Abstract
For a driving simulator to be a valid tool for research, vehicle development, or driver training, it is crucial that it elicits similar driver behavior as the corresponding real vehicle. To assess such behavioral validity, the use of quantitative driver models has been suggested but not previously reported. Here, a task-general conceptual driver model is proposed, along with a taxonomy defining levels of behavioral validity. Based on these theoretical concepts, it is argued that driver models without explicit representations of sensory or neuromuscular dynamics should be sufficient for a model-based assessment of driving simulators in most contexts. As a task-specific example, two parsimonious driver steering models of this nature are developed and tested on a dataset of real and simulated driving in near-limit, low-friction circumstances, indicating a clear preference of one model over the other. By means of closed-loop simulations, it is demonstrated that the parameters of this preferred model can generally be accurately estimated from unperturbed driver steering data, using a simple, open-loop fitting method, as long as the vehicle positioning data are reliable. Some recurring patterns between the two studied tasks are noted in how the model's parameters, fitted to human steering, are affected by the presence or absence of steering torques and motion cues in the simulator.
Highlights
D RIVING simulators are widely used for various purposes in driver training, traffic research, and automotive development [1]
This conceptual analysis is intended to be general across driving tasks and simulator applications; 2) to explore the feasibility of a model-based simulator assessment by means of an open-loop fitting of steering models to unperturbed human task performance, i.e., without a forcing function approach. This part of the paper is application-specific; we target the assessment of simulators as an industrial tool for the near-limit, lowfriction stability testing of prototype vehicles, a context where a close replication of the human control behavior is relevant but where the necessity of using professional test drivers implies small sample sizes, such that the emphasis here will remain on methodological aspects
Since the Desired Path Yaw Rate Error (DPYRE) model almost always obtained higher R2 than the Modified Gordon & Magnuski (MG&M) model, with equal number of parameters or fewer, the DPYRE model was adopted as the candidate model for measuring behavior validity
Summary
D RIVING simulators are widely used for various purposes in driver training, traffic research, and automotive development [1]. This conceptual analysis is intended to be general across driving tasks and simulator applications; 2) to explore the feasibility of a model-based simulator assessment by means of an open-loop fitting of steering models to unperturbed human task performance, i.e., without a forcing function approach This part of the paper is application-specific; we target the assessment of simulators as an industrial tool for the near-limit, lowfriction stability testing of prototype vehicles, a context where a close replication of the human control behavior is relevant but where the necessity of using professional test drivers implies small sample sizes, such that the emphasis here will remain on methodological aspects. A general discussion and conclusions are provided in Sections VII and VIII, respectively
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