Methods for Machine Vision Based Driver Monitoring Applications: Dissertation

An increasing number of information and driver-assistive facilities-such as PDAs, mobile phones, and navigators-are a feature of today's road vehicles. Unfortunately, they occupy a vital part of the driver's attention and may overload him or her in critical moments when the driving situation requires full concentration. The automotive industry has shown a growing interest in capturing the driver's behaviour due to the necessity of adapting the vehicle's Human-Machine Interface (HMI), for example, by scheduling the information flow or providing warning messages when the driver's level of alertness degrades. The ultimate aim is to improve traffic safety and the comfort of the driving experience. The scope of this thesis is to investigate the feasibility of techniques and methods, previously examined within the industry, for monitoring the driver's momentary distraction state and level of vigilance during a driving task. The study does not penetrate deeply into the fundamentals of the proposed methods but rather provides a multidisciplinary review by adopting new aspects and innovative approaches to state-of-art monitoring applications for adapting them to an in-vehicle environment. The hypotheses of this thesis states that detecting the level of distraction and/or fatigue of a driver can be performed by means of a set of image processing methods, enabling eye-based measurements to be fused with other safety-monitoring indicators such as lane-keeping performance or steering activity. The thesis includes five original publications that have proposed or examined image processing methods in industrial applications, as well as two experiment-based studies related to distraction detection in a heavy goods vehicle (HGV), complemented with some initial results from implementation in a passenger car. The test experiments of the proposed methods are mainly described in the original publications. Therefore, the objective of the introduction section is to generate an overall picture of how the proposed methods can be successfully incorporated and what advantages they offer to driver-monitoring applications. The study begins by introducing the scope of this work, and continues by presenting data acquisition methods and image pre- and post-processing techniques for improving the quality of the input data. Furthermore, feature extraction from images and classification scheme for detecting the driver's state are outlined based in part on the author's own experiments. Finally, conclusions are drawn based on the results obtained.