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their attention between competing sources of in-
formation. Driving is largely a performance and
time-critical visual-manual task with significant
spatial components (e.g. estimating distances).
Consequently, secondary tasks must not be overly
time consuming to achieve and/or require atten-
tional resources that are largely visual/manual/
spatial in nature, if they are to avoid having a
significant impact on primary driving.
A second fundamental issue is the amount of in-
formation processing or decision making required
for successful task performance, known as mental
workload (Wickens et al., 2004). Novel in-car
computing systems may provide functionality of
utility to a driver and/or passengers, but interac-
tion with the technology will inevitably increase
(or in some cases decrease) overall workload.
Context is very important here, as driving is a
task in which workload varies considerably from
one situation to another (compare driving in city
traffic versus on the motorway). In this respect,
certain authors (e.g. Jones, 2002; Green, 2004;
Markkula, Kutila, and Engström, 2005) have
taken the view that workload managers must be
developed which make real-time predictions of
the workload a driver is under and only present
information or enable interactions to occur when
overall workload is considered to be at an accept-
able level. As an example, an incoming phone call
may be sent straight to voice mail when the driver
is considered to be particularly loaded (e.g. when
driving in an unfamiliar city), but may be permitted
in a lower workload scenario (e.g. driving along
a dual carriageway and following a lead vehicle).
Simple workload managers already exist in some
vehicles (e.g. http://driving.timesonline.co.uk/
article/0,,12929-2319048,00.html, September
2006), nevertheless, there are several complex
research issues which must be addressed to fully
realise the benefits of adaptive software in this
context. For instance, workload managers need a
comprehensive and accurate model of the driver,
driving tasks and the driving environment. Given
the vast range of variables of relevance to these
categories, many of which do not lend themselves
to accurate and reliable measurement, extensive
workload managers are likely to remain in the
research domain for several years.
Equipment
The driving situation necessitates the use of in-
put and output devices which are familiar to the
majority of user-interface designers (pushbuttons,
rockers, rotaries, LCDs, touchscreens, digitised/
synthesised speech), together with equipment
which is perhaps less known. For instance, there
is a considerable research literature regarding the
use of Head-Up Displays (HUDs) within vehicles.
A HUD uses projection technology to provide
virtual images which can be seen in the driver's
line of sight through the front windscreen (see
Figure 1). They are widely used within the aviation
and military fields, and are now beginning to be
implemented on a large-scale within road-based
vehicles. HUDs will potentially allow drivers to
continue attending to the road ahead whilst taking
in secondary information more quickly (Ward and
Parkes, 1994). As a consequence, they may be
most applicable to situations in which the visual
modality is highly loaded (e.g. urban driving),
and for older drivers who experience difficulties
in rapidly changing accommodation between near
and far objects (Burns, 1999).
From a human-focused perspective, there are
clear dangers in simply translating a technology
from one context to another, given that vehicle-
based HUDs will be used by people of varying
perceptual/cognitive capabilities within an envi-
ronment where there is a complex, continually
changing visual scene. Specifically, researchers
have established that poorly designed HUDs can
mask critical road information, disrupt distance
perception and visual scanning patterns, and
negatively affect the ability of drivers to detect
hazards in their peripheral vision (known as per-
ceptual tunnelling) - summarised by Tufano, 1997;
and Ward and Parkes, 1994). Critical design fac-
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