ANÁLISIS DE MUTACIONES EN RAS

An informative, aesthetically pleasing tool with which individuals are engaged and enjoy using may help to encourage efficient, environmentally-friendly driving styles, but that does not necessarily mean it will be appropriate for use in vehicles on the road. The practice of Hypermiling (see www.hypermiler.co.uk) provides an interesting example of where a range of behaviours that have a significantly positive effect on energy conservation are not necessarily advisable due to safety reasons. Although over-inflating tyres, turning off the engine and free-wheeling downhill, and drafting as close as possible to the vehicle in front in order to make use of the slipstream may be beneficial activities for reducing fuel

consumption, they present a trade-off in terms of road safety (Barkenbus, 2010; Edmunds.com, 2009).

The driving task is highly complex, comprising over 1600 separate tasks (Walker, Stanton, & Young, 2001c). Being the safety critical domain it is, the addition of more information to an already complex array of in-car systems should be very carefully considered if we are to avoid increasing workload and distraction, both of which are causal factors for accidents (Birrell & Young, 2011; Pradhan et al., 2011). Take the Wada et al. (2011) study described above; although subjective workload ratings decreased with time in the control and non-adaptive display groups, those with the adaptive tool demonstrated higher workload scores. Importantly, these scores did not decrease with time. This may be problematic; people have limited cognitive resources, and as such, if the non- driving task demands increase (such as can happen when required to attend to an additional ‘eco’ display), attentional resources for other tasks may decrease (Wickens & Carswell, 1997). This could result in the possibility that the concurrent feedback will interfere with on-going task performance, a principle that has been demonstrated both within and outside of the driving domain (Arroyo, Sullivan, & Selker, 2006; Corbett & Anderson, 2001; Stanton, Dunoyer, & Leatherland, 2011). Furthermore, Groeger (2000) describes driving as a goal- directed task, with multiple goals (e.g. speed, safety, economy) active simultaneously that at any point in time may be in conflict with each other. Highlighting the importance of economy goals may, therefore, have a detrimental effect on performance in other aspects of driving, for example safety.

Despite the possibility of conflict arising in the driving task, safe driving and economical driving do have significant overlaps (Young et al., 2011). Aggressive driving is seen as both dangerous (Young et al., 2011) and uneconomical (Ericsson, 2001) due to characteristically high acceleration and deceleration rates, and high engine speed and power demands. It is possible then to encourage both safe and economical driving through supporting eco-driving; for example, Hedges and Moss (Hedges & Moss, 1996) showed that after supplying eco-training to Parcelforce van drivers accident rates dropped by 40% and fuel efficiency increased by 50%. Moreover, Haworth and Symmons (2001) demonstrated a 35% reduction in accident rates alongside reductions in fuel

consumption (11%) and emission volumes (up to 50%) following similar training. Although these studies demonstrate some of the joint benefits of certain driving styles, they are both examples of antecedent strategies, that is they both employed pre-task driver training, not concurrent feedback, thus they do not address the issue of distraction, a point noted by Haworth and Symmons (2001).

The distractive qualities of an in-car information system have been investigated by a number of researchers (e.g. Donmez, Boyle, & Lee, 2007; Harms & Patten, 2003; Horberry, Anderson, Regan, Triggs, & Brown, 2006; Horberry, Stevens, Burnett, Cotter, & Robbins, 2008; Lansdown, Brook-Carter, & Kersloot, 2004; Reyes & Lee, 2008), yet research primarily considering eco-feedback distraction effects is less abundant. As aforementioned, Wada et al. (2011) considered workload in their investigation of an adaptive co-feedback interface; however, this was relatively limited in its appraisal of distraction in that subjective workload scores were obtained only through questionnaires, not direct measurements of distraction. A study by Birrell and Young (2011; see also Young & Birrell, 2012) did directly assess the impact on both fuel use and safety in an investigation of two versions of a smart driving tool, i.e., a device that offers advice both on eco-driving matters (e.g. acceleration and deceleration rapidity) and on safety (e.g. lane departure, headway information). They found that participants with access to in-vehicle feedback displayed fewer speeding behaviours and fewer instances of aggressive acceleration and braking, beneficial for both safety and economy. Furthermore, drivers with the in-vehicle feedback also exhibited safer headway maintenance behaviours. These results were all obtained without significant increases in driver distraction. When investigating the efficacy with which participants performed a peripheral detection task while driving, Birrell and Young (2011) found that those with one of the two in-vehicle feedback systems investigated performed significantly better in an urban driving scenario, with no significant differences in other scenarios or with the other interface design. That the researchers examined two different interfaces again highlights the importance of the way in which information is presented; not only was one design superior in terms of the peripheral detection task results, that same design received significantly lower subjective workload ratings (Birrell & Young, 2011).

It is clear that the way in which an interface is designed can have huge implications on its ability to elicit target behaviours, its acceptance by users, and its propensity to cause distraction and confusion. Results from van der Voort et al.’s (2001) study, described above, led the authors to describe a set of user requirements for a fuel efficiency support tool:

 clear, accurate, non-contradictory information;  account for the context in which the car is situated;  not interfere with the driving task;

 work in urban and non-urban environments.

Similar sentiments were put forward by Harvey et al. (2011b) for the design of In-Vehicle Information Systems (IVIS). For such systems one of the main priorities must be to minimise conflicts with the primary driving task, thus reducing the likelihood of distraction. When designing such a system complexity is a major issue; that the driving context is highly complex necessarily means designing for usability in the driving context will be complex (Fastrez & Haué, 2008). As such the usability of an in-vehicle system must be defined specifically for the context of use (Harvey et al., 2011a; Harvey & Stanton, 2012), and to test such a system requires repeated usability evaluations at different stages of the development process (Mitsopoulos-Rubens, Trotter, & Lenné, 2011), with a variety of evaluation methods, for example focus groups, user tests, and expert evaluations, including both subjective and objective usability measures (Harvey et al., 2011a; Tango & Montanari, 2006).