Eliciting Driver Stress Using Naturalistic Driving Scenarios on Real Roads

Baltodano, S; Garcia-Mancilla, J; Ju, W; ,

HERO ID

7117537

Reference Type

Journal Article

Year

2018

HERO ID 7117537
In Press No
Year 2018
Title Eliciting Driver Stress Using Naturalistic Driving Scenarios on Real Roads
Authors Baltodano, S; Garcia-Mancilla, J; Ju, W; ,
Page Numbers 298-309
Abstract We propose a novel method for reliably inducing stress in drivers for the purpose of generating real-world participant data for machine learning, using both scripted in-vehicle stressor events and unscripted on-road stressors such as pedestrians and construction zones. On-road drives took place in a vehicle outfitted with an experimental display that lead drivers to believe they had prematurely ran out of charge on an isolated road. We describe the elicitation method, course design, instrumentation, data collection procedure and the post-hoc labeling of unplanned road events to illustrate how rich data about a variety of stress-related events can be elicited from study participants on-road. We validate this method with data including psychophysiological measurements, video, voice, and GPS data from (N = 20) participants. Results from algorithmic psychophysiological stress analysis were validated using participant self-reports. Results of stress elicitation analysis show that our method elicited a stress-state in 89% of participants.
Doi 10.1145/3239060.3239090
Wosid WOS:000455217200030
Is Certified Translation No
Dupe Override No
Conference Location Toronto, CANADA
Conference Name 10th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI)
Comments Journal:AUTOMOTIVEUI'18: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON AUTOMOTIVE USER INTERFACES AND INTERACTIVE VEHICULAR APPLICATIONS
Is Public Yes
Keyword Driver Evaluation; Driver Benchmarking; Stress; Interaction Design; Design Methods; Wizard of Oz