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Perhaps the obvious tool to manipulate the vehicle performance experienced by participants would be real vehicles. The vehicle itself, and/or its fuel, could be varied, and its performance evaluated by drivers, either in naturalistic, real-world driving or in more controlled conditions on a test track. Driving simulation offers a third alternative. A driving simulator consists o f a physical representation of the driving position and controls (often a complete vehicle); a model o f a driving environment, through which the vehicle appears to the driver to move; a model of the vehicle dynamics, which translates control actions o f the driver into movement o f the vehicle through the virtual driving environment; and one or more cueing systems that create a perception of movement through the virtual environment. Driving simulation enables a high level o f experimental control - driving situations can be replicated from drive to drive and participant to participant with high reliability. In addition, it is possible to simulate driving situations in complete physical safety that might elicit unsafe driving behaviour from some participants. Driving simulation also offers high measurement accuracy, and the ability to manipulate aspects o f vehicle performance independently.

The three different options, field studies in a real car, test track studies in a real car, and driving simulation, each offer different trade-offs of internal and external validity. Internal validity (Campbell & Stanley, 1963) is the ability to plausibly demonstrate a causal relationship between manipulation of independent variables and measurement of dependent variables. A causal relationship may not be plausibly inferred if confounding variables, not controlled in the experiment, could also have caused the outcome. Among the potential threats to internal validity identified by Campbell and Stanley, and Cook and Campbell (1979), is history: factors that may change in the participant’s environment, between experimental treatments, that are outside the experimenter’s control. In a field study of naturalistic driving in real traffic, there are potentially many such factors: differences in the driving conditions, traffic density, routes followed, visibility,

weather conditions, etc. Many, but not all of these can be controlled in a test track experiment. However driving simulation offers the possibility to control the majority of them; in principle all but the confounding variables associated with the participants themselves. Thus, there is a progression towards higher internal validity moving from field study via test track experiment to driving simulation experiment.

External validity refers to the generalisability of the research findings (Campbell & Stanley, 1963). Internal and external validity tend to be inversely related, in the sense that the more control is imposed to increase internal validity, the less the conditions in an experiment may be representative of the real world situations to which the experimenter wishes to generalize the findings. In driving research, where the intention is to generalize the findings to real-world driving, a field study tends to have the highest external validity. The constraints associated with a test track (safety considerations, road layout, absence of traffic, inappropriate roadside visual environment, etc) may lead to poor external validity. In principle many of these constraints can be removed by using driving simulation.

However, the external validity of a driving simulation experiment may be threatened by lack of appropriate fidelity of the simulation. Fidelity refers to the degree to which the simulation provides physical experiences that match what the participant would experience in the situation to which the experimenter wants to generalize: typically, real-world driving in a real car. Fidelity (sometimes referred to as physical validity in the simulation literature (Jamson, 1999)) is a function of how accurately the various cueing systems in the simulator (visual, auditory, motion, tactile) reproduce the vehicle and environmental stimuli that are relevant to the research question. For example, speed perception is known to be dependent on optic flow in the periphery of the field of view. If speed perception is relevant to the research question, then a simulator with a narrow field of view would have low fidelity. One way to relate fidelity to external validity is to consider the behavioural validity of the simulator (Jamson, 1999). Behavioural validity refers to the degree to which the simulator elicits the same behaviour by the driver as would be shown in the real-world situation to which the experimenter intends to generalize the findings.

Behavioural validity itself can take two forms: absolute and relative (Kaptein, Theeuwes, & Van der Horst, 1996; Reymond & Kemeny, 2000). Absolute validity is achieved if the behaviour elicited in the simulator is quantitatively the same as would be shown in the real-world situation under all conditions relevant to the research question. Absolute validity is not easily achieved, but has been demonstrated in several research simulators for mean speed (Aim, 1995; Carsten, Groeger, Blana, & Jamson, 1997; Harms, 1996); speed control (Blaauw, 1982; Reed & Green, 1999); and route choice (van der Mede & Berkum, 1993). Relative validity represents a lower standard, in which the quantitative ordering of behaviours in each relevant condition is reproduced in the simulator, but the quantitative values are not necessarily accurate. Relative validity has been established in driving simulation for many behavioural measures, such as effect o f speed-reducing measures (Godley et al., 2004; Lockwood, 1997; Riemersma, van der Horst, Hoekstra, Alink, & Otten, 1990); control of lateral position (Blaauw, 1984; Carsten et ah, 1997; Tomros, 1998); speed choice as a function of road width (Tenkink & van der Horst, 1991); speed when driving through a tunnel (Tomros, 1998); braking to avoid a collision (Hoffman, Brown, Lee, & McGehee, 2002; Kaptein, van der Horst, & Hoekstra, 1996); and the effect o f driving experience on steering control through curves (Jamson, 1999).

Whilst absolute or relative behavioural validity is important in most simulator studies, to test hypotheses H1-H6 it was necessary to be concerned with perceptual validity (Kemeny & Panerai, 2003; Reymond & Kemeny, 2000). This is related to fidelity, but rather than being defined in terms of physical variables (such as image resolution) it refers to the equivalence o f the driver’s perceptual experience in the simulator and the real-world situation to which the experimenter wishes to generalize the findings. It therefore involves not only the properties o f the simulator, but also of the driver’s perceptual systems. Recall the Bayesian formulation o f perception outlined in Chapter Four. The posterior distribution p(5|7), the probability that the state of the environment is S, given that the sensory input is I, is given by:

p ( 5 |/) o c p ( /|5 ) p ( 5 ) ...(5-1)

where the term p(I\S) is the likelihood function for S, the probability that an environment S will give rise to a sensory input I. This term incorporates the fidelity o f the simulator (which is the

degree to which the set of sensory inputs ISim in the simulator corresponds to the set Ireai in the real world, for the same environment S). However to achieve perceptual validity, not just ISjm and Ireai

should correspond, but also and p(Sreai\lrea{). Equation (5-1) shows that this

correspondence depends not just on the fidelity o f the simulator, but also on the prior distribution p(S), the driver’s stored representation of the prior probability o f different environment properties occurring in the external world.

Assuming conditional independence of sensory inputs and prior, Bayesian theory predicts that they will be combined as a weighted sum. Where sensory information is lacking, for instance in the case o f a simulator with a narrow field o f view, or the absence o f motion cueing, or has reduced reliability, such as an image system with low resolution, then the driver’s prior representation will have higher weight. Thus the effect of a fidelity decrement is to increase the weight given to prior representations, relative to sensory inputs, in the driver’s perception.

Hypotheses H1-H8 relate to naturalistic driving. In principle, a field study would offer the highest external validity for research on naturalistic driving; but it would suffer from poor internal validity. It is very difficult to represent naturalistic driving conditions on a test track, so the external validity of this method would be low. Driving simulation, on the other hand, offers the best experimental control, so highest internal validity. Its external validity is also quite high, since naturalistic driving conditions can be simulated (although fidelity issues may challenge external validity somewhat). Overall, driving simulation offers the best option for naturalistic driving studies of perception of vehicle performance. It was therefore my choice for Experiment 2.