Effects of Distraction in On-Road Level 2 Automated Driving: Impacts on Glance Behavior and Takeover Performance. (December 2021)

• Record Type: Journal Article
• Authors: Yang, Shiyan; Kuo, Jonny; Lenné, Michael G.
• Is Part Of: Human factors. Volume 63:Number 8(2021)
• Page Start: 1485
• Page End: 1497
• Publication Date: 2021-12
• Subjects: driver distraction -- automated driving -- glance behavior -- takeover reaction -- driver monitoring; Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720820936793
• Languages: English
• ISSNs: 0018-7208

Abstract

Objective: The paper aimed to investigate glance behaviors under different levels of distraction in automated driving (AD) and understand the impact of distraction levels on driver takeover performance. Background: Driver distraction detrimentally affects takeover performance. Glance-based distraction measurement could be a promising method to remind drivers to maintain enough attentiveness before the takeover request in partially AD. Method: Thirty-six participants were recruited to drive a Tesla Model S in manual and Autopilot modes on a test track while engaging in secondary tasks, including temperature-control, email-sorting, and music-selection, to impose low and high distractions. During the test drive, participants needed to quickly change the lane as if avoiding an immediate road hazard if they heard an unexpected takeover request (an auditory warning). Driver state and behavior over the test drive were recorded in real time by a driver monitoring system and several other sensors installed in the Tesla vehicle. Results: The distribution of off-road glance duration was heavily skewed (with a long tail) by high distractions, with extreme glance duration more than 30 s. Moreover, being eyes-off-road before takeover could cause more delay in the urgent takeover reaction compared to being hands-off-wheel. Conclusion: The study measured off-road glance duration under different levels of distraction and demonstrated the impacts of being eyes-off-road and hands-off-wheel on the following takeover performance. Application: The findings provide new insights about engagement in Level 2 AD and are useful for the design of driver monitoring technologies for distraction management.