Abstract
For SAE Level 3 of automated driving (AD), human drivers are considered as the potential fallback and are expected to take over the vehicle when the system issues a take-over request (TOR). Thus, the evaluation take-over performance with regards to driving safety is the key to designing Level 3 AD systems. This study discusses the influence of the relative position of surrounding traffic on drowsy drivers’ take-over performance. An parameter of the relative position, which is defined as the average of the shortest distances between the ego vehicle and the surrounding vehicles (ASDESV) was proposed. Through objective and subjective methods, the ASDESV was proved appropriate to evaluate the influence of the relative position of surrounding traffic on take-over performance and longer ASDESV was proved to conduce to better take-over performance. This study proved that the relative position of surrounding traffic had important effects on take-over performance and driving safety.
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References
Zeeb, K., Buchner, A., Schrauf, M.: What determines the take-over time? an integrated model approach of driver take-over after automated driving. Accid. Anal. Prev. 78, 212–221 (2015)
Gold, C., Naujoks, F., Radlmayr, J., Bellem, H., Jarosch, O.: Testing scenarios for human factors research in level 3 automated vehicles. In: Stanton, N.A. (ed.) AHFE 2017. AISC, vol. 597, pp. 551–559. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-60441-1_54
Gold, C., Happee, R., Bengler, K.: Modeling take-over performance in level 3 conditionally automated vehicles. Accid. Anal. Prev. 116, 3–13 (2018)
Naujoks, F., Mai, C., Neukum, A.: The effect of urgency of take-over requests during highly automated driving under distraction conditions. Adv. Hum. Aspects Transp. 7, Part I, 431 (2014)
Walch, M., et al.: Autonomous driving: investigating the feasibility of bimodal take-over requests. Int. J. Mob. Hum. Comput. Inter. (IJMHCI) 9(2), 58–74 (2017)
Radlmayr, J., et al.: How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 58. no. 1. Sage CA: Sage Publications, Los Angeles (2014)
Jamson, A.H., et al.: Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions. Transp. Res. Part C Emerg. Technol. 30, 116–125 (2013)
Tanshi, F., Söffker, D.: Modeling drivers’ takeover behavior depending on the criticality of driving situations and the complexity of secondary tasks. In: 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). IEEE (2019)
Du, N., Yang, X.J., Zhou, F.: Psychophysiological responses to takeover requests in conditionally automated driving. Accid. Anal. Prev. 148, 105804 (2020)
Du, N., et al.: Evaluating effects of cognitive load, takeover request lead time, and traffic density on drivers’ takeover performance in conditionally automated driving. In: 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (2020)
Gold, C., et al.: Taking over control from highly automated vehicles in complex traffic situations: the role of traffic density. Hum. Fact. 58(4), 642–652 (2016)
Scharfe, M.S., Lisa, K.Z., Russwinkel, N.: The impact of situational complexity and familiarity on takeover quality in uncritical highly automated driving scenarios. Information 11(2), 115 (2020)
Brandenburg, S., Chuang, L.: Take-over requests during highly automated driving: how should they be presented and under what conditions? Transport. Res. F: Traffic Psychol. Behav. 66, 214–225 (2019)
Gold, C., et al.: “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. Sage CA: Sage Publications, Los Angeles (2013)
Li, Q., et al.: Drivers’ visual-distracted take-over performance model and its application on adaptive adjustment of time budget. Accid. Anal. Prev. 154 (2021)
Acknowledgments
This work was supported by Tsinghua University-Toyota Joint Research Center for AI Technology of Automated Vehicle (TTAD2020-05) and the National Natural Science Foundation of China (No. 51965055 and 52072214).
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Li, Q. et al. (2021). Influence of the Relative Position of Surrounding Traffic on Drivers’ Take-Over Performance. In: Stanton, N. (eds) Advances in Human Aspects of Transportation. AHFE 2021. Lecture Notes in Networks and Systems, vol 270. Springer, Cham. https://doi.org/10.1007/978-3-030-80012-3_46
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DOI: https://doi.org/10.1007/978-3-030-80012-3_46
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