Human modelling approaches are typically limited to feedback-only, compensatory tracking tasks. Advances in system identification techniques allow us to consider more realistic tasks that involve feedforward and even precognitive control. In this paper we study the human development of a feedforward control response while learning to accurately follow a ramp-shaped target signal in the presence of a disturbance acting on the controlled element. An experiment was conducted in which two groups of eight subjects each tracked ramps of different steepnesses in a random or ordered fashion. In addition, ordered runs were followed by a 'surprise' run with a random ramp steepness. Results show that operators learn rapidly, continue to learn during the entire experiment, and can adapt very quickly to surprise situations. Experiments involving learning operators are challenging, as it is difficult to balance-out all experimental conditions and control for inevitable differences between (groups of) subjects.

This paper investigates the effects of simulator motion on driver steering performance, and how this depends on the available visual information and external disturbances such as wind gusts. A human-in-the-loop driving experiment was performed in which twelve participants steered a fixedvelocity car to follow a winding road (target tracking, TT) while suppressing side-wind gusts (disturbance-rejection, DR). Driver performance with and without motion feedback is compared in six tasks: “regular” lane-keeping with optic flow, centerline tracking with optic flow, and centerline tracking without optic flow, all with both 5 and 100 m of preview. Performance is calculated in the frequency domain to separate TT and DR contributions. The results show that motion feedback always yields improved DR performance, but the actual improvement depends strongly on the available simulator visuals. TT performance is generally unaffected by motion feedback, except when preview is limited. We conclude that simulator motion is required to evoke realistic driver performance in tasks where substantial external disturbances are present, but not in disturbance-free tasks where a winding road is being followed.

Traditional Frequency Response Function (FRF) estimation techniques used for analysis of Human Controller (HC) dynamics in tracking tasks assume HC dynamics to be linear, but generally do not quantify or compensate for the effects of human nonlinearities. The robust and fast Best Linear Approximation (BLA) techniques for estimating an FRF do provide such quantification of nonlinear distortions caused by Period-In-Same-Period-Out (PISPO) nonlinearities and can reduce the effect of PISPO nonlinear operations on the FRF estimate. This paper investigates the application of these BLA techniques to both measured and simulated HC data. For the simulated data, a linear HC model was deliberately extended with a symmetric PISPO deadzone nonlinear operator and a realistic level of HC “remnant” noise. Overall, both the measured and the simulated data indicate that due to the high levels of remnant noise inherent to HC data, no consistent estimate of PISPO nonlinear contributions could be made. This also means that the improvement of using BLA techniques and averaging over multiple forcing function realizations does not result in a substantial improvement over the current practice of estimating HC FRFs from repeated measurements of a single forcing function.

This paper investigates the importance of yaw and sway motion cues in curve driving simulation. While such motion cues are known to enhance simulation realism, their function in supporting realistic driver behavior in simulators is still largely unknown. A human-in-the-loop curve driving experiment was performed in the SIMONA Research Simulator at TU Delft, in which eight participants were asked to follow a winding road’s center-line, while being subject to wind disturbances. Four motion conditions were tested: 1) no motion, 2) yaw only, 3) sway only, and 4) both yaw and sway; each was tested with 5 m and 100 m road preview for correspondence with earlier work. Results show that visual road preview is essential for adequate road-following. Although the effects of yaw and sway cues are much smaller, sway motion feedback allows for improved disturbance-rejection performance, while yaw motion feedback results in reduced control activity. These distinctly different effects suggest that both motion cues are important for evoking realistic driving behavior in simulators.