Using space windowing for a preliminary analysis of complex time data in human component system studies. Examples with eye-tracking in advertising and car/head movements in driving

Empirical studies of human systems often involve recording multidimensional signals because the system components may require physical measurements (e.g., temperature, pressure, body movements and/or movements in the environment) and physiological measurements (e.g., electromyography or electrocardiography). Analysis of such data becomes complex if both the multifactor aspect and the multivariate aspect are retained. Three examples are used to illustrate the role of fuzzy space windowing and the large number of data analysis paths. The first example is a classic simulated data set found in the literature, which we use to compare several data analysis paths generated with principal component analysis and multiple correspondence analysis with crisp and fuzzy windowing. The second example involves eye-tracking data based on advertising, with a focus on the case of one category variable, but with the possibility of several space windowing models and time entities. The third example concerns car and head movement data from a driving vigilance study, with a focus on the case involving several quantitative variables. The notions of analysis path multiplicity and information are discussed both from a general perspective and in terms of our two real examples.     

Driver–vehicle cooperation: a hierarchical cooperative control architecture for automated driving systems

The concept of automated driving changes the way humans interact with their cars. However, how humans should interact with automated driving systems remains an open question. Cooperation between a driver and an automated driving system—they exert control jointly to facilitate a common driving task for each other—is expected to be a promising interaction paradigm that can address human factors issues caused by driving automation. Nevertheless, the complex nature of automated driving functions makes it very challenging to apply the state-of-the-art frameworks of driver–vehicle cooperation to automated driving systems. To meet this challenge, we propose a hierarchical cooperative control architecture which is derived from the existing architectures of automated driving systems. Throughout this architecture, we discuss how to adapt system functions to realize different forms of cooperation in the framework of driver–vehicle cooperation. We also provide a case study to illustrate the use of this architecture in the design of a cooperative control system for automated driving. By examining the concepts behind this architecture, we highlight that the correspondence between several concepts of planning and control originated from the fields of robotics and automation and the ergonomic frameworks of human cognition and control offers a new opportunity for designing driver–vehicle cooperation.

     

Multivariate analysis of human behavior data using fuzzy windowing: Example with driver–car–environment system

In most human component system studies performed in simulators, several factors (or independent variables) (at least two, i.e., individual and time) and many variables (or dependent variables) are present. Large and complex databases have to be analyzed. Instead of using rather automatic procedures, this article suggest that, for a very first analysis at least, the human being must be present and he/she must choose a method being adapted to the data, which is different to run a method supposing that the data fit such or such model. This article suggests starting the analysis while keeping both the multifactorial (MF) and multivariate (MV) aspects. To achieve this aim, with the possibility to show nonlinear relationships, a MFMV exploration of the experimental database is performed using the pair (fuzzy space windowing, Multiple Correspondence Analysis). Then may come an inference analysis. This long (due to multiple large graphical views) but rich procedure is illustrated and discussed using a car driving study example.