Real-life tracking tasks often show preview information to the human controller about the future track to follow. The effect of preview on manual control behavior is still relatively unknown. This paper proposes a generic operator model for preview tracking, empirically derived from experimental measurements. Conditions included pursuit tracking, i.e., without preview information, and tracking with 1 s of preview. Controlled element dynamics varied between gain, single integrator, and double integrator. The model is derived in the frequency domain, after application of a black-box system identification method based on Fourier coefficients. Parameter estimates are obtained to assess the validity of the model in both the time domain and frequency domain. Measured behavior in all evaluated conditions can be captured with the commonly used quasi-linear operator model for compensatory tracking, extended with two viewpoints of the previewed target. The derived model provides new insights into how human operators use preview information in tracking tasks.

Abstract-Manual control cybernetics aims to understand and describe how humans control vehicles and devices using mathematical models of human control dynamics. This 'cybernetic approach' enables objective and quantitative comparisons of human behavior, and allows a systematic optimization of human control interfaces and training associated with manual control. Current cybernetics theory is primarily based on technology and analysis methods formalized in the 1960s and has shown to be limited in its capability to capture the full breadth of human cognition and control. This paper reviews the current state-of-the-art in our knowledge of human manual control, points out the main fundamental limitations in cybernetics, and proposes a possible roadmap to advance the theory and its applications. Central in this roadmap will be a shift from the current linear time-invariant modeling approach that is only truly valid for human behavior under tightly controlled and stationary conditions, to methods that facilitate the analysis of adaptive, and possibly time-varying, human behavior in realistic control tasks. Examples of key current developments in the field of cybernetics -human use of preview, predictable discrete maneuvering, skill acquisition and training, time-varying human modeling, and neuromuscular system modeling -that contribute to this shift are presented in this paper. The new foundations for cybernetics that will emerge from these efforts will impact all domains that involve humans in manual and semi-automatic control.

This paper investigates how humans use a previewed target trajectory for control in tracking tasks with various controlled element dynamics. The human's hypothesized "near" and "far" control mechanisms are first analyzed offline in simulations with a quasi-linear model. Second, human control behavior is quantified by fitting the same model to measurements from a human-in-the-loop experiment, where subjects tracked identical target trajectories with a pursuit and a preview display, each with gain, single-, and double-integrator controlled element dynamics. Results show that target-tracking performance improves with preview, primarily due to the far-viewpoint response, which allows humans to cancel their own and the controlled element's lags, without additional control activity. The near-viewpoint response yields better target tracking at higher frequencies, but requires substantially more control activity. The control-theoretic approach adopted in this paper provides unique quantitative insights into human use of preview, which can help to explain human behavior observed in other preview control tasks, like driving.

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], [S.Barendswaard], [D.M.Pool], [M.Mulder] @tudelft.nl).Abstract: In many practical control tasks, human controllers (HC) can preview the trajectory they must follow in the near future. This paper investigates the effects of the length of previewed target trajectory, or preview time, on HC behavior in rate tracking tasks. To do so, a human-inthe-loop experiment was performed, consisting of a combined target-tracking and disturbancerejection task. Between conditions the preview time was varied between 0, 0.1, 0.25 0.5 0.75 or 1 s, capturing the complete human control-behavioral adaptation from zero-to full-preview tasks, where the performance remains constant. The measurements were analyzed by fitting a HC model for preview tracking tasks to the data. Results show that optimal performance is attained when the displayed preview time is higher than 0.5 s. When the preview time increases, subjects exhibit more phase lead in their target response dynamics. They respond to a single point on the target ahead when the preview time is below 0.5 s and generally to two different points when more preview is displayed. As the model tightly fits to the measurement data, its validity is extended to different preview times.

Drivers rely on a variety of cues from different modalities while steering, but which exact cues are most important and how these different cues are used is still mostly unclear. The goal of our research project is to increase understanding of driver steering behavior; through a measuring and modeling approach we aim to extend the validity of McRuer et al.'s crossover model for compensatory tracking to curve driving tasks. As part of this larger research project, this paper first analyzes the four main differences between compensatory tracking and curve driving: 1) pursuit and preview, 2) viewing perspective, 3) multiple feedback cues, and 4) boundary-avoidance strategies due to available lane width. Second, this paper introduces multiloop system identification as a method for explicitly disentangling the driver's simultaneous responses to various cues, which is subsequently applied to two sets of human-in-the-loop experimental data from a preview tracking and a curve driving experiment. The results suggest that recent human modeling advances for preview tracking can be extended to curve driving, by including the human's adaptation to viewing perspective, multiple feedback cues, and lane width. Such a model's physically interpretable parameters promise to provide unmatched insights into between-driver steering variations, and facilitate the systematic design of novel individualized driver support systems.

Novel driver support systems potentially enhance road safety by cooperating with the human driver. To optimize the design of emerging steering support systems, a profound understanding of driver steering behavior is required. This article proposes a new theory of driver steering, which unifies visual perception and control models. The theory is derived directly from measured steering data, without any a priori assumptions on driver inputs or control dynamics. Results of a human-in-the-loop simulator experiment are presented, in which drivers tracked the centerline of straight and winding roads. Multiloop frequency response function (FRF) estimates reveal how drivers use visual preview, lateral position feedback, and heading feedback for control. Classical control theory is used to model all three FRF estimates. The model has physically interpretable parameters, which indicate that drivers minimize the bearing angle to an "aim point" (located 0.25-0.75 s ahead) through simple compensatory control, both on straight and winding roads. The resulting unifying perception and control theory provides a new tool for rationalizing driver steering behavior, and for optimizing modern steering support systems.