Transitioning to multi-dimensional estimation of visual distraction and its safety effects under automated driving: A spatiotemporal and directional estimation approach

Traditional methodologies for measuring visual distraction have been limiting in their approaches, treating distraction as a one-dimensional variable. This has been accomplished either by categorizing distraction as a binary variable or using surrogate measurements, such as reaction time in response to non-driving related tasks, which follow the procedures of measuring distraction from psychology. Furthermore, as human-vehicle interaction (HVI) under automated driving has the potential to provide safety information and bring visual distraction simultaneously, there lacks an investigation on the quantitative relationship between distraction and safety due to the restrained methodologies in measuring visual distraction. As HVI-induced driver distraction plays a critical role in determining safe driving under automated driving, a methodology is highly needed to comprehensively measure the visual distraction under automated driving so that its impact on safety can be further investigated. Therefore, sticking to the definition of visual distraction, this research aims to (1) improve the existing methodology in measuring HVI-induced driver distraction under automated driving by focusing on visual distraction and mathematically describing it from spatiotemporal and directional dimensions; (2) theoretically investigate how the quantified visual distraction influences driving safety and (3) quantify the relationship between distraction and safety with introducing the “distraction-safety” ratio. Drivers’ fixation behaviors are used in quantifying visual distraction, which is measured by spatiotemporal and directional relationships between drivers’ visual attention and the attention that indicates “zero distraction”. Three newly added performance measures in quantifying HVI-induced visual distraction are real-time magnitudes, real-time directions, and intensities (cumulation of magnitudes over time). To validate the proposed methods, a verification study was conducted by recruiting drivers to test automated driving under Level 3 automation. Drivers are required to wear an eye-tracker and go through two scenarios interacting with jaywalkers where takeover actions are needed with two takeover warnings (“visual-only” and “visual & audible”). Per past studies, takeover time was measured in representing distraction level. As a result, this study confirms the validity of the proposed methods by revealing the significant and positive correlations between the measured distraction intensity and the takeover time. Discussions of the quantified visual distraction from the spatiotemporal and directional perspective further enhance the understanding of HVI-induced driver distraction under automated driving through multiple dimensions. Furthermore, this research reveals how distracted driving affects safety under automated driving through the takeover performance. Thresholds under visual distraction magnitudes and degrees that lead to traffic conflicts were identified. More importantly, a “distraction-safety” ratio that quantifies the relationship between visual distraction and safety benefits is proposed. The results suggest that the “visual & audible” is more effective in significantly enhancing safety while aggregating a significantly smaller amount of visual distraction. The contribution of this research is to re-define the methodological approach of measuring visual distraction by (1) measuring distraction multi-dimensionally and (2) establishing a “distraction-safety” system in quantitatively assessing the balance of safety benefits and the HVI-induced visual distraction magnitude under automated driving environment.