title: Augmented reality cues to assist older drivers with gap estimation for left-turns.
abstract: The objective of this study was to assess the effects of augmented reality (AR) cues designed to assist middle-aged and older drivers with a range of UFOV impairments, judging when to make left-turns across oncoming traffic. Previous studies have shown that AR cues can help middle-aged and older drivers respond to potential roadside hazards by increasing hazard detection without interfering with other driving tasks. Intersections pose a critical challenge for cognitively impaired drivers, prone to misjudge time-to-contact with oncoming traffic. We investigated whether AR cues improve or interfere with hazard perception in left-turns across oncoming traffic for drivers with age-related cognitive decline. Sixty-four middle-aged and older drivers with a range of UFOV impairment judged when it would be safe to turn left across oncoming traffic approaching the driver from the opposite direction in a rural stop-sign controlled intersection scenario implemented in a static base driving simulator. Outcome measures used to evaluate the effectiveness of AR cueing included: Time-to-Contact (TTC), Gap Time Variation (GTV), Response Rate, and Gap Response Variation (GRV). All drivers estimated TTCs were shorter in cued than in uncued conditions. In addition, drivers responded more often in cued conditions than in uncued conditions and GRV decreased for all drivers in scenarios that contained AR cues. For both TTC and response rate, drivers also appeared to adjust their behavior to be consistent with the cues, especially drivers with the poorest UFOV scores (matching their behavior to be close to middle-aged drivers). Driver ratings indicated that cueing was not considered to be distracting. Further, various conditions of reliability (e.g., 15% miss rate) did not appear to affect performance or driver ratings.
authors.affiliation: University of Iowa, Department of Neurology, 200 Hawkins Drive, Iowa City, IA 52242, United States of America. Electronic address: mrusch@healthcare.uiowa.edu.
authors.forename: Michelle L
authors.initials: ML
authors.lastname: Rusch
authors.affiliation: University of Iowa, Department of Mechanical and Industrial Engineering, 3131 Seamans Center for the Engineering Arts and Sciences, Iowa City, IA 52242, United States of America. Electronic address: mark-schall@uiowa.edu.
authors.forename: Mark C
authors.initials: MC
authors.lastname: Schall
authors.affiliation: University of Wisconsin-Madison, Department of Industrial and Systems Engineering, 1513 University Avenue, 3007 Mechanical Engineering, Madison, WI 53706-1572, United States of America. Electronic address: jdlee@engr.wisc.edu.
authors.forename: John D
authors.initials: JD
authors.lastname: Lee
authors.affiliation: University of Iowa, Department of Biostatistics, 200 Hawkins Drive, Iowa City, IA 52242, United States of America. Electronic address: jeffrey-dawson@uiowa.edu.
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Dawson
authors.affiliation: University of Iowa, Department of Neurology, 200 Hawkins Drive, Iowa City, IA 52242, United States of America. Electronic address: matthew-rizzo@uiowa.edu.
authors.forename: Matthew
authors.initials: M
authors.lastname: Rizzo
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology*; D001288:Attention / Q000502:physiology*; D001334:Automobile Driving; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D003463:Cues; D006801:Humans; D008297:Male; D008875:Middle Aged; D013998:Time Perception / Q000502:physiology; D014796:Visual Perception / Q000502:physiology
keywords: Aging; Augmented reality; Displays; Driver behavior; Left-turns; UFOV