Detecting distraction and degraded driver performance with visual behavior metrics

Abstract

Driver distraction contributes to approximately 43% of motor-vehicle crashes and 27% of near-crashes. Rapidly developing in-vehicle technology and electronic devices place additional demands on drivers, which might lead to distraction and diminished capacity to perform driving tasks. This situation threatens safe driving. Technology that can detect and mitigate distraction by alerting drivers could play a central role in maintaining safety. Correctly identifying driver distraction in real time is a critical challenge in developing distraction mitigation systems, and this function has not been well developed. Moreover, the greatest benefit may be from real-time distraction detection in advance of dangerous breakdowns in driver performance. Based on driver performance, two types of distraction-visual and cognitive-are identified. These types of distraction have very different effects on visual behavior and driving performance; therefore, they require different algorithms for detection. Distraction detection algorithms typically rely on either eye measures or driver performance measures because the effect of distraction on the coordination of measures has not been established. Combining both eye glance and vehicle data could enhance the ability of algorithms to detect and differentiate visual and cognitive distraction. The goal of this research is to examine whether poor coordination between visual behavior and vehicle control can identify diminished attention to driving in advance of breakdowns in lane keeping. The primary hypothesis of this dissertation is that detection of changes in eye-steering relationship caused by distraction could provide a prospective indication of vehicle state changes. Three specific aims are pursued to test this hypothesis. The first aim examines the effect of distracting activity on eye and steering movements to assess the degree to which the correlation parameters are indicative of distraction. The second aim applies a control-theoretic system identification approach to the eye movement and steering data to distinguish between distracted and non-distracted conditions. The third aim examines whether changes of eye-steering coordination

Exploratory Development of Algorithms for Determining Driver Attention Status

“…The federally funded SAVE-IT program was designed to enhance the effectiveness of safety warning systems and mitigate distraction with effective countermeasures (NHTSA, 2004). This served as the catalyst for many distraction and driver workload algorithms, such as those that used vision-based systems and combined different glance patterns like glance location, glance frequency, glance duration, and eyelid movement (Ahlström & Kircher, 2010;Fernández et al, 2016;Kircher & Ahlstrom, 2018;Kircher et al, 2009;Liang et al, 2012;Victor et al, 2008;Yekhshatyan, 2010;Yilu et al, 2004;Zhang et al, 2006). Other research used driving simulator data to determine a driver's attention level and/or their interaction with distraction feedback displays (Donmez et al, 2007;Engström & Mårdh, 2007; J.…”