In haptic shared control (HSC), an alternative to full automation, a human operator (HO) performs a steering task while being assisted by force feedback on the control device. Human-haptic conflict, caused by misalignment of intentions or different use of reference information, is a source of annoyance and controllers should be designed to eliminate it as much as possible. One way to influence how a haptic shared controller uses reference information is by adjusting the look-ahead time. In this research, a human-in-the-loop experiment was conducted to investigate the effects of varying the haptic look-ahead time in a shared-control preview tracking task. An HSC was designed according to the Four Design Choices philosophy that uses an internal HO model to provide a human-compatible reference. Results reveal a region where conflict is minimal, and no significant in conflict occurs. Subjective ratings by the participants show a similar yet larger region, for which no significant difference in usefulness and satisfaction is perceived. Meanwhile, despite showing a strong `exchange' of authority through human-haptic forces, the error remains low, indicating how the HO adapts to changes in the haptic controller in order to keep the tracking error satisfactory. The results of this research highlight the importance of properly tuning the haptic look-ahead time, while also revealing a `region of acceptance' for which participants are unlikely to notice any change in the haptic shared controller.

Accurate models of driver steering behavior are essential with ever-growing automation in road vehicles. In this project, the effects of driving speed on steering behavior on winding roads are investigated and modeled. Data were collected in a human-in-the-loop curve driving experiment with fifteen participants, who were asked to drive at five different constant speeds between 30 and 70 km/h and at variable speed, using gas and brake pedals. This experiment was performed in the fixed-base simulator of the Human-Machine Laboratory at the Faculty of Aerospace Engineering. The collected data show that drivers do not use a constant portion in time or distance of the road ahead at different speeds. Instead, they put their visual aim point at an approximately constant time away, while they filter a constant distance after this. Moreover, drivers’ response time delays, lead time-constants, and position-to-heading feedback gains decrease with increasing driving speed. In a next step, a speed-dependent linear-parameter-varying (LPV) model was constructed to describe variable speed steering behavior. For some participants and parts of the trajectory, measured steering wheel deflections are modeled more accurately by this model than linear-time-invariant (LTI) estimates. However, simpler LTI models on average explain the measured variable speed steering better. Overall, this research clearly shows that drivers adapt their steering behavior to driving speed and how key steering behavior parameters are adjusted with speed. Modeling this adaptation for realistic driving, however, is challenging due to the still unknown dynamics between speed and parameter adaptation.

In recent efforts to improve the theory of human control behaviour, different system identification techniques have been developed to estimate human linear time-invariant feedback- and feedforward control behavior. This thesis focuses on a newly developed subsystem identification (SSID) technique, that estimates feedback- and feedforward behaviour from the closed-loop system data. To analyse SSID, the method is first applied to a versatile preview model. Consequently, the performance of SSID is compared to other system identification techniques used in similar applications, differing from SSID in the required number of forcing functions, the cost function data and the optimization algorithm. It is concluded that the SSID technique does not perform optimally, both in terms of estimation accuracy (average parameter estimation error of 30%) and computational time. Best results (average parameter estimation error of 10%) are obtained with a method that uses two forcing functions, the control output signal as cost function data and a Nelder-Mead simplex optimization algorithm.

This research applied a Dual Extended Kalman Filter (DEKF) to time-varying human operator (HO) parameter estimation in preview tracking tasks. The preview time parameter was of particular interest, as the amount of preview can be highly variable in practical situations. The filter was centered around a linear cybernetic HO model with six identifiable parameters for single-integrator and eight for double-integrator control tasks. The DEKF was applied to a range of time-varying simulations, both remnant-free as well as with realistic remnant based on an experimentally identified first-order model. In addition, the tests were validated using existing experimental tracking data. By keeping the HO physical limitation parameters constant (i.e. neuromuscular parameters ω_nms and ζ_nms and time delay τ_v), good estimation results were obtained, particularly for tracking of SI systems. General guidelines for the sensitive tuning process of the filter are proposed. The presence of coloured remnant noise in double-integrator tracking was found to greatly affect the quality of the estimates. Future versions of the filter should explicitly include models of remnant noise, to reduce its sensitivity and ease the tuning process. The obtained algorithm is applicable to single sets of measurement data without a priori assumptions on time-variance or the need for averaging. With some additional development, this makes the DEKF a suitable candidate for practical applications, such as driver monitoring and advanced driver assistance systems in the automotive industry.