Driver inattention assessment has become a very active field in intelligent transportation systems. Based on active sensor Kinect and computer vision tools, we have built an efficient module for detecting driver distraction and recognizing the type of distraction. Based on color and depth map data from the Kinect, our system is composed of four sub-modules. We call them eye behavior (detecting ...

Cognitive distraction represents an important and growing traffic safety issue, particularly with the increasing computerization of cars. The target paper in this special section describes a protocol for assessing the distraction potential of information and entertainment systems. Cognitive distraction has specific relevance to the challenges facing driving safety but also reflects the more per...

Driver distraction is strongly associated with crashes and near-misses, and despite the attention this topic has received in recent years, the effect of different types of distracting task on driving performance remains unclear. In the case of non-visual distractions, such as talking on the phone or other engaging verbal tasks that do not require a visual input, a common finding is reduced late...

Driver distraction is an important and growing safety concern as information technologies, such as navigation systems and internet-content services, have become increasingly common in vehicles. To allow people to benefit from these technologies without compromising safety, an adaptive In-Vehicle Information System (IVIS) is needed. Such systems can manage drivers workload and mitigate distracti...

Detection of cognitive distraction presents an indispensable function for driver distraction mitigation systems. In this study, we developed a layered algorithm that integrated two data mining methods—Dynamic Bayesian Network (DBN) and supervised clustering method—to identify cognitive distraction from drivers’ eye movements and driving performance measures. We used the data collected in a simu...

Driver distraction is a relevant driving safety issue and an ongoing field of research. A particular distraction is cognitive distraction, which refers to when the driver is mentally engaged in a task unrelated to driving, e.g. talking to a passenger. Eye movements can be analyzed to study effects of cognitive distraction during driving, and are typically recorded using video-based eye tracker ...

Jaihie Kim Yonsei University School of Electrical and Electronic Engineering 134 Sinchon-dong, Seodaemun-gu Seoul, Seoul 120-749, Republic of Korea E-mail: [email protected] Abstract. Most driver-monitoring systems have attempted to detect either driver drowsiness or distraction, although both factors should be considered for accident prevention. Therefore, we propose a new drivermonitoring me...

In this paper, we propose initial steps towards multi-modal driver stress (distraction) detection in urban driving scenarios involving multi-tasking, dialog system conversation, and medium-level cognitive tasks. The goal is to obtain a continuous operation-mode detection employing driver’s speech and CAN-Bus signals, with a direct application for an intelligent human-vehicle interface which wil...

There is concern that roadside advertising presents a significant risk to driving safety, with conservative estimates putting external distractions responsible for up to 10% of all traffic incidents (Young and Mahfoud, 2007). Studies indicate that any interference that distracts the driver from looking forward from the roadway for more than two seconds significantly increases the chances of cra...

One of the main reasons for fatal accidents on the road is distracted driving. The continuous attention of an individual driver is a necessity for the task of driving. While driving, certain levels of distraction can cause drivers to lose their attention, which might lead to an accident. Thus, the number of accidents can be reduced by early detection of distraction. Many studies have been condu...