tracking using vision (or other) sensors, and the strength or weakness of different methods of driver behaviour analysis (e.g. iris or pupil status monitoring, and EEG spectrum analysis). This article focuses then on image analysis techniques and develops a multi-faceted approach in order to analyse driver's face and eye status via implementing a real-time AdaBoost cascade classifier with Haar-like features. The proposed method is tested in a research vehicle for driver distraction detection using a binocular camera. The developed algorithm is robust in detecting different types of driver distraction such as drowsiness, fatigue, drunk driving or the performance of secondary tasks.
机译:智能驾驶员辅助系统的设计对于汽车制造行业和道路安全解决方案越来越重要。本文首先回顾了路况监控和驾驶员行为分析。本文还讨论了使用视觉(或其他)传感器进行车道跟踪,以及驾驶员行为分析的不同方法(例如虹膜或瞳孔状态监测以及EEG频谱分析)的优缺点。然后,本文重点介绍图像分析技术,并开发一种多方面的方法,以通过实现具有类似Haar功能的实时AdaBoost级联分类器来分析驾驶员的面部和眼睛状态。所提出的方法在研究车辆中进行了测试,以使用双目摄像机进行驾驶员注意力分散检测。所开发的算法在检测不同类型的驾驶员注意力(例如困倦,疲劳,酒后驾驶或执行次要任务)方面具有强大的功能。

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