Title
Attuning in-car user interfaces to the momentary cognitive load
Author
Hoedemaeker, M.
Neerincx, M.A.
Publication year
2007
Abstract
Cars, trucks and busses are more and more equipped with functions and services that drivers are supposed to operate and understand. The most important developments in this area are the Advanced Driver Assistance Systems (ADAS) and In Vehicle Information Systems (IVIS). In order to make sure that the driver understands and appreciates (comfort) these services and traffic safety is not at risk (distraction, workload), the HMI's (Human Machine Interfaces) of all these functions should be attuned to each other, to the driver, and to the context. For attuning the functions to each other, a HMI platform is needed on which these functions are integrated. For attuning the functions to the driver it is necessary to have knowledge about the momentary state of the driver and of the intentions of the driver at a certain moment. For attuning the functions to the context, it is required to sense the relevant environmental conditions or states. This paper shows that a recent cognitive task load model from process control domain can be applied for the design of adaptive in-car user interfaces. Furthermore, current developments of such interfaces are being discussed. © Springer-Verlag Berlin Heidelberg 2007.
Subject
Traffic
Adaptive user interface
Central management
In-car services
Workload
Cognitive systems
Drive-in facilities
Information systems
Trucks
Adaptive user interface
Advanced Driver Assistance Systems (ADAS)
Central management
In-car services
User interfaces
traffic
in-vehicle information systems
user interfaces
adaptive interfaces
To reference this document use:
http://resolver.tudelft.nl/uuid:169084ce-c570-48c3-a17b-afc649efa70b
DOI
https://doi.org/10.1007/978-3-540-73216-7_32
TNO identifier
240464
ISBN
9783540732150
ISSN
0302-9743
Source
3rd International Conference on Foundations of Augmented Cognition, FAC 2007, 22 July 2007 through 27 July 2007, Beijing, 4565 LNAI, 286-293
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Document type
conference paper