Lane-Keeping Behavior and Cognitive Load with Use of Lane Departure Warning

2017-01-1407

03/28/2017

Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
Lane Departure Warning (LDW) systems, along with other types of Advanced Driver Assistance Systems (ADAS), are becoming more common in passenger vehicles, with the general aim of improving driver safety through automation of various aspects of the driving task. Drivers have generally reported satisfaction with ADAS with the exception of LDW systems, which are often rated poorly or even deactivated by drivers. One potential contributor to this negative response may be an increase in the cognitive load associated with lane-keeping when LDW is in use. The present study sought to examine the relationship between LDW, lane-keeping behavior, and concurrent cognitive load, as measured by performance on a secondary task. Participants drove a vehicle equipped with LDW in a demarcated lane on a closed-course test track with and without the LDW system in use over multiple sessions. On a subset of laps, participants were required to perform a secondary task requiring mental arithmetic while driving. The frequency and duration of lane line crossings and the participants’ accuracy on the secondary arithmetic task were recorded for each lap in two sessions. LDW successfully enhanced lane-keeping behavior in the earlier session, but this effect was eliminated in the later session. Secondary task performance showed no effect of LDW on the earlier session, but significantly declined when LDW was in use during the second session. Implications of these preliminary results are discussed with regard to overall driver performance with LDW and other ADAS, as well as driver acceptance of more fully autonomous vehicle systems.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-1407
Pages
7
Citation
Moorman, H., Niles, A., Crump, C., Krake, A. et al., "Lane-Keeping Behavior and Cognitive Load with Use of Lane Departure Warning," SAE Technical Paper 2017-01-1407, 2017, https://doi.org/10.4271/2017-01-1407.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-1407
Content Type
Technical Paper
Language
English