Abstract
Although automated driving systems can perform dynamic driving tasks, at its lower levels, human drivers must still take over control of the vehicle whenever a Take Over Request (TOR) is issued. Human factors such as drowsiness may affect driver performance in responding to TOR. Results of research on the effects of different drowsiness levels on driver takeover performance appear inconsistent, for two possible reasons: 1. Some studies triggered TORs after a predefined duration of driving, while others triggered TORs based on a certain level of drowsiness. 2. Differences in drowsiness levels, which may have nonlinear effects on takeover performance, were not adequately considered. To investigate these inconsistencies, this experimental study recruited 40 participants and adopted a repeated-measures design. A total of 1436 available datasets, including a series of takeover performance measures and drowsiness measures, were collected. Analyses based on driving duration as well as drowsiness levels produced the following results: 1. Driving duration had a significant effect on drowsiness level but not on takeover performance. 2. Although mediumly drowsy drivers tended to sacrifice takeover quality for a fast reaction to ensure safety, highly drowsy drivers reacted significantly more slowly to TOR and were not able to maintain a safety margin comparable to that of drivers who were not highly drowsy. These findings have important implications for both researchers who are developing experimental studies to examine the effects of drowsiness on takeover performance and designers who use this information to design driver assistance systems.
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Notes
- 1.
Analysis of EOG and ECG data is beyond the scope of this paper, and the results will be published separately.
References
Dhillon, B.: Human Reliability and Error in Transportation Systems. Springer Series - Reliability Engineering. Springer, Ottawa (2007). https://doi.org/10.1007/978-1-84628-812-8
Fagnant, D.J., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. Part A: Policy Pract. 77, 167–181 (2015)
SAE International: Surface vehicle recommended practice, taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles, J3016. Revised September (2016)
Victor, T.W., Tivesten, E., Gustavsson, P., Johansson, J., Sangberg, F., Ljung Aust, M.: Automation expectation mismatch: Incorrect prediction despite eyes on threat and hands on wheel. Hum. Fact. 60(8), 1095–1116 (2018)
Radlmayr, J., Fischer, F.M., Bengler, K.: The influence of non-driving related tasks on driver availability in the context of conditionally automated driving. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds.) IEA 2018. AISC, vol. 823, pp. 295–304. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96074-6_32
Wu, Y., Kihara, K., Takeda, Y., Sato, T., Akamatsu, M., Kitazaki, S.: Age-related differences in effects of non-driving related tasks on takeover performance in automated driving. J. Saf. Res. 72, 231–238 (2020)
Schömig, N., Hargutt, V., Neukum, A., Petermann-Stock, I., Othersen, I.: The interaction between highly automated driving and the development of drowsiness. Procedia Manufact. 3, 6652–6659 (2015)
Saxby, D.J., Matthews, G., Hitchcock, E.M., Warm, J.S., Funke, G.J., Gantzer, T.: Effect of active and passive fatigue on performance using a driving simulator. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 52, no. 21, pp. 1751–1755. Sage Publications, Los Angeles (2008)
Saxby, D.J., Matthews, G., Warm, J.S., Hitchcock, E.M., Neubauer, C.: Active and passive fatigue in simulated driving: discriminating styles of workload regulation and their safety impacts. J. Exp. Psychol.: Appl. 19(4), 287 (2013)
Gonçalves, J., Happee, R., Bengler, K.: Drowsiness in conditional automation: proneness, diagnosis and driving performance effects. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 873–878. IEEE, Rio de Janeiro (2016)
Jarosch, O., Bellem, H., Bengler, K.: Effects of task-induced fatigue in prolonged conditional automated driving. Hum. Fact. 61(7), 1186–1199 (2019)
Feldhütter, A., Gold, C., Schneider, S., Bengler, K.: How the duration of automated driving influences take-over performance and gaze behavior. In: Schlick, C., et al. (eds.) Advances in Ergonomic Design of Systems, Products and Processes, pp. 309–318. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-53305-5_22
Feldhütter, A., Kroll, D., Bengler, K.: Wake up and take over! the effect of fatigue on the take-over performance in conditionally automated driving. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2080–2085. IEEE, Maui (2018)
Feldhütter, A., Ruhl, A., Feierle, A., Bengler, K.: The effect of fatigue on take-over performance in urgent situations in conditionally automated driving. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 1889–1894. IEEE, New Zealand (2019)
Johns, M.: Rethinking the assessment of sleepiness. Sleep Med. Rev. 2(1), 3–15 (1998)
Wu, Y., Kihara, K., Takeda, Y., Sato, T., Akamatsu, M., Kitazaki, S.: Assessing the mental states of fallback-ready drivers in automated driving by electrooculography. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 4018–4023. IEEE, New Zealand (2019a)
Åkerstedt, T., Gillberg, M.: Subjective and objective sleepiness in the active individual. Int. J. Neurosci. 52(1–2), 29–37 (1990)
Kaida, K., et al.: Validation of the Karolinska sleepiness scale against performance and EEG variables. Clin. Neurophysiol. 117(7), 1574–1581 (2006)
Kitajima, H., Numata, N., Yamamoto, K., Goi, Y.: Prediction of automobile driver sleepiness (1st report, rating of sleepiness based on facial expression and examination of effective predictor indexes of sleepiness). Trans. Jpn. Soc. Mech. Eng. Part C 63(613), 3059–3066 (1997)
Homma, R.: Evaluation of systems and strategies for driver assistance based on human characteristics. Doctoral thesis. The Graduate School of Human Sciences, Waseda University (2016)
Wu, Y., Kihara, K., Takeda, Y., Sato, T., Akamatsu, M., Kitazaki, S.: Effects of scheduled manual driving on drowsiness and response to take over request: a simulator study towards understanding drivers in automated driving. Accid. Anal. Prev. 124, 202–209 (2019b)
Razali, N.M., Wah, Y.B.: Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J. Stat. Model. Anal. 2(1), 21–33 (2011)
Karrer, K., Vöhringer-Kuhnt, T., Baumgarten, T., Briest, S.: The role of individual differences in driver fatigue prediction. In: Third International Conference on Traffic and Transport Psychology, Nottingham, UK, pp. 5–9 (2004)
Schmidt, J., Dreißig, M., Stolzmann, W., Rötting, M.: The influence of prolonged conditionally automated driving on the take-over ability of the driver. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 61, no. 1, pp. 1974–1978. Sage Publications, Los Angeles (2017)
Petermeijer, S., Bazilinskyy, P., Bengler, K., De Winter, J.: Take-over again: investigating multimodal and directional TORs to get the driver back into the loop. Appl. Ergon. 62, 204–215 (2017)
Gold, C., Damböck, D., Lorenz, L., Bengler, K.: “Take over!” How long does it take to get the driver back into the loop?. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 57, no. 1, pp. 1938–1942. Sage Publications, Los Angeles (2013)
Acknowledgement
This work was supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), “Large-scale Field Operational Test for Automated Driving Systems” (funding agency: NEDO).
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Wu, Y., Kihara, K., Takeda, Y., Sato, T., Akamatsu, M., Kitazaki, S. (2020). The Relationship Between Drowsiness Level and Takeover Performance in Automated Driving. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. Driving Behavior, Urban and Smart Mobility. HCII 2020. Lecture Notes in Computer Science(), vol 12213. Springer, Cham. https://doi.org/10.1007/978-3-030-50537-0_11
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