This thesis is on the assessment of handling qualities for a robust controller, previously designed using H∞ techniques for a longitudinal model of the Bo-105. A piloted simulator flight test was conducted in which a set of tasks was flown. These tasks included configuration of stick sensitivity, pitch and altitude tracking tasks, flown at different flight conditions to show robustness, as well as a bob-up and acceleration-deceleration task. The resulting handling quality rating showed that the controller, designed for a theoretical rating of level 1, achieved between level 1 and level 2 handling qualities between the tasks. Non-controller factors, such as stick force, missing cueing and performance limits, were commented on by the pilots to have an effect on the rating. Anti-windup methods, clamping and back-calculation, were used to improve controller response, and reduce oscillations observed during testing.

Target-in-the-loop (TIL) systems are key to engaging aerial threats and enabling laser processing. This work simulates a TIL system and evaluates its performance under varying atmospheric turbulence and target distances. The goal is to assess how these factors affect energy delivery and real-world viability. A computational model was developed using standard wavefront propagation to account for turbulence and target surface roughness. The simulation allows controlled changes in turbulence strength and range. An experimental setup was also created to validate the results. Findings show that compensating for surface roughness is crucial, and system performance improves significantly with a controller—boosting peak intensity by up to 60% and power in the bucket by 10%. A tradeoff exists between distance and turbulence strength, but adaptive optics can help extend operational range.

Introduction
Cancer-related pain is a prevalent and under-assessed issue in oncological care. Despite the availability of effective pain treatments, challenges in pain assessment - such as communication barriers, subjective interpretation, and workflow constraints - continue to hinder accurate pain recognition and management. In recent years, advances in artificial intelligence (AI), sensor technology and mobile health have offered promising new opportunities for more consistent, continuous and context-aware assessment strategies. Facial expressions and vocal cues have been identified as promising behavioural indicators of pain, and their use in automatic pain assessment (APA) is gaining increasing attention.

To explore how such technologies could support cancer pain assessment and management, the SENSAI project was initiated. The project focuses on the development of a human-centred, AI-empowered tool for APA in cancer care, based on facial expressions and vocal cues captured through a mobile application.

This thesis presents the initial steps of the SENSAI project. Its objectives were to examine current pain assessment practices and challenges in oncology, investigate end-user perspectives on AI-supported assessment, translate research insights into a conceptual tool framework, support the design of a user-centred APA application, and initiate the development of a multimodal cancer-related pain database.

Methodology
A multiphase, design-oriented research approach was used, guided by user-centred design, the Double Diamond framework, and a translational AI development model for healthcare. This thesis encompassed the Research and Conceptualisation phases and initiated the Development phase.

In the research phase, background studies and an exploratory interview study with oncologists were conducted to understand current pain assessment practices and challenges. This informed the problem definition and supported the development of an initial concept for the tool In the conceptualisation phase, this concept was refined into was structured framework. The second part of the interview study explored oncologists’ attitudes towards the proposed APA tool, guided by the mobile health Technology Acceptance Model. These findings, along with expert consultations, a brainstorm session and feasibility considerations, shaped the envisioned design and functionalities of the tool. In the development phase, agile application development was initiated in collaboration with software company Innovattic, with the author serving as product owner. In parallel, a clinical study protocol was prepared for the creation of a multimodal cancer-related pain dataset to support future AI model training.

Results
The interview study identified five key challenges in current cancer pain assessment: the complexity and subjectivity of pain, ambiguity in responsibility, communication barriers, balancing all pain information with clinical judgement to come to decision-making, and practical constraints. Oncologists expressed conditional interest in an APA tool, emphasising the importance of clinical validation, interpretability, and seamless integration. The conceptual framework proposed three core functionalities: multimodal data collection via a mobile application, AI-based pain classification, and feedback for both patients and clinicians. The mobile application is currently under development and includes measurement and login/authentication modules. A clinical protocol for database development has been submitted for ethical review.

Conclusion
This thesis establishes a solid interdisciplinary foundation for the development of a human-centred APA tool tailored to cancer care. While the AI model itself lies beyond the scope of this thesis, the design process, stakeholder engagement, and technical groundwork contribute to a clinically grounded and ethically aligned vision for future development. The work advances efforts to improve how cancer-related pain is recognised, communicated, and managed in clinical practice. Building on this foundation, future research should focus on user testing with patients, iterative refinement of the application and measurement protocol, technical development of the AI model, and broader clinical evaluation to assess its impact on care delivery and patient experience.

This research focuses on developing a control system for a compound coaxial helicopter (CCH) similar to the SB-1 Defiant, capable of both hovering and high-speed forward flight. The study introduces a 9 degrees of freedom mathematical model with 3 inflow states for the upper rotor, lower rotor, and pusher propeller, accounting for mutual rotor interference. The model undergoes a trim and stability analysis, revealing instability during transitional flight, necessitating a stability and control augmentation system. This system is implemented using an Explicit Model-Following (EMF) method, stabilized by a PID controller and a Weighted Pseudo-Inverse (WPI) control allocation algorithm. The controller effectively manages various flight controls through transition, as tested in a bob-up/bob-down mission task. While it performs well, there is room for improvement in input coupling and jitter behavior. Finally, an objective handling quality assessment based on ADS-33E metrics shows that the control system meets Level 1 handling quality criteria in the transition region for attitude and bandwidth, though some inefficiencies in control allocation during large attitude maneuvers were identified.

This thesis presents the development process of an aircraft control law. The control law is
designed using a two-degree-of-freedom (2DoF) structured H∞ loop-shaping approach. This
method allows the reuse of controller structures required by certification procedures while
directly including handling qualities and robust stability requirements in the optimization
process. This strategy is employed to develop a Rate Command and Attitude Hold (RCAH)
demand system aimed at satisfying longitudinal handling qualities. First, the stability of the
open-loop model and its compliance with the handling qualities guidelines are evaluated. Then, the control law is designed. In this step, a detailed description of the design specifications and how to specify them in the context of H∞ control is given. Subsequently, the controller parameters are optimized to satisfy the design specifications and a closed-loop analysis is performed. Finally, a simulator flight testing campaign is conducted to experimentally validate the designed control law. It is shown that the aircraft equipped with the RCAH system achieves better handling quality ratings (HQRs) and more favorable pilot feedback, providing a substantial improvement over the bare airframe.

Enroute Air Traffic Controllers (ATCOs) determine the appropriate course of action by scanning radar displays to identify and select a flight that requires clearances to ensure safe and efficient operations within their airspace sector. After selecting a flight of interest, ATCOs visually compare the flight parameter displayed in the flight labels to assess the impact of potential flight control actions on the sector safety. Expected surge in global air traffic will make it difficult for ATCOs to compare flight parameters, leading to delayed responses and increased workload. This research proposes a flight filtering mechanism based on interacting flight trajectories with spatio-temporal proximities to guide ATCOs’ attention toward potential interaction flights after selecting a flight of interest. ATCOs will find potential interaction flights more saliently as non-interacting flights fade once the flight of interest is selected. This approach targets ATCO’s pre-attentive phase of visual processing, alleviating additional perceptual and cognitive efforts in finding and processing information during conflict detection and previewing clearances. Experiment results with eight subject matter experts as participants showed no significant differences in the safety, operator performance, and perceived workload while controlling traffic due to demanding task requirements and strategy variation among participants. Nonetheless, participants expressed positive feedback regarding the assistance offered by filtering in fading non-interacting flights. No significant differences in objective measures coupled with filtering’s positive perceptions indicate filtering did not negatively affect the performance, suggesting further research with optimal balance between task requirements and participant expertise to evaluate the flight filtering concept.

Given the expected increase of automation in the vehicles of the future, touchscreens are expected to be used in a wide variety of scenarios, including the ones that can become safety-critical.
One disadvantage of the direct interaction approach that characterizes touchscreen operation is the feedthrough of accelerations through the human body that can induce unwanted activations, leading to unsafe situations. The current paper investigates the feasibility of applying a method that can track the biodynamic responses of the elbow, wrist and index finger using cameras in a stereo configuration and an open-source pose estimator (OpenPose). An experiment with six participants was performed to understand whether different neuromuscular settings, task instructions or degrees of arm extension induce an adaptive behavior of the biodynamic feedthrough to the recorded joints. The outcomes of the experiments showed that the finger is the body part that exhibits the most adaptive behavior in terms of feedthrough, being dependent on whether it is interacting with the screen with no velocity, moving on the screen or not touching the screen. Moreover, the difference between keeping the upper limb relaxed or stiff would need to be taken into account for the specified body part when the finger is not touching the screen, showing a decrease of 29% in the mean RMS of the vertical feedthrough component when the stiff condition is exhibited. The adaptive nature of the biodynamic feedthrough was also demonstrated concerning two different cases of arm extension (close and far from the body). This study proved the feasibility of using a linear mass-spring-damper model to the data gathered with the camera-based system to describe the feedthrough effects for all the body parts, neuromuscular settings and tasks, being able to explain on average at least 75% of the variance of the raw signals. The approach presented in this study can be further refined toward reaching real-time capabilities, which is a key step in accurately identifying the highly adaptive and subjective nature of the feedthrough of the accelerations.

The rise in high-speed quadrotor applications demands aerodynamic models that explain the flight characteristics at these broader flight regimes. For developing these models using system identification methods, having information-rich data at these flight conditions is crucial. While there have been sufficient works in the input design for identification procedures in quadrotors, there are limited guidelines for high-speed flights. This work focuses on two key contributions to aid the high-speed quadrotor model identification process. First, an automated flight testing pipeline using various software tools has been developed to support the iterative input design process. Second, various inputs such as position tracking, trajectory tracking, and attitude injection while trajectory tracking have been designed. The goal of these input designs was to push the quadrotor velocities while trying to maximize the excitation in forces and moments. The HITL simulation tool, being part of the testing pipeline, was used to iteratively design and verify inputs before actual flight tests. Finally, automated high-speed flight tests of the designed inputs were conducted at the Cyberzoo indoor facility, reaching maximum velocities of up to 7 m/s. The results show that attitude injection while trajectory tracking provides a method to excite forces and moments during high-speed flights. This work is an initial attempt at understanding the effects of input design at high-speed flights with the goal of obtaining data for quadrotor model identification. The methodology and pipeline developed here serve as a platform to build and explore various other identification inputs with the scope to push the quadrotor flight speeds even further.

Touchscreens are increasingly being integrated into modern aircraft cockpits due to their space-efficiency and adaptability. However, operating touchscreens in turbulent conditions reduces input accuracy and alters the applied force. These issues are caused by involuntary limb movements induced by external accelerations such as turbulence, called biodynamic feedthrough. Understanding and mitigating this phenomenon is crucial to be able to provide consistent haptic feedback on touchscreens. This study investigates the dynamics of biodynamic feedthrough into the applied normal force on the screen, by conducting a human-in-the-loop experiment with fifteen participants. In the experiment, participants performed both stationary and dynamic position tracking tasks on a touchscreen, while being exposed to turbulence in all three linear motion directions. It was observed that on average 63% higher normal forces were applied during stationary tasks than during dynamic tasks, likely resulting in increased arm co-contraction. Additionally, a second-order mass-spring-damper model was fitted to the obtained force data. It was found that biodynamic feedthrough into the applied normal force has the highest feedthrough levels when the direction of turbulence aligns with the direction of the applied force, and that up to 90% of the disturbance-induced normal force can be described by the proposed individualized models. The results also show the variability of dynamics between participants and therefore the importance of individualized models. These findings provide insights for future developments in adaptive haptic feedback for touchscreens, aiming to enhance the reliability and safety of touchscreen interactions in turbulent environments.

This research investigates the impact of modeling uncertainties on helicopter handling qualities and simulator tolerances as prescribed by ADS-33 and JAR-STD. Using a six-degree-of freedom linearized model of the Bo105 helicopter, the analysis focuses on the propagation of stability derivative uncertainty on four Quantities of Interest (QoIs): Pitch Quickness, Roll Quickness, Pitch-Due-To-Roll Cross-Coupling, and Roll-Due-To-Pitch Cross-Coupling. These QoIs are evaluated across the flight envelope, from hover to maximum velocity (145 knots), capturing sensitivity variations with flight con ditions and advance ratios. To propagate uncertainty, two methods are employed: Monte Carlo Simulation (MCS) and Polynomial Chaos Expansion (PCE), with PCE reducing computation time by 98% while maintaining accuracy. Each QoI shows greater sensitivity to uncertainty at the extremes of the flight envelope. Pitch Quickness is most sensitive at high speed, whereas the other QoIs are most sensitive at hover. Cases
with both JAR-STD tolerance violations and ADS-33 HQR level changes show JAR-STD tolerances are generally more stringent, except for the Cross-Coupling QoIs, where ADS-33 level changes are more frequent.

An aging population puts a pressure on health-care workers working with dementia patients globally. A potential solution is to provide care with Socially Assistive Robots (SARs), i.e. robots who help people through social interaction. However, for effective care these SARs must be able to personalize their behavior to individual patients and adapt this behavior to changes in this patient’s references. This paper presents the decision making process of a SAR that enables the SAR to personalize its behavior to the personality of the patients and adapt this behavior to their current state-of-mind. The system consists of a Fuzzy Logic Control personalization module, which personalizes the SAR’s behavior and a Reinforcement Learning based decision making module together with a Fuzzy Logic Control reward module for the adaption of the personalized behavior. The personalization of the SAR’s behavior is assessed by comparing the output of the system with answers from a survey. The average scatter index over all different behavioral parameters of the SAR is 20.4% The adaptation of the behavior is assessed with computer-based simulations, where an overall accuracy of 81.8% is achieved. A third experiment is carried out to assess the effect of adding the Fuzzy Logic Control personalization module to the system. This experiment shows that adding the personalization module to the decision making system of the SAR decreases the time for the learning process to converge with 13.3%. Although the first assessments of the system look promising, more extensive experiments should be held in later stages of the research. A crucial experiment that must be held in future research is performing real-life interactions between dementia patients and the SAR, in such an experiment the functionality of the system really can be assessed.

The identification of time-varying, adaptive behaviour of a human operator in basic manual control tasks is undoubtedly under development since most methodologies only account for time-invariant systems. Previous authors have proved that estimation techniques based on ARX structures can generally identify the HO model parameters. Nonetheless, ARX methods present several problems, such as the persistent bias in estimates that may increase due to coupled noise and system models. Therefore, a novel identification technique based on Box Jenkins models is proposed to achieve a more adequate match between the estimator structure and the HO model. The identification process can be conducted offline by the Ordinary Least Squares and Prediction Error Method, or online, when Recursive Least Squares and Recursive PEM are employed, respectively, in ARX and BJ models. The BJ estimator has excellent potential as an identification tool due to its bias reduction capabilities, as clearly shown in batch-fitting, although non-linear optimization processes decrease its convergence speed by 500%. An RPEM algorithm with forgetting factor 𝝀 = 0.99609 and first-order remnant BJ structure is implemented and tested under Monte Carlo simulation and experimental data. Recursive BJ algorithms could help to achieve the ideal identification method by diminishing the Neuro-Muscular parameter bias in ARX.

When placing a Head-Mounted Display (HMD) of a Virtual Reality (VR) or Augmented Reality (AR) system in the cabin of a moving base simulator to provide a larger field-of-regard, the HMD's inertial sensors will interpret the motion of the moving base simulator as if provided by the user, thus distorting the HMD's cabin-fixed pose estimation.
In order to estimate a proper cabin-fixed HMD pose, this thesis proposes an Unscented Kalman Filter (UKF) that fuses the information from the simulator sensors, as well as the sensors of the VR/AR system, including its visual position sensor and IMU. For this, three sensor configurations are proposed and tested offline deterministically based on real data. Although an unexpected latency in the HMD's visual pose sensor prevented a full validation of the algorithms, both the configuration with an IMU attached to the motion base, and the configuration without an IMU but with second-order additive noise variables added to the UKF, resulted in a proper, tight innovation sequence, indicating accurate system state estimation. The third configuration, one using the novel approach of using setpoints, provided a tight, but out-of-bounds, innovation sequence indicative of high accuracy, but also diminished tuning efficacy. It is also sensitive to stability issues when estimating the motion system's dynamics when no movement is present on the moving base. The unaltered setpoint method is not recommended as a solution. However, a hybrid solution of the setpoint and second-order additive noise variables methods should be investigated in a follow-up study to combine stability with high accuracy. Another recommendation is to test all configurations interactively, i.e. online.

Aircraft design systems are frequently used to synthesize aircraft designs. However, it is inherently difficult to reconfigure these software systems to facilitate a broader range of design studies (e.g. optimization or sensitivity studies) and to address follow-up questions about the synthesized aircraft designs. This report presents an investigation into the feasibility of developing a reconfigurable aircraft design system.

Firstly, the issues preventing current aircraft design systems from being used in a reconfigurable way are identified. These issues are closely tied to the intricate and tightly integrated nature of the source code that underpins these systems. This is unsurprising, given that these systems have typically been devised by experts in aircraft design (with varying expertise in software design) who are primarily interested in solving concrete design problems rather than creating sophisticated source code. In particular, the extensive design logic, characterized by high cyclomatic complexity, combined with cluttered and ambiguous design data structures, makes it challenging to comprehend and adapt the functioning of these systems.

An iterative development methodology is employed to come up with a software architecture aimed at mitigating the identified issues. A number of distinct architectural elements are devised that leverage a centralized semantic data management approach and a standardized interface for the formulation of modular analysis & sizing methods. Furthermore, a prototype aircraft design system that serves as a reference implementation for this architecture is developed. The prototype incorporates a graph database, a self-explanatory ontology (defining the semantics of the data stored in the database), and several abstract base classes for encapsulating the computation logic contained within typical analysis & sizing methods used during aircraft design studies. Concrete instances of these base classes are supposed to interact with the database through a well-defined endpoint interface, which includes extensive logging capabilities.

The prototype aircraft design system exhibits promising characteristics: The semantic data management approach facilitates the creation of a genuinely unambiguous and flexible data model. Simultaneously, it helps uncover inconsistencies and limitations in the employed analysis & sizing methods. Treating analysis & sizing methods as modular and nested instances of standardized classes appears to be the key to achieving reconfigurability. In addition to the unit-testability of these classes, the self-visualization features incorporated within can significantly enhance the comprehensibility and transparency of the analysis & sizing methods.

The architecture development, repository configuration, ontology formulation, and interface generation took a significant amount of time. Furthermore, some critical challenges surfaced, necessitating further investigation. Specifically, some of the employed analysis & sizing methods feature limitations and implicit assumptions that are required for a synthesis system but may need to be revised before being used in a reconfigurable design system based on the proposed architecture. In the end, there was insufficient time left for implementing a comprehensive set of analysis & sizing methods essential for materializing a thorough aircraft design loop. Therefore, achieving a fully functional aircraft design system prototype proved unattainable. Consequently, it was not possible to demonstrate that adopting the proposed architecture yields an aircraft design system that can be used in a reconfigurable way, despite indications that this could very well be the case...

Increasing levels of automation in the ATM system are required to allow for higher traffic densities without exceeding the ATCo’s mental capacity. Maastricht Upper Area Control Centre envisions to first automate the control of, so called, ``basic’’ traffic. For this strategy to work, an allocation model is required to determine the complexity of each individual flight entering the airspace, based on objective and predictable flight characteristics, which can allocate a flight to human or automation control. Five metrics are proposed related for the individual flight complexity, and their predictive ability is tested with a human-in-the-loop experiment, where air traffic controllers were tasked with assessing the complexity of an individual flight in a series of static scenarios and indicate which other flights in the scenario affected the complexity score.
Results show that the complexity is depends mostly on flights that near each other within 10 NM in the horizontal plane and have overlapping flight levels. Furthermore, flights requiring a flight level change are considered more complex as the number of possible interactions increases due to the vertical change and the additional dimension increases the uncertainty of the trajectory. The performance of the metrics indicated a dependency on different traffic patterns, leading to the conclusion that the use of only one metric is too limited to describe the complexity, and future research is needed to better understand the interdependence between the metrics.

Future human-machine control tasks with preview (e.g., car driving) are expected to include automation for safety, but keep operators in charge for liability. Such shared control applications require time-varying human identification because the control feedback should be compatible with the operator's variable behavior. A promising time-domain identification algorithm is the Dual Extended Kalman Filter (DEKF), estimating human operator parameters from Van der El's preview model. In this article, the DEKF's time-varying identification performance is studied with realistic simulations, followed by human operator experiments in a fixed-base simulator. The investigation focuses on look-ahead time, indicating how much future information the operator uses for control. Compared to other parameters, look-ahead time is adapted most considerably with preview. The results suggest that this parameter should be initialized in a 0.25 s proximity of its actual value to make the DEKF converge within 30 s. Although only estimating look-ahead time while fixing the other parameters, the DEKF is capable of identifying time variations in preview. Based on the sigmoid results, the estimation bias increases linearly to 0.35 s at the largest 0.75 s steps in look-ahead time. For sine variations, the DEKF estimations are in phase with the look-ahead time until 0.03 rad/s. Between 0.03 rad/s and 0.4 rad/s the DEKF behaves as a lag function, and for higher frequencies the estimation response is decayed. For the first time, it is quantified how well the DEKF can identify variations in look-ahead time during preview tracking tasks. With further research, the DEKF might become capable of real-time identification, bringing the cybernetics community one step closer to intuitive shared control applications.

Haptic Shared Control (HSC) systems offer a means to naturally support human drivers in the transition to automated driving. Tuning of HSC requires knowledge of the neuromuscular system (NMS) of drivers. This MSc thesis project aimed to experimentally validate a linear parameter varying (LPV) technique for modeling driver neuromuscular admittance in time-varying conditions, scheduled with grip force. LPV estimations were compared with estimates made with a Recursive Least Squares (RLS) algorithm and, where possible, a linear time-invariant (LTI) estimation technique. A human-in-the loop experiment was performed in the HMILab fixed-base driving simulator at the AE faculty. To force drivers to change their neuromuscular admittance, a steering wheel manipulation task was designed, where drivers had to keep the steering wheel angle in between limits that were visually indicated on the dashboard, while a perturbation torque was applied. Changing the limits (narrow/wide) forced drivers to adjust their admittance. The results show that the relation between admittance and grip force is not only different between subjects, but also for the same drivers in time-invariant and time-varying conditions. This leads to fundamental scheduling issues for the LPV modelling approach. The variance accounted for (VAF) for the estimated steering wheel angle varied between 0% and 90%. To overcome these scheduling problems, individual LPV models can be constructed on time-varying experiment data. RLS estimates reproduced the system output data accurately with average VAF between 80 and 90% for all experiments. RLS low-frequency (< 2.5Hz) FRF estimates closely match the LTI FRF estimates. The largest variations in driver admittance occur in this lower frequency domain, making the RLS algorithm an effective tool for identifying changes in driver neuromuscular admittance, which can be applied online.

A haptic shared control implementation for car steering is developed using the four design choices architecture guidelines. Nowadays, haptic shared control systems are often tuned heuristically and there is little knowledge on how to set the design parameters. In this research a novel driver model was used to generate human-compatible references for the haptic controller with a moderate amount of curve-cutting. In a driving simulator study with different levels of haptic support (LoHS) it is found that driver acceptance increases with higher LoHS values up to 1. In this implementation a higher LoHS equates to stronger steering angle feedforward. Objective driver acceptance is measured by analyzing the torque conflict data and the total conflict is found to decrease by 70% for the highest LoHS condition compared to the lowest setting. This result is verified with higher subjective ratings of the haptic support as the LoHS is increased. The required driver steering effort decreases significantly for higher LoHS values with a reduction of 81% for the highest LoHS condition compared to manual driving. Likewise a small decrease in the steering wheel reversal rate is found as the haptic support is increased. Under high LoHS values the remaining torque conflict is found in the curve entry and exit phases. The addition of individualised prepositioning into the reference has the potential to reduce the remaining conflict. The findings suggest that choosing the right LoHS is of great importance in achieving high acceptance and should thus be the first parameter to be tuned for a haptic controller of the four design choices architecture type.

Fault-tolerant flight control faces challenges as developing a model-based controller for each unexpected failure is unrealistic, and online learning methods can handle limited system complexity due to their low sample efficiency. In this research, a model-free coupled-dynamics flight controller for a jet aircraft able to withstand multiple failure types is proposed. An offline-trained cascaded Soft Actor-Critic Deep Reinforcement Learning controller is successful on highly coupled maneuvers, including high-bank coordinated climbing turns. The controller is robust to six unforeseen failure cases, including the rudder jammed at -15°, the aileron effectiveness reduced by 70%, a structural failure, icing and a backward c.g. shift as the response is stable and the climbing turn is completed successfully. Robustness to biased sensor noise, atmospheric disturbances, and to varying initial flight conditions and reference signal shapes is also demonstrated.

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.