Modeling of Driver Behavior in Real World Scenarios Using Multiple Noninvasive Sensors

Download or read book Modeling of Driver Behavior in Real World Scenarios Using Multiple Noninvasive Sensors written by Nanxiang Li and published by . This book was released on 2015 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the development of new in-vehicle technology, drivers are exposed to more sources of distractions, which can lead to unintentional accidents. Monitoring the driver attention level has become a relevant research problem. Many studies aim to understand the driver behavior using measurements from different perspectives, such as driving task performance, secondary task performance and driver physiology signal. Although these studies provide important characteristics about driver distractions, there are open challenges that remain to be addressed. First, there is no standard for quantifying the driver distraction level in either absolute or relative terms. Defining a reliable driver distraction metric is important to train machine learning algorithms that predict driver distractions. To address this question, we propose to use human perception to measure the perceived distraction levels across different types of distractions (e.g. visual and cognitive). In the visual-cognitive distraction space, we define distraction modes to represent the driver distraction level using data driven approaches. This representation provides a more comprehensive description of the detrimental effects caused by secondary tasks. It provides an ideal framework to analyze the effects of future in-vehicle systems on driver behaviors. Second, most of the previous studies have largely relied on driving simulators. Instead, we consider real-world driving scenarios on real roads. We use multimodal features extracted from various noninvasive sensors including the controller area network-bus (CAN-Bus), video cameras and microphone arrays. By applying different machine learning techniques, including binary classification, multiclass classification and regression models, we build models to track driver's attention level, detect driver distraction, and identify multi-modal discriminative features to capture distracted driver behaviors. Finally, we explore the detection of contextual information about the driver and the road to enhance the proposed driver attention model. In particular, we focus on detecting mirror check actions and detection of frontal vehicles. This contextual information can inform the in-vehicle safety system whether the drivers appropriately respond to the driving tasks required by the road conditions. In addition, we also develop a user-independent calibration free gaze estimation model, which is closely related to the driver visual/cognitive distraction estimation.