Literature points to persistent issues in humanautomation interaction, which are caused either when the human does not understand the automation or when the automation does not understand the human. Design guidelines for human-automation interaction aim to avoid such issues and commonly agree that the human should have continuous interaction and communication with the automation system and its authority level and should retain final authority. This paper argues that haptic shared control is a promising approach to meet the commonly voiced design guidelines for human-automation interaction, especially for automotive applications. The goal of the paper is to provide evidence for this statement, by discussing several realizations of haptic shared control found in literature. We show that literature provides ample experimental evidence that haptic shared control can lead to short-term performance benefits (e.g., faster and more accurate vehicle control; lower levels of control effort; reduced demand for visual attention). We conclude that although the continuous intuitive physical interaction inherent in haptic shared control is expected to reduce long-term issues with humanautomation interaction, little experimental evidence for this is provided. Therefore, future research on haptic shared control should focus more on issues related to long-term use such as trust, overreliance, dependency on the system, and retention of skills.

Shared control is an increasingly popular approach to facilitate control and communication between humans and intelligent machines. However, there is little consensus in guidelines for design and evaluation of shared control, or even in a definition of what constitutes shared control. This lack of consensus complicates cross fertilization of shared control research between different application domains. This paper provides a definition for shared control in context with previous definitions, and a set of general axioms for design and evaluation of shared control solutions. The utility of the definition and axioms are demonstrated by applying them to four application domains: automotive, robot-assisted surgery, brain-machine interfaces, and learning. Literature is discussed for each of these four domains in light of the proposed definition and axioms. Finally, to facilitate design choices for other applications, we propose a hierarchical framework for shared control that links the shared control literature with traded control, cooperative control, and other human-automation interaction methods. Future work should reveal the generalizability and utility of the proposed shared control framework in designing useful, safe, and comfortable interaction between humans and intelligent machines.

A conceptual and computational framework is proposed for modelling of human sensorimotor control and is exemplified for the sensorimotor task of steering a car. The framework emphasises control intermittency and extends on existing models by suggesting that the nervous system implements intermittent control using a combination of (1) motor primitives, (2) prediction of sensory outcomes of motor actions, and (3) evidence accumulation of prediction errors. It is shown that approximate but useful sensory predictions in the intermittent control context can be constructed without detailed forward models, as a superposition of simple prediction primitives, resembling neurobiologically observed corollary discharges. The proposed mathematical framework allows straightforward extension to intermittent behaviour from existing one-dimensional continuous models in the linear control and ecological psychology traditions. Empirical data from a driving simulator are used in model-fitting analyses to test some of the framework’s main theoretical predictions: it is shown that human steering control, in routine lane-keeping and in a demanding near-limit task, is better described as a sequence of discrete stepwise control adjustments, than as continuous control. Results on the possible roles of sensory prediction in control adjustment amplitudes, and of evidence accumulation mechanisms in control onset timing, show trends that match the theoretical predictions; these warrant further investigation. The results for the accumulation-based model align with other recent literature, in a possibly converging case against the type of threshold mechanisms that are often assumed in existing models of intermittent control.

Purpose To evaluate the association between longitudinal changes in quality of life and rates of progressive visual field loss in glaucoma. Design Prospective observational cohort study. Participants 322 eyes of 161 patients with glaucomatous visual field loss recruited from the Diagnostic Innovations Glaucoma Study (DIGS) followed for an average of 3.5 ± 0.7 years. Methods All subjects had NEI VFQ-25 performed annually and standard automated perimetry (SAP) at 6-month intervals. Subjects were included if they had a minimum of 2 NEI VFQ-25 and at least 5 standard automated perimetry (SAP) during follow up. Evaluation of rates of visual field change was performed using the mean sensitivity (MS) of the integrated binocular visual field. Rasch analysis was performed to obtain final scores of disability as measured by the NEI VFQ-25. A joint longitudinal multivariate mixed model was used to investigate the association between change in NEI VFQ-25 Rasch-calibrated scores and change in binocular visual field sensitivity. Potentially confounding socio-economic and clinical variables were also analyzed. Main outcome measures The relationship between change in NEI VFQ-25 Rasch-calibrated scores and change in binocular SAP MS. Results There was a statistically significant correlation between change in the NEI VFQ-25 Rasch scores during follow-up and change in binocular SAP sensitivity. Each 1db change in binocular SAP MS per year was associated with a change of 2.9 units per year in the NEI VFQ-25 Rasch scores during the follow-up period (R2=26%; P<0.001). Eyes with more severe disease at baseline were also more likely to have a decrease in NEI VFQ-25 scores during follow-up (P<0.001). For subjects with the same amount of change in SAP sensitivity, those with shorter follow-up times had larger changes in NEI VFQ-25 scores (P=0.005). A multivariable model containing baseline and rate of change in binocular MS had an adjusted-R2 of 50% in predicting change in NEI VFQ-25 scores. Conclusion Baseline severity, magnitude and rates of change in binocular visual field sensitivity were associated with longitudinal changes in quality of life of glaucoma patients. Assessment of longitudinal visual field changes may help identify patients at higher risk for developing disability from the disease.

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.

Driving is a multitasking activity that requires drivers to manage their attention among various driving- and non-driving-related tasks. When one models drivers as continuous controllers, the discrete nature of drivers’ control actions is lost and with it an important component for characterizing behavioral variability. A proposal is made for the use of cognitive architectures for developing models of driver behavior that integrate cognitive and perceptual-motor processes in a serial model of task and attention management. A cognitive architecture is a computational framework that incorporates built-in, well-tested parameters and constraints on cognitive and perceptual-motor processes. All driver models implemented in a cognitive architecture necessarily inherit these parameters and constraints, resulting in more predictive and psychologically plausible models than those that do not characterize driving as a multitasking activity. These benefits are demonstrated with a driver model developed in the ACT-R cognitive architecture. The model is validated by comparing its behavior to that of human drivers navigating a four-lane highway with traffic in a fixed-based driving simulator. Results show that the model successfully predicts aspects of both lower-level control, such as steering and eye movements during lane changes, and higher-level cognitive tasks, such as task management and decision making. Many of these predictions are not explicitly built into the model but come from the cognitive architecture as a result of the model’s implementation in the ACT-R architecture.

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Experienced drivers performed simple steering maneuvers in the absence of continuous visual input. Experiments conducted in a driving simulator assessed drivers' performance of lane corrections during brief visual occlusion and examined the visual cues that guide steering. The dependence of steering behavior on heading, speed, and lateral position at the start of the maneuver was measured. Drivers adjusted steering amplitude with heading and performed the maneuver more rapidly at higher speeds. These dependencies were unaffected by a 1.5-s visual occlusion at the start of the maneuver. Longer occlusions resulted in severe performance degradation. Two steering control models were developed to account for these findings. In the 1st, steering actions were coupled to perceptual variables such as lateral position and heading. In the 2nd, drivers pursued a virtual target in the scene. Both models yielded behavior that closely matches that of human drivers.