The Research-Practice Gap: An Explanatory Factor for Automotive HMI Customers’ Complaints?

International audienceAutomotive HMI development was historically feature and technology-driven. Over time, we witnessed a shift in focus from physical to cognitive issues, especially due to technology evolution and embedded HMI complexification. This made adapting automotive HMI development process a necessity to address human factors and cognitive ergonomics challenges in design/evaluation phases. It is in this context that car manufacturers enhanced the traditional systems engineering logic (V like model) thanks to the User-Centered Design cycle (UCD). But, despite this user centric approach, some customers' complaints and usability issues concerning automotive HMI are reported. Why is it so? To answer this question a research is underway. In this article, we (1) describe the work that led us to consider the research-practice gap as a candidate factor explaining why the user centric approach fails and (2) describe what we are planning to do as next steps

Validation of a simulation algorithm for safety-critical human multitasking.

Multitasking has become surprisingly present in our life. This is mostly due to the fact that nowadays most of our activities involve the interaction with one or more devices. In such a context the brain mechanism of selective attention plays a key role in determining the success of a human’s interaction with a device. Indeed, it is a resource to be shared among the concurrent tasks to be performed, and the sharing of attention turns out to be a process similar to process scheduling in operating systems. In order to study human multitasking situations in which a user interacts with more than one device at the same time, we proposed in a previous work an algorithm for simulating human selective attention. Our algorithm focuses, in particular, on safety-critical human multitasking, namely situations in which some of the tasks the user is involved in may lead to dangerous consequences if not executed properly. In this paper, we present the validation of such an algorithm against data gathered from an experimental study performed with real users involved concurrently in a “main” task perceived as safety-critical and in a series of “distractor” tasks having different levels of cognitive load