The effects of takeover request modalities on highly automated car control transitions

Sol Hee Yoon, Young Woo Kim, Yong Gu Ji

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This study investigated the influences of takeover request (TOR) modalities on a drivers’ takeover performance after they engaged in non-driving related (NDR) tasks in highly automated driving (HAD). Visual, vibrotactile, and auditory modalities were varied in the design of the experiment under four conditions: no-task, phone conversation, smartphone interaction, and video watching tasks. Driving simulator experiments were conducted to analyze the drivers’ take-over performance by collecting data during the transition time of re-engaging control of the vehicle, the time taken to be on the loop, and time taken to be physically ready to drive. Data were gathered on the perceived usefulness, safety, satisfaction, and effectiveness for each TOR based on a self-reported questionnaire. Takeover and hands-on times varied considerably, as shown by high standard deviation values between modalities, especially for phone conversations and smartphone interaction tasks. Moreover, it was found that participants failed to take over control of the vehicle when they were given visual TORs for phone conversation and smartphone interaction tasks. The perceived safety and satisfaction varied for the NDR task. Results from the statistical analysis showed that the NDR task significantly influenced the takeover time, but there was no significant interaction effect between the TOR modalities and the NDR task. The results could potentially be applied to the design of safe and efficient transitions of highly controlled, automated driving, where drivers are enabled to engage in NDR tasks.

Original languageEnglish
Pages (from-to)150-158
Number of pages9
JournalAccident Analysis and Prevention
Volume123
DOIs
Publication statusPublished - 2019 Feb

Fingerprint

Smartphones
Railroad cars
conversation
driver
interaction
Statistical methods
Safety
Simulators
Experiments
experiment
statistical analysis
performance
video
time
questionnaire
Smartphone
Values

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Safety, Risk, Reliability and Quality
  • Public Health, Environmental and Occupational Health
  • Law

Cite this

@article{7e2f12186c7c429d8520354906b254d9,
title = "The effects of takeover request modalities on highly automated car control transitions",
abstract = "This study investigated the influences of takeover request (TOR) modalities on a drivers’ takeover performance after they engaged in non-driving related (NDR) tasks in highly automated driving (HAD). Visual, vibrotactile, and auditory modalities were varied in the design of the experiment under four conditions: no-task, phone conversation, smartphone interaction, and video watching tasks. Driving simulator experiments were conducted to analyze the drivers’ take-over performance by collecting data during the transition time of re-engaging control of the vehicle, the time taken to be on the loop, and time taken to be physically ready to drive. Data were gathered on the perceived usefulness, safety, satisfaction, and effectiveness for each TOR based on a self-reported questionnaire. Takeover and hands-on times varied considerably, as shown by high standard deviation values between modalities, especially for phone conversations and smartphone interaction tasks. Moreover, it was found that participants failed to take over control of the vehicle when they were given visual TORs for phone conversation and smartphone interaction tasks. The perceived safety and satisfaction varied for the NDR task. Results from the statistical analysis showed that the NDR task significantly influenced the takeover time, but there was no significant interaction effect between the TOR modalities and the NDR task. The results could potentially be applied to the design of safe and efficient transitions of highly controlled, automated driving, where drivers are enabled to engage in NDR tasks.",
author = "Yoon, {Sol Hee} and Kim, {Young Woo} and Ji, {Yong Gu}",
year = "2019",
month = "2",
doi = "10.1016/j.aap.2018.11.018",
language = "English",
volume = "123",
pages = "150--158",
journal = "Accident Analysis and Prevention",
issn = "0001-4575",
publisher = "Elsevier Limited",

}

The effects of takeover request modalities on highly automated car control transitions. / Yoon, Sol Hee; Kim, Young Woo; Ji, Yong Gu.

In: Accident Analysis and Prevention, Vol. 123, 02.2019, p. 150-158.

Research output: Contribution to journalArticle

TY - JOUR

T1 - The effects of takeover request modalities on highly automated car control transitions

AU - Yoon, Sol Hee

AU - Kim, Young Woo

AU - Ji, Yong Gu

PY - 2019/2

Y1 - 2019/2

N2 - This study investigated the influences of takeover request (TOR) modalities on a drivers’ takeover performance after they engaged in non-driving related (NDR) tasks in highly automated driving (HAD). Visual, vibrotactile, and auditory modalities were varied in the design of the experiment under four conditions: no-task, phone conversation, smartphone interaction, and video watching tasks. Driving simulator experiments were conducted to analyze the drivers’ take-over performance by collecting data during the transition time of re-engaging control of the vehicle, the time taken to be on the loop, and time taken to be physically ready to drive. Data were gathered on the perceived usefulness, safety, satisfaction, and effectiveness for each TOR based on a self-reported questionnaire. Takeover and hands-on times varied considerably, as shown by high standard deviation values between modalities, especially for phone conversations and smartphone interaction tasks. Moreover, it was found that participants failed to take over control of the vehicle when they were given visual TORs for phone conversation and smartphone interaction tasks. The perceived safety and satisfaction varied for the NDR task. Results from the statistical analysis showed that the NDR task significantly influenced the takeover time, but there was no significant interaction effect between the TOR modalities and the NDR task. The results could potentially be applied to the design of safe and efficient transitions of highly controlled, automated driving, where drivers are enabled to engage in NDR tasks.

AB - This study investigated the influences of takeover request (TOR) modalities on a drivers’ takeover performance after they engaged in non-driving related (NDR) tasks in highly automated driving (HAD). Visual, vibrotactile, and auditory modalities were varied in the design of the experiment under four conditions: no-task, phone conversation, smartphone interaction, and video watching tasks. Driving simulator experiments were conducted to analyze the drivers’ take-over performance by collecting data during the transition time of re-engaging control of the vehicle, the time taken to be on the loop, and time taken to be physically ready to drive. Data were gathered on the perceived usefulness, safety, satisfaction, and effectiveness for each TOR based on a self-reported questionnaire. Takeover and hands-on times varied considerably, as shown by high standard deviation values between modalities, especially for phone conversations and smartphone interaction tasks. Moreover, it was found that participants failed to take over control of the vehicle when they were given visual TORs for phone conversation and smartphone interaction tasks. The perceived safety and satisfaction varied for the NDR task. Results from the statistical analysis showed that the NDR task significantly influenced the takeover time, but there was no significant interaction effect between the TOR modalities and the NDR task. The results could potentially be applied to the design of safe and efficient transitions of highly controlled, automated driving, where drivers are enabled to engage in NDR tasks.

UR - http://www.scopus.com/inward/record.url?scp=85057332999&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85057332999&partnerID=8YFLogxK

U2 - 10.1016/j.aap.2018.11.018

DO - 10.1016/j.aap.2018.11.018

M3 - Article

C2 - 30503824

AN - SCOPUS:85057332999

VL - 123

SP - 150

EP - 158

JO - Accident Analysis and Prevention

JF - Accident Analysis and Prevention

SN - 0001-4575

ER -