Take-Over Time: A Cross-Cultural Study of Take-Over Responses in Highly Automated Driving
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Equipment
2.3. Non-Driving Tasks
2.4. Procedure
2.5. Response Measures
2.6. Statistical Analysis
3. Results
3.1. Take-Over Responses
3.2. Take-Over Time
3.3. Measures of Workload for Non-Driving Tasks
3.3.1. Task Completion Rate
3.3.2. NASA TLX
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Merat, N.; Jamson, A.H.; Lai, F.C.H.; Daly, M.; Carsten, O.M.J. Transition to manual: Driver behaviour when resuming control from a highly automated vehicle. Transp. Res. Part F Traffic Psychol. Behav. 2014, 27, 274–282. [Google Scholar] [CrossRef] [Green Version]
- De Winter, J.C.F.; Happee, R.; Martens, M.H.; Stanton, N.A. Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence. Transp. Res. Part F Traffic Psychol. Behav. 2014, 27, 196–217. [Google Scholar] [CrossRef] [Green Version]
- Gold, C.; Körber, M.; Lechner, D.; Bengler, K. Taking over Control from Highly Automated Vehicles in Complex Traffic Situations. Hum. Factors 2016, 58, 642–652. [Google Scholar] [CrossRef] [PubMed]
- Clark, H.; Feng, J. Age differences in the takeover of vehicle control and engagement in non-driving-related activities in simulated driving with conditional automation. Accid. Anal. Prev. 2017, 106, 468–479. [Google Scholar] [CrossRef] [PubMed]
- Zhang, B.; de Winter, J.; Varotto, S.; Happee, R.; Martens, M. Determinants of take-over time from automated driving: A meta-analysis of 129 studies. Transp. Res. Part F Traffic Psychol. Behav. 2019, 64, 285–307. [Google Scholar] [CrossRef]
- Merat, N.; Jamson, A.H.; Lai, F.C.H.; Carsten, O. Highly automated driving, secondary task performance, and driver state. Hum. Factors 2012, 54, 762–771. [Google Scholar] [CrossRef] [PubMed]
- Radlmayr, J.; Gold, C.; Lorenz, L.; Farid, M.; Bengler, K. How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2014, 58, 2063–2067. [Google Scholar] [CrossRef] [Green Version]
- Gold, C.; Berisha, I.; Bengler, K. Utilization of drivetime—Performing non-driving related tasks while driving highly automated. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2015, 59, 1666–1670. [Google Scholar] [CrossRef]
- Zeeb, K.; Buchner, A.; Schrauf, M. Is take-over time all that matters? the impact of visual-cognitive load on driver take-over quality after conditionally automated driving. Accid. Anal. Prev. 2016, 92, 230–239. [Google Scholar] [CrossRef] [PubMed]
- Körber, M.; Gold, C.; Lechner, D.; Bengler, K. The influence of age on the take-over of vehicle control in highly automated driving. Transp. Res. Part F Traffic Psychol. Behav. 2016, 39, 19–32. [Google Scholar] [CrossRef] [Green Version]
- Petermeijer, S.; Doubek, F.; De Winter, J. Driver response times to auditory, visual, and tactile take-over requests: A simulator study with 101 participants. In Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada, 5–8 October 2017; pp. 1505–1510. [Google Scholar] [CrossRef] [Green Version]
- Wandtner, B.; Schömig, N.; Schmidt, G. Effects of Non-Driving Related Task Modalities on Takeover Performance in Highly Automated Driving. Hum. Factors 2018, 60, 870–881. [Google Scholar] [CrossRef]
- Li, S.; Blythe, P.; Guo, W.; Namdeo, A.; Edwards, S.; Goodman, P.; Hill, G. Evaluation of the effects of age-friendly human-machine interfaces on the driver’s takeover performance in highly automated vehicles. Transp. Res. Part F Traffic Psychol. Behav. 2019, 67, 78–100. [Google Scholar] [CrossRef]
- Miller, D.; Johns, M.; Ive, H.P.; Gowda, N.; Sirkin, D.; Sibi, S.; Mok, B.; Aich, S.; Ju, W. Exploring Transitional Automation with New and Old Drivers. SAE Tech. Pap. 2016. [Google Scholar] [CrossRef]
- Hergeth, S.; Lorenz, L.; Krems, J.F.; Toenert, L. Effects of Take-Over Requests and Cultural Background on Automation Trust in Highly Automated Driving. In Proceedings of the 8th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, Salt Lake City, UT, USA, 22–25 June 2015; pp. 331–337. [Google Scholar] [CrossRef] [Green Version]
- Wang, W.; Cheng, Q.; Li, C.; André, D.; Jiang, X. A cross-cultural analysis of driving behavior under critical situations: A driving simulator study. Transp. Res. Part F Traffic Psychol. Behav. 2019, 62, 483–493. [Google Scholar] [CrossRef]
- Miller, E.E.; Boyle, L.N.; Hwang, S.; Sodnik, J.; Cegovnik, T. Takeover response: Differences between US and Slovenia. In Proceedings of the Adjunct Proceedings—11th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, Utrecht, The Netherlands, 22–25 September 2019; pp. 227–231. [Google Scholar]
- Hart, S.G.; Staveland, L.E. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. Adv. Psychol. 1988, 52, 139–183. [Google Scholar] [CrossRef]
- Seabold, S.; Perktold, J. Statsmodels: Econometric and Statistical Modeling with Python. In Proceedings of the 9th Python in Science Conference, Austin, TX, USA, 28 June–3 July 2010; pp. 92–96. [Google Scholar]
- Jamson, A.H.; Merat, N.; Carsten, O.M.J.; Lai, F.C.H. Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions. Transp. Res. Part C Emerg. Technol. 2013, 30, 116–125. [Google Scholar] [CrossRef] [Green Version]
- Naujoks, F.; Purucker, C.; Neukum, A. Secondary task engagement and vehicle automation- Comparing the effects of different automation levels in an on-road experiment. Transp. Res. Part F Traffic Psychol. Behav. 2016, 38, 67–82. [Google Scholar] [CrossRef]
Total | Younger | Older | |||
---|---|---|---|---|---|
SI | US | SI | US | US | |
n | 18 | 20 | 18 | 10 | 10 |
Male | 13 | 11 | 13 | 3 | 8 |
Ages | 19–39 | 19–57 | 19–39 | 46–57 | |
Mean age | 26 | 41 | 26 | 32 | 51 |
SD age | 5 | 11 | 5 | 7.8 | 4.6 |
Total | Crashed | Distracted at TOR | ||||
---|---|---|---|---|---|---|
Response | SI | US | SI | US | SI | US |
None | – | 6 | 6 | – | 5 | |
Brake | 11 | 14 | 7 | 6 | 6 | 8 |
Steer | 7 | – | 4 | – | 5 | – |
Independent Variables | Task Completion | ||||
---|---|---|---|---|---|
Est. | SE | z Value | p Value | 95% CI | |
(Intercept) | 0.573 | 0.047 | 12.170 | 0.000 | [0.481, 0.666] |
Location (US) | 0.099 | 0.053 | 1.880 | 0.060 | [−0.004, 0.203] |
Task (base: A/C) | |||||
contact | 0.403 | 0.057 | 7.126 | 0.000 | [0.292, 0.514] |
dial | 0.169 | 0.057 | 2.979 | 0.003 | [0.058, 0.280] |
playlist | 0.391 | 0.057 | 6.897 | 0.000 | [0.280, 0.502] |
radio | −0.077 | 0.057 | −1.345 | 0.179 | [−0.189, 0.035] |
Age (older: 46–57) | −0.117 | 0.058 | −2.018 | 0.044 | [−0.231, −0.003] |
Gender (female) | −0.020 | 0.045 | −0.459 | 0.647 | [−0.108, 0.067] |
Task Completion Rate | NASA TLX Index | ||||||
---|---|---|---|---|---|---|---|
Group 1 | Group 2 | Mean Diff | pcorr | 95% CI | Mean Diff | pcorr | 95% CI |
A/C | contact | 0.40 | 0.001 | [0.2436, 0.5621] | −0.25 | 0.001 | [−0.3876, −0.111] |
A/C | dial | 0.17 | 0.030 | [0.0103, 0.3288] | −0.12 | 0.114 | [−0.2598, 0.0168] |
A/C | playlist | 0.39 | 0.001 | [0.2317, 0.5502] | −0.27 | 0.001 | [−0.4025, −0.1279] |
A/C | radio | −0.08 | 0.67 | [−0.2377, 0.0847] | −0.08 | 0.546 | [−0.2113, 0.0615] |
contact | dial | −0.23 | 0.001 | [−0.3916, −0.075] | 0.13 | 0.073 | [−0.0071, 0.2626] |
contact | playlist | −0.01 | 0.9 | [−0.1702, 0.1464] | −0.02 | 0.9 | [−0.1497, 0.1179] |
contact | radio | −0.48 | 0.001 | [−0.6396, −0.3191] | 0.17 | 0.004 | [0.0415, 0.3072] |
dial | playlist | 0.22 | 0.002 | [0.0631, 0.3797] | −0.14 | 0.029 | [−0.2775, −0.0098] |
dial | radio | −0.25 | 0.001 | [−0.4063, −0.0858] | 0.05 | 0.855 | [−0.0863, 0.1795] |
playlist | radio | −0.47 | 0.001 | [−0.6277, −0.3072] | 0.19 | 0.001 | [0.0584, 0.3221] |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Strle, G.; Xing, Y.; Miller, E.E.; Boyle, L.N.; Sodnik, J. Take-Over Time: A Cross-Cultural Study of Take-Over Responses in Highly Automated Driving. Appl. Sci. 2021, 11, 7959. https://doi.org/10.3390/app11177959
Strle G, Xing Y, Miller EE, Boyle LN, Sodnik J. Take-Over Time: A Cross-Cultural Study of Take-Over Responses in Highly Automated Driving. Applied Sciences. 2021; 11(17):7959. https://doi.org/10.3390/app11177959
Chicago/Turabian StyleStrle, Gregor, Yilun Xing, Erika E. Miller, Linda Ng Boyle, and Jaka Sodnik. 2021. "Take-Over Time: A Cross-Cultural Study of Take-Over Responses in Highly Automated Driving" Applied Sciences 11, no. 17: 7959. https://doi.org/10.3390/app11177959
APA StyleStrle, G., Xing, Y., Miller, E. E., Boyle, L. N., & Sodnik, J. (2021). Take-Over Time: A Cross-Cultural Study of Take-Over Responses in Highly Automated Driving. Applied Sciences, 11(17), 7959. https://doi.org/10.3390/app11177959