The risk of drivers engaging in distracting activies is increasing as in-vehicle technology and carried-in devices become increasingly common and complicated. Consequently, distraction and inattention contribute to crash risk and are likely to have an increasing influence on driving safety. Analysis of police-reported crash data from 2008 showed that distractions contributed to an estimated 5,8...

This paper presents a multi-modal automotive dictation editor (codenamed ECOR) used to compose and correct text messages while driving. The goals are to keep driver's distraction minimal while achieving good task completion rates and times as well as acceptance by users. We report test results for a set of 28 English novice users using the system while driving a standard lanechange-test (LCT) c...

Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and ...

This paper presents a computational modeling approach for negative effects simulation of visual distraction while driving a car. In order to investigate these effects, an experiment was firstly implemented on a driving simulator. Twenty participants were invited to perform a car following task in different driving conditions (12 driving scenarios), with or without a secondary task of visual dis...

In the last decade, distraction detection of a driver gained lot significance due to increases in number accidents. Many solutions, such as feature based, statistical, holistic, etc., have been proposed solve this problem. With advent high processing power at cheaper costs, deep learning-based techniques shown promising results. The study proposes ReSVM, an approach combining features ResNet-50...

Transportation safety is highly correlated with driving behavior, especially human error playing a key role in large portion of crashes. Modern instrumentation and computational resources allow for the monitorization driver, vehicle, roadway/environment to extract leading indicators crashes from multi-dimensional data streams. To quantify variations that are beyond normal driver behavior vehicl...

Automatically adapting vehicle settings due to activities of the driver has crucial impact to safety in road traffic, intelligent driver assistance and vehicle control systems as well as driving comfort. Distraction continues to increase by reason of technology growth (classical officeand home electronics like entertainment and navigation systems, televisions, Email and internet-clients, multim...

Driver distraction is known to increase crashes, especially when the driver glances for especially long periods of time inside the vehicle. While it is clear that such glances increase risk for the driver when looking inside the vehicle, it is less clear how these glances disrupt the ongoing processing of information outside the vehicle once the eyes return to the road. The present study was ai...

Effects of different types of cell phone use were examined through an analysis of selected data from the SHRP2 Naturalistic Driving Study (NDS). Driving events involving lead-vehicle or approaching-vehicle incidents were analyzed to compare driver reaction times and crash probability across driver distraction type, driver age, and roadway environment. The analysis found that the median reaction...