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Conceptualization of Human Factors in Automated Driving by Work Domain Analysis
Technical Paper
2020-01-1202
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The increasing automation of driving functionalities is one of the most important trends in the automotive industry. The trend is moving towards systems which allow the driver to be absent from the active driving task. During the process, on one hand, the human driver more and more relies upon the driving automation to perform the dynamic driving tasks. Therefore, the driver needs to trust the driving automation. On the other hand, even the high driving automation (e.g. SAE Level 4) can only performs its functionality within the specific operational design domain and the driving automation relies upon the human driver to handle events when the vehicle operates outside the domain. What’s more, for the lower level driving automation, the driver still needs to assume some fallback responsibility, and may be required to react promptly when the driving automation even inside the operational design domain is inadequate to operate the vehicle. From above, it is obvious that the interactions between human driver and driving automation are becoming complicated and less transparent. Hazardous events may occur due to the human factors in these interactions. However, human beings are subject to the context in which they work, and their behaviors are not random. Thus, to identify these factors in the early design phase can benefit the design of automated driving system. This contribution proposes to use Work Domain Analysis to analyze human factors in automated driving. As the 1st dimension of Cognitive Work Analysis, an overarching analytic framework for human behavior constraints, Work Domain Analysis can identify potential problems in driver automation interaction in a proactive manner and thus can benefit the design of driving automation.
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Authors
Citation
Zhang, Y., Lintern, G., Gao, L., and Zhang, Z., "Conceptualization of Human Factors in Automated Driving by Work Domain Analysis," SAE Technical Paper 2020-01-1202, 2020, https://doi.org/10.4271/2020-01-1202.Also In
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