Exploring factors in Human Cognition that affect Human Performance during Takeover in Autonomous Driving
DOI:
https://doi.org/10.54097/tr5g0e77Keywords:
Autonomous Vehicles, human-machine interface (HMI), Mandatory Transition, exploratory factor analysis (EFA).Abstract
Driver's mental load is an important influence on the design of the human-machine interface (HMI) for self-driving cars. During the autopilot-forced takeover process, aspects of the user's cognition, decision-making, attention, and emotional state affect the takeover performance. Therefore, the cognitive and psychological states of drivers must be considered in the HMI design of self-driving cars. And a good HMI design will reduce the user's mental burden and improve the takeover performance. In this study, a model affecting drivers' takeover performance was designed through human factors. And we refine the analysis from three aspects of cognitive load, emotional load, and environmental load by making subjective likert-style-questionnaires, derive the main influencing factors through the reliability analysis of the data, factor analysis, and other methods. This work finds that the clarity, comprehensiveness, and level of humanization of takeover messages, as well as the driver's arousal level when receiving the message, have the greatest impact on the driver's workload.
Downloads
References
Wu, C., Wu, H., & Lyu, N. (2018). Review of control switch and safety of human-computer driving intelligent vehicle [J]. Journal of Traffic and Transportation Engineering, 2018, 18 (6), 131 - 141.
Melnicuk, V., Thompson, S., Jennings, P., & Birrell, S. (2021). Effect of cognitive load on drivers' State and task performance during automated driving: Introducing a novel method for determining stabilization time following take-over of control. Accident; analysis and prevention, 151, 105967.
Maggi, D., Romano, R., & Carsten, O. (2022). Transitions Between Highly Automated and Longitudinally Assisted Driving: The Role of the Initiator in the Fight for Authority. Human factors, 64(3), 601–612.
Merat, N., Seppelt, B.D., Louw, T., Engström, J., Lee, J.D., Johansson, E., Green, C. A., Katazaki, S., Monk, C.A., Itoh, M., McGehee, D.V., Sunda, T., Unoura, K., Victor, T., Schieben, A., & Keinath, A. (2018). The “Out-of-the-Loop” concept in automated driving: proposed definition, measures and implications. Cognition, Technology & Work, 21, 87 - 98.
Endsley, M. R., & Kaber, D. B. (1999). Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics, 42 (3), 462 – 492. https: //doi.org/10.1080/001401399185595.
Blasch, E., Llinas, J., Lambert, D.A., Valin, P., Das, S.K., Chong, C., Kokar, M.M., & Shahbazian, E. (2010). High Level Information Fusion developments, issues, and grand challenges: Fusion 2010 panel discussion. 2010 13th International Conference on Information Fusion, 1 - 8.
Broadhurst, P. L. (1957). Emotionality and the Yerkes-Dodson Law. Journal of Experimental Psychology, 54 (5), 345 – 352.
Golestan, K., Soua, R., Karray, F., & Kamel, M. S. (2016). Situation awareness within the context of connected cars: A comprehensive review and recent trends. Inf. Fusion, 29, 68 - 83.
Reyes-Muñoz, A., & Guerrero-Ibáñez, J. (2022). Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey. Sensors (Basel, Switzerland), 22 (12), 4614.
Ma, G., Hao, Z., Wu, X., & Wang, X. (2020). An Optimal Electrical Impedance Tomography Drive Pattern for Human-Computer Interaction Applications. IEEE transactions on biomedical circuits and systems, 14 (3), 402 – 411.
Kunze, A., Summerskill, S. J., Marshall, R., & Filtness, A. J. (2019). Automation transparency: implications of uncertainty communication for human-automation interaction and interfaces. Ergonomics, 62 (3), 345 – 360.
Oh, S., Bae, C., Cho, J., Lee, S., & Jung, Y. (2021). Command Recognition Using Binarized Convolutional Neural Network with Voice and Radar Sensors for Human-Vehicle Interaction. Sensors (Basel, Switzerland), 21 (11), 3906.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
