Despite the rapid growth of research on socially aware robot navigation, mobile robots still struggle to navigate smoothly through crowded environments, often disrupting both human and robot motion. One major gap to their successful integration into real-world shared environments is our limited understanding of how humans perceive and predict mobile robot intent. Prior work demonstrated that intent-expressive trajectory modulation improves legibility for point robots and manipulator arms, but this has not been validated for mobile robots using Dragan and Srinivasa’s defining metrics: correctness and speed of human goal inference. This study provides the first empirical test of whether legibility-optimizing trajectory modulation enhances mobile robot legibility, as measured by these metrics. In a within-subjects laboratory experiment (N = 40), participants observed a physical mobile robot navigating toward one of two goals. We varied a parameter, λ, that controls the trajectory’s lateral deviation from the efficient path. Increasing λ improved prediction accuracy (from 77.1% at λ = 0 to 99.2%at λ = 4; p < .001) and reduced reaction time to correct inference (from 2.08 s to 0.97 s; p < .001). These effects persisted after controlling for MPC trajectory tracking deviation, with effect sizes increasing when this noise source was accounted for. These results provide empirical evidence that legibility-optimizing trajectory modulation enhances mobile robot legibility, extending prior video-based findings to a physical, non-holonomic platform. Future work should address the real-time implementation of such trajectory modulations in motion planners and their evaluation in dynamic multi-agent environments. ... Read more

Healthcare systems increasingly rely on robotic technologies to address workforce shortages, yet asignificant disconnect remains between the technical logic of efficiency and the relational requirementsof care. While standard Value Sensitive Design (VSD) provides a method for value integration, it lacksspecific normative content. Care Centered VSD (CCVSD) resolves this by anchoring design in careethics, yet it remains a theoretical model that lacks the practical tools required for engineering implementation.This thesis posits that this failure stems not from a deficit of moral will (practitioner apathy),but from a deficit of moral skill: the lack of intermediate-level knowledge (operational artifacts thatbridge the divide between high-level theory and concrete implementation) to translate abstract valuesinto concrete technical implementations.Adopting a Research through Design (RtD) approach, this study operationalized the CCVSD frameworkthrough a case study within a commercial robotics company, focusing on the development of a socialnavigation module (interaction-aware motion planning that respects human proximity and social cues)for the PAL Tiago robot. The investigation revealed three structural barriers. First, semi-structured interviewswith 19 stakeholders (comprising robotics engineers and caregivers) identified a semantic gapwhere groups held divergent interpretations of care values; this was bridged by the Prospective ValueHierarchy, which translated care values into technical constraints. Second, the technical implementationof the navigation algorithm revealed a normative void, caused by the original framework structurallyskipping the technical development process. This phase was made explicit and operationalizedby integrating standard engineering workflows, providing the necessary structure to document technicaltrade-offs as ethical decisions rather than implicit defaults. Third, the integration phase of theframework, executed through comparative evaluations with 20 stakeholders (spanning care receivers,caregivers, and management) uncovered contextual instability, demonstrating that identical robotic behaviorselicited divergent value judgments across different care settings.The findings from this case study suggest that the operationalized framework offers practical utility forengineering teams. Feedback from the participating engineers indicated that the translational artifactshelped structure their tacit knowledge into a formalized process. These results indicate that providingengineers with tools to convert ethical mandates into actionable constraints can support a shift frompost-hoc ethical critique to continuous design input within such collaborative environments. ... Read more

Musculoskeletal injuries are among the leading causes of pain, disability, and loss of independence worldwide. They affect millions of people, with prevalence rising steeply with age. One of the most common musculoskeletal injuries is tears to the shoulder rotator cuff. As these muscle-tendon tissues are anatomically constricted in a very narrow space between the shoulder bones, they are frequently subject to trauma or wear. Treatment of these injuries is both medically and socially pressing: they impair daily activities, limit the ability to work and engage in sports, and generate high personal and healthcare costs. Rehabilitation is essential to recovery, but it is often lengthy and labor-intensive for both physiotherapists (PTs) and patients. Moreover, it is prone to setbacks such as re-injury, since PTs lack quantitative tools to monitor the evolution of complex musculoskeletal structures during therapy.
In this context, the adoption of robotic devices offers opportunities to support manual manipulation of patients and provide sophisticated sensors to monitor them. Yet, despite advances in robot design and control, current systems remain unaware of the patient’s underlying biomechanics, and therefore cannot monitor or prevent harmful loading of healing tissues.
This thesis addresses such critical lack of knowledge by embedding state-of-the-art musculoskeletal models into the control of rehabilitation robots. Through the development of novel algorithms, it enables real-time estimation of deep muscle activity and tendon strain in the shoulder during physical human-robot interaction. By spanning from improved biomechanical simulations to their integration in robotic therapy execution, this work significantly advances the current state of the art to form a cohesive framework for biomechanics-aware robotic physiotherapy. ... Read more

This project investigates how Mixed Reality (MR) can enhance the experience of Human-Robot Interaction (HRI) for crafting tasks, aiming to preserve the positive aspects of user experience and feeling of craftsmanship.
Initiated with an interest in the growth of MR and robotic automation, the research examines how MR might bridge the gap between user satisfaction and the increasing robot implementation in automating tasks execution. While automation streamlines repetitive or labour-intensive tasks, it often removes aspects that contribute to user engagement and job satisfaction. MR, as an immersive and interactive tool, offers a promising solution to enabling users to control and collaborate with robots in a more intuitive and meaningful way.
The project is built around the research question: “How can a unilateral vision-based control system, implemented through a Mixed Reality headset, enable telemanipulation of a robotic arm for crafting purposes, without disrupting the user’s experience and performance?”
The study defines MR’s unique potential for HRI by leveraging vision-based hand-tracking to control a robotic arm, specifically in a crafting task. A prototype was developed to explore the interaction dynamics, using, due to technical constraints, virtual reality to simulate the MR interaction and the robot The primary goal was to design an interaction system that offers full control of spatial navigation and force application, allowing the direct and real-time adjustments essential to a crafting task.
The iterative design process involved testing multiple prototypes with users, identifying challenges and the experience in relation to craftsmanship. Key issues identified included difficulty in manipulating the robot precisely, due to the absence of haptic feedback, limited visual depth awareness, and a reliance on visual-only feedback, which, while informative, have a low robustness and clutters the user interface. These findings lead to the design of three revised prototypes with various constraint levels, one with full user autonomy, one with low constraints and a one highly constrained, to test the impact on user experience and task effectiveness and find the right balance between user control and ease of manipulation.
The results indicate that the low-constraint system provided the most positive user experience, finding the right balance between user control and guidance while maintaining the essential qualities of craftsmanship. Although MR offers a viable framework for enhancing user experience in HRI, the research highlights limitations in replicating the experience of real-world crafting. The study concludes by recommending that future MR applications should leverage MR’s unique qualities, rather than simply mimicking traditional crafting qualities, to foster novel, user-centred interactions.
... Read more

Touch is fundamental to our perception of the world and to interaction with our physical surroundings. With touch we can intuitively and effortlessly move and shape objects and control complex machines. In most modern machines, the part that interfaces with user often integrate touchscreens or touchpads, owing to their ease of use. However, these interfaces often deliver very poor tactile feedback, which limits the usability in contexts such as driving, low-light environments, and for users with visual impairments. Existing solutions like vibrotactile feedback, are poor substitute to the richness of natural touch and offer only transient sensations. Surface haptic devices offer more complexity, but since they rely on friction modulation, they require continuous movement and cannot nudge the user to arbitrary directions. These limitations highlight the need for active lateral force devices that can guide users in an arbitrary direction. This thesis introduces the Ultra loop, an active surface haptic device that generates net lateral forces using resonant traveling waves. Built around an oblong ring-shaped structure, the Ultra loop provides a large and flat interaction area with uniform force generation. Unlike existing active haptic devices, it operates at resonance, achieving a high vibration amplitude-to-input ratio, resulting in a more salient force feedback. Additionally, this thesis introduces a planar adaptation of the Ultra loop, the flat Loop, which is more compact with a height of just 5 mm, facilitating integration into consumer electronics. These devices can guide users via their sense of touch and render complex forces fields by modulating the wave amplitude and phase control as a function of the position and velocity of the user. To evaluate their effectiveness, this thesis investigates two types of rendered haptic environments: position-based elastic potential fields and velocity based viscous damping. Experimental user studies show that participants could perceive virtual 3D shapes (e.g., bumps and holes) and stepwise force fields that enhance their target-search performance. Moreover, directional cues provided by the force feedback enabled users to navigate toward a target without visual feedback, while viscous damping environments, where lateral force is a function of finger speed, reduced oscillations during selection, and improved overall targeting performance. This doctoral work systematically explores the benefits of active force feedback in touch interactions by introducing resonant traveling wave-based haptic displays and performing user studies. By advancing surface-haptic technology, this research paves the way for next-generation touch interfaces that support eye-free interaction and effortless control of complex machines. ... Read more

Intelligent machines (in the form of physically embodied robots or virtual agents) are increasingly able to perform tasks in collaboration with humans. However, learning to become a good team takes time, especially when dynamic tasks require the team to constantly adapt to new situations. Over time, both human and machine need to not only learn how to execute the task, but also how their team partner behaves in the task, as well as how to improve their collaboration over time by attuning their behavior to each other. Existing research on human-machine collaboration often does not sufficiently address adaptation and learning. Work that does study adaptation and learning tends to focus on either machine learning and adaptation or human learning and adaptation, thereby not addressing the interaction of these learning processes that would be present in a co-learning situation... ... Read more

Automated driving technologies offer significant societal benefits but face challenges, particularly in interactions between automated and human-driven vehicles during lane changes and merging on highways. This thesis addresses this issue by focusing on joint driver efforts and proposes a new Communication-Enabled Interaction (CEI) model framework.

Human drivers communicate intent through vehicle kinematics during interactions, making joint decisions and exhibiting joint behaviors. However, current autonomous vehicle (AV) models often lack generalization to real-world behaviors and fail to capture dynamic interactions. AVs typically use models assuming human drivers only respond to AV behavior, leading to over-conservative and sometimes awkward interactions.

To enhance AV-human interactions, the thesis proposes a joint driver model that considers multi-level contributions of drivers. It critiques existing models, highlighting their limitations in capturing dynamic interactions. For instance, many models only consider single drivers and fail to address communication and continuous behavioral adaptation.

The CEI model framework explicitly accounts for driver communication and integrates deterministic future plans with probabilistic beliefs. This framework acknowledges that humans do not continuously optimize behavior but seek satisfactory solutions. The thesis presents a case study where the CEI model accurately describes merging scenarios, generating human-like gap-keeping behavior.

Further, the thesis explores naturalistic driving behaviors using the HighD dataset and develops visualization tools to validate driver models. It extracts and analyzes similar driving scenarios to understand variability in human responses, both operationally and tactically. Controlled experiments in simulators examine driver behaviors during merging conflicts, revealing insights into acceleration control and conflict resolution.

The empirical findings inspire improvements to the CEI model, incorporating intermittent piecewise-constant control observed in human drivers. This updated model accurately reproduces joint driver behaviors and outcomes from experimental scenarios, emphasizing the importance of individual contributions to joint safety margins.

In conclusion, the thesis contributes valuable insights into human lane-changing and merging interactions, proposing a robust model framework for AVs to understand and emulate human driver behaviors. While the study focuses on simplified scenarios, it lays the groundwork for extending the model to more complex real-world situations. The work represents a significant step toward enhancing autonomous vehicles' ability to interact safely and effectively with human drivers on the road. ... Read more

This paper addresses the research question: “How can a human-robot team achieve co-learning, and interdependence in physically embodied tasks?”
A method has been developed that enables a human-robot team to co-learn the handover of an object from the robot to the human. Five design requirements were composed to address the challenges of human-robot co-learning in physically embodied environments. The method is based on a Q-learning algorithm that was adapted and extended to meet these requirements. An experiment was conducted with six participants. For every human-robot team, each design requirement was qualitatively evaluated. Interdependent co-learning was identified in three of the six teams. The limitation of the design, and how this method can be improved further, was discussed. The method, presented in this paper, demonstrates how human-robot co-learning and interdependence can be enabled in physically embodied tasks. ... Read more

The increase in online retail demand has stimulated automation in order picking systems, leading to new challenges and opportunities in task assignment and scheduling. In partially automated order picking systems, such challenges and opportunities exist regarding human factors implementation in the job-shop scheduling problem, an optimisation problem essential in operations. Workplace fatigue is a human factor often overlooked in scheduling research and application, despite hurting employees’ well-being and costing U.S. employers up to €127 billion annually. With the opportunities that automation offers, cobotic order picking systems could actively consider human fatigue development, mitigating its negative effects in operation.
This thesis investigates the possibility and potential benefits of fatigue consideration in the job-shop scheduling problem for a partially automated order picking system. We present a new bi-objective mixed integer nonlinear programming problem formulation to represent system constraints and a predictive fatigue model while considering worker fatigue and productivity during schedule optimisation. To put the results of simulated optimisation in perspective, we experimentally validate the fatigue model predictions and fatigue mitigation capabilities of the scheduling approach using heart rate measurements and qualitative fatigue ratings. These experiments occur with employees in a real-life partially automated order picking system.
Our mathematical model can find solutions that the conventional single-objective optimisation approach cannot, allowing fractional energy expenditure distribution improvements more than 4x larger than the decrease in productivity they require in 53% of the considered virtual cases. This is a promising result for fatigue mitigation in operations only by altering operational decision-making. However, the validation experiments show that our predictive fatigue model has an average RMSE of 2.20 kcal/min in estimating energy expenditure rates compared to heart rate measurements while also showing a low correlation. When assessing 10 minute intervals, a time span that fits a scheduling scope, the estimations improve slightly (avg. deviation of -1.85 kcal/min, avg. correlation of 0.17) but still underestimate the measured values. The experiments also show no significant differences in experienced fatigue between existing schedules and those with fatigue mitigation measures applied.
We conclude that the current scheduling formulation is not yet fit for application with a predictive fatigue model. However, real-life operations can benefit from energy expenditure estimation via heart rate measurements and a different approach for implementation is proposed. Research opportunities lie in further fatigue model development and validation, extension to indirect fatigue effects and other human factors, and further development of the mathematical formulation. ... Read more

Shoulder injuries, prevalent worldwide, often occur from ageing and accidents. In Western countries, these injuries primarily afflict the elderly population, while in rural regions of Bangladesh, Iran, India, and Pakistan, they affect younger individuals who are often the family's primary earners. Due to that, preventing and aiding the recovery of shoulder injuries is crucial. To address this, strain maps with vibrotactile feedback, emerge as a promising solution. However, the feedback system must be affordable, compact, comfortable, user-friendly, easily understood, and portable to suit the local environment. Vibrotactile feedback appears promising but can distract the user from work. Hence, this study seeks to investigate if vibrotactile feedback can be paired with strain maps to guide users in maintaining healthy postures and reducing the risk of shoulder injuries in rural areas, where visual feedback is used as a benchmark. To provide feedback using strain maps, shoulder angles are determined using Python's OpenCV and MediaPipe libraries. PyGame is utilized to display the strain maps, and OpenCV helps delineate boundaries between regions of high and low strain within the shoulder. Visual feedback is integrated into the strain map display, while vibrotactile feedback is delivered through a wearable haptic device. Despite challenges related to axial rotation accuracy and the camera-dependent nature of shoulder angle measurements, user experiments, conducted independently for shoulder elevation and planar elevation, reveal that vibrotactile feedback shows better performance compared to visual feedback. Consequently, this study concludes that vibrotactile feedback has the potential to prevent shoulder injuries with strain maps, but also still needs to improve for future work. ... Read more

Hand-worn haptic systems must be able to produce high-quality haptics while also being lightweight and energy-efficient. In order to meet market expectations, research is being done into novel drivetrains that may be implemented in hand worn devices. This is being pushed forward by the growing demand in the virtual reality sector, which is placing pressure on the development of new wearable force feedback technology. In order to validate a proof of concept, this thesis proposes a novel force feedback drivetrain with a dual actuator setup-a motor and a brake—that is integrated into a tabletop prototype. Using both a motor and a brake will reduce weight and improve the haptic rendering quality relative to each component when considered separately.

To evaluate the presumptive advantages, a theoretical analysis into the suggested hybrid actuation solution is conducted. The analysis comprises of numerous simulations using a model of the hybrid drivetrain's working principle. Additionally, the analysis is employed to investigate and spot any early-stage defects or undesirable behavior. Consequently, a 3D model was created in order to 3D print a functioning prototype in order to apply the theory in a physical version. The prototype is initially used to validate the simulation model and enforce the findings of the theoretical analysis. Force data is measured when using the prototype and in turn is fed into the simulation model to assess whether the output behaviour is consistent with the prototype. Both the model's competence and the prototype's predictability are assessed using the outcomes.

Finally, an experiment is conducted to both asses the mechanical performance and predictability as well as the participant's perception; 2 virtual environments and 3 actuation modes (motor, brake, and hybrid) were cross-examined and each repeated 6 times, for a total of 36 trials. This experiment ultimately assesses the prototypes validity and determines whether the theoretically assumed benefits are present in the prototype as well. Finally, the participant completes a questionnaire in the form of a 5-point Likert scale to determine if the experiment data complies with the users experience.

The theoretical analysis revealed both reliable behavior and potential problems, such as oscillations in feedback force due to rapid switching between actuator activity. However, the cause of this behaviour was identified and anticipated to be less pronounced during use of the prototype. The prototype was successfully designed and produced, and a comparison to the model revealed consistent findings and, thus, predictable behavior. However, the model occasionally miscalculated the velocity. The experiment also shows that the hybrid drivetrain approach is promising and outperforms the state of the art both in mechanical prowess as well as physical fidelity when providing haptic feedback; the motor fails to render rigid objects and the brake cannot render spring-like objects, while the hybrid approach can render both successfully.

The thesis demonstrates that additional investigation into the suggested drivetrain is supported, despite some obvious limitations. When compared to equally powerful DC motors, the hybrid technique, as employed in the prototype, is capable of portraying the largest variety of haptics, including hard walls, impacts, and stiffness while potentially conserving weight. ... Read more

The demand for robots in the industry is increasing. Organisations acknowledge the need to innovate in order to keep up with the competition. When robots were first introduced, they took over human tasks if they could execute the task faster than humans. However, this often has a negative effect on the meaningfulness of work. That’s why cobots were introduced, robots that could work together with humans in the same workplace. This provided the industry with many new opportunities. However, as with any new technology, research has to be done to find out how this technology can best be implemented. That’s why FRAIM was founded. They are researching how cobots can be introduced without decreasing the meaningfulness of humans’ work. FRAIM decided to join the Brightsky project, to take their research into practice. However, with the Brightsky project, FRAIM is facing other challenges. Not only does the technical feasibility have to be researched, but also the employees’ needs and other stakeholder values have to be taken into account. A challenge that can be perfectly tackled with a human-centered design approach.

Firstly, through interviewing and stakeholder mapping, the essential stakeholders are identified. These stakeholders have high power or/and high interest in the company and thus contribute to the future of the organisation. Afterwards, the stakeholders are brought together for a co-creation session. In this session, the stakeholders discuss their individual needs and values to identify the contradictions and similarities. Next, a shared vision is created, representing the stakeholders’ view on ‘optimising’ the workflow.
A stakeholder which is often negatively affected by the implementation of robots, is the employee, their needs are often neglected. That’s why this project’s focus is mostly on this stakeholder. During context mapping sessions, the themes of meaningful work are identified. These themes represent the conditions of meaningful work for the employees at KLM Engines.

During the research phase, it became clear that the stakeholders are unaware of the contradictions and similarities of their values. Furthermore, the knowledge and skills needed to understand the possibilities and limitations of implementing robots and their consequences on the stakeholders are lacking.
In the design phase, a co-design is created to tackle this challenge. Co-design is chosen, because it is well-known to be used for bringing together different expertise and for sharing knowledge and skills. Besides, the concept of game design is used. Game design helps to represent a real-life situation in a fun and engaging way and helps the participants to explore different scenarios. Using game design helps to visualise the consequences of robots on the workflow well.

The co-design is tested and evaluated to assess if the co-design meets the design requirements and to discover how the tool can be improved. A final iteration is done to improve the concept.

Lastly, the project is concluded and reflected on. The limitations of the project are discussed and future research and design recommendations are proposed.
... Read more

Robotic teleoperation has gained popularity in various applications, including the nuclear industry, where the experience and intelligence of a human operator is necessary for making complex decisions that are beyond the autonomy of robots. Human-robot interfaces that help strengthen an operator’s situational awareness without inducing excessive cognitive load are crucial to the success of teleoperation. This paper presents an intuitive visual interface that allows operators to simultaneously control a 6DOF camera platform and a robotic manipulator whilst experiencing the remote environment through a virtual reality head-mounted-display (VR-HMD). The proposed system, Orbital Head-Mounted Display (OHMD), utilizes head rotation tracking to command camera movement in azimuth and elevation directions around a fixation point located at a robot's end-effector. A human factor study was conducted to compare the interface acceptance, perceived workload, and task performance of OHMD with a conventional interface utilizing multiple fixed cameras (Array) and a standard head-mounted display implementation (HMD). Results show that both the OHMD and HMD interfaces significantly improve task performance, reduce perceived workload and increase interface acceptance compared to the Array interface. Whilst orbital head-mounted display excelled in usefulness, the standard HMD interface allowed operators to perform robotic welding tasks significantly faster. Still participants reported they preferred OHMD due to the increased assistance and freedom in viewpoint selection. ... Read more

To safely deploy legged robots in the real world, it is necessary to provide them with the ability to reliably detect unexpected contacts and accurately estimate the corresponding contact force. Therefore, a collision detection, isolation, and identification pipeline is proposed for a quadrupedal manipulator. An approach based on band-pass filtered forces is presented which accurately detects a collision, and estimates the collision time span. Next, the colliding body link is isolated. Finally, a collision identification method accurately identifies the magnitude and direction of the force. It is robust against model inaccuracies, unmodeled loads and any other potential source of disturbances acting on the robot. The framework is validated using extensive hardware experiments in various scenarios summing up to 416 collisions, including trotting and additional unmodeled load on the robot. ... Read more

Overtaking on two-lane roads can cause dangerous situations, due to drivers’ errors during the gap acceptance decision. Understanding gap acceptance decisions can help mitigate these situations. Response time (i.e. the time it takes the driver to evaluate the gap and make a decision) has been shown to provide valuable insights into the cognitive processes during gap acceptance decisions in pedestrian crossing and left turn decisions. However, for overtaking, previous studies have not investigated the response time.
I investigated how factors that play a role in gap acceptance decisions during overtaking (namely, size of the gap and the drivers’ velocity) influence the response time. I also studied the change of the drivers' velocity during the decision process. I proposed a novel method to measure the response time in drivers' overtaking decisions, and conducted an experiment to demonstrate the usefulness of the method. 25 participants were presented with multiple overtaking situations in a driving simulator experiment, with varying distance gaps. I analyzed how the probability of gap acceptance varied with the distance gap and participants’ velocity at the start of the overtaking situation using a generalized logistic mixed model. I also analyzed how the response time varied with the distance gap, participants’ velocity and the decision outcome using a linear mixed model. Lastly, I analyzed if the velocity changed between the start and the end of the decision process and whether there was a relation between the decision outcome and the distance gap and the velocity change, using a linear mixed model.
The probability of accepting a gap increased significantly with the distance gap and the velocity of the participant. The response times for rejected gaps were on average 0.7s longer than accepted gaps. The response time increased with the distance gap (42ms per 10m), but decreased with the velocity (-92ms per 1m/s). The velocity changed differently between the decision processes leading to either decisions, with an average difference of 4 m/s.
Using the proposed method, I found that the factors which influence the outcome of the decision, also influence the response time. The dependence of response time on the distance gap and participants’ velocity could be explained by the speed-accuracy tradeoff or the difficulty of the decision. Furthermore, it was shown that the drivers already adapt their velocity during the decision process, instead of after they have made their decision. So, I can conclude that using the proposed method for measuring response time can give insight in the way drivers make gap acceptance decisions during overtaking. My results provide basis for cognitive process models that can help further understand the results and are capable of predicting decision outcomes and response times. ... Read more

When a trainee is (re)learning a skilled movement, physical guidance from a trainer is crucial. Yet, providing physical cues to guide movements is highly challenging when training is digitally mediated (e.g.remotely). This work demonstrates the utility of pseudo forces generated by a wearable tactile interface for providing non-intrusive movement guidance. First, we developed hardware to generate pseudo forces using asymmetric vibrations, whose frequency and amplitude can be tuned to vary the acceleration of the pseudo forces. Maximum acceleration of 160 m/sec^2 is obtained at a frequency of 40Hz and amplitude of 1. Second, the utility of the generated pseudo forces to provide movement guidance was explored by involving 19 participants in 4 separate experiments: 1) Symmetric and asymmetric vibration comparison, 2) Duration modulation, 3) Amplitude modulation, 4) Frequency Modulation of asymmetric vibration. For every experiment, the elements of movement guidance: direction and joint angular velocity were investigated. Participants perceive directional cues with 96% accuracy (P<0.001), and translate the perceived pseudo forces into directed arm movements, with a uniform joint angular velocity of 14+/-8 degrees/second for the duration of the provided pseudo force. The joint angular velocity of the arm movement changes until 12 degrees/second with frequency. With these findings, we anticipate pseudo forces to be the foundation for remote guidance of human body movements in fields like rehabilitation and sports. ... Read more

Traffic jams occurring on highways cause increased travel time as well as increased fuel consumption and crashes. Traffic jams without a clear cause, such as an on-ramp or an accident, are called phantom traffic jams and are said to make up 50% of all traffic jams. They are the result of an unstable traffic flow caused by human driving behaviour. Recent studies have shown how automating the longitudinal vehicle motion of only 5% of all cars in the flow can dissipate phantom traffic jams. However, automation introduces new problems, mainly regarding safety when human drivers need to take over the control. This research tries to answer whether phantom traffic jams can be dissolved or prevented using haptic shared control. This means of control keeps the human in the loop and would therefore eliminate the takeover problems while still benefiting from the advantages of automation. 24 participants took part in a driving experiment in a fixed base simulator. In these experiments, we tested haptic shared control against manual control and full automation for longitudinal motion. Results show that traffic jam dissipation performance for haptic shared control lies between manual control and automation. The number of unsafe situations is reduced compared to the automated condition. We conclude that haptic shared control is able to reduce the increased fuel consumption and crashes caused by phantom traffic jams. ... Read more

Conventional steering systems in passenger vehicles have a mechanically fixed steering ratio. The steering sensitivity, defined as the amount of vehicle response to the driver's steering wheel input, remains fixed with changing road environments. Research has shown that driving comfort and safety can be improved when the vehicle's steering sensitivity is adapted to the road curvature profile. Current vehicle models can adapt the vehicle's steering sensitivity based on vehicle's speed and driver's steering wheel angle (i.e variable gear-ratio systems), or on individual selection of driving mode (i.e sport, comfort). It is hypothesised that adaptation of the steering sensitivity based on frequency measures of individual drivers' steering behaviour could improve driving comfort and safety. In a fixed-base driving simulator experiment involving 24 participants, real-time adaptation of steering wheel sensitivity based on individual drivers' steering behaviour was compared to three different fixed steering sensitivity settings on a road with changing road curvature. Here I show that intermittent switching frequency in drivers' steering movements can be used to adapt the vehicle's steering response to a varying road curvature. Significant differences in intermittent switching were found between different road curvature sections and between different steering sensitivity settings. Driver's positional control and comfort ratings did not significantly increase with the steering sensitivity adaptation strategy. ... Read more

Currently, active steering systems are implemented in production vehicles to assist the driver by varying the steering response, where the adaptation of the steering response is either initiated by the vehicle or by the driver. Though studies have shown that these steering systems affect the steering performance positively, the effect on driving behavior and driver acceptance is not taken into account. Moreover, the effect of machine-initiated and driver-initiated steering systems on the driver has yet to be investigated. The aim of this driving simulator study was to examine the effects of machine-initiated and driver-initiated steering systems on driving behavior and driver acceptance. During the experiment, the machine switched between a slow and fast steering response on predetermined locations based on the traffic conditions and road curvature, and the driver could switch the steering response by pressing a mouse-button which was attached to the steering wheel. The expectation was that the preferred steering response would be dependent on the steering task, and that between machine- and driver-initiated steering there would be a trade-off between effort and acceptance. Twenty-four participants drove with a constant velocity on a two-lane road with three sections i.e. overtaking traffic vehicles, driving on a straight road and driving on a curved road. Four conditions were completed in a counter-balanced order i.e. passive slow steering response (PS), passive fast steering response (PF), machine-initiated steering (MI) and driver-initiated steering (DI). A post-experiment questionnaire showed that the participants had a preferred steering response for each of the three sections: between slow and fast for overtaking, slow on the straight road, and fast on the curved road. Furthermore, a lower effort and higher acceptance was achieved with the active steering systems compared to the passive steering systems, where there were no significant differences between MI and DI. For future research, it is recommended to further investigate the use of a range of steering responses and free driving speed. ... Read more

This study proposes an LCA system that provides haptic guidance during lane changes. This system is fully integrated with LKA functionality to provide continuous lateral support during highway driving. Two different system configurations of this LCA are investigated. One is a generalized LCA that provides lane change reference trajectories based on a fixed lane change duration value of 4 seconds. The other is an adaptive LCA that provides personalized lane change reference trajectories through trial-by-trial adaptation to lane change duration of previously driven lane changes. The effects of these systems with respect to mental workload, lateral control performance and user acceptance are investigated. This is observed in an experiment with three different driving sessions for each participant. A manual driving session, a driving session in which the generalized LCA is active and a driving session in which the adaptive LCA is active. The experiments are conducted on a 6 DoF motion-based simulator with 34 participants, driving in a three-lane highway simulation environment with a scripted traffic scenario. To measure mental workload, an auditory cognitive secondary N-back task is introduced. The results show that the introduction of a generalized LCA or adaptive LCA does not have significant influence on mental workload compared to the manual driving session. When the adaptive LCA is introduced, lateral control performance is enhanced compared to the generalized LCA and manual driving. Additionally, user acceptance expressed as subjective usefulness is increased by introducing the adaptive LCA compared to the generalized LCA. Furthermore, inter-driver variability of the lateral control performance during lane changes is reduced by the proposed trial-by-trial adaptive LCA system compared to the generalized LCA system and manual driving. ... Read more