**Introduction**
Reliable visualization of the coronary origins is essential in neonates with Transposition of the Great Arteries (TGA), as coronary anatomy directly influences surgical planning for the arterial switch operation. Photon-counting computed tomography (PCCT) offers high spatial resolution, spectral imaging, and potential dose efficiency, making it promising for neonatal coronary assessment. However, quantitative evidence guiding the choice of electrocardiogram (ECG)-synchronized acquisition strategy, virtual monoenergetic imaging (VMI) level, and reconstruction phase remains limited. This study evaluated which PCCT acquisition and reconstruction strategy provides the most favorable balance between coronary origin visibility, motion robustness, and radiation dose under neonatal cardiac motion.

**Methods**
A neonatal coronary artery phantom was developed to assess small-vessel visibility under controlled cardiac motion. The phantom contained contrast-filled tubes of 1.1 and 2.0 mm diameter, oriented along three spatial directions in a polymethyl methacrylate background. Cardiac motion was generated using an ECG-synchronized motion simulator at 120, 130, 140, and 150 bpm. Two ECG-synchronized PCCT strategies were evaluated: high-pitch flash acquisition and prospective sequential acquisition.

Protocols were compared under matched conditions at 53 keV. Flash acquisitions were additionally reconstructed at 40 and 47 keV to assess VMI effects, while prospective acquisitions were reconstructed at 53 keV across cardiac phases from 25% to 80% of the R–R interval to assess phase influence. Image quality was quantified using segmentation-derived metrics describing tube geometry, cross-sectional area, shape, edge definition, attenuation, spatial overlap, and contrast-to-noise ratio (CNR). Radiation exposure was assessed using scanner-reported CT dose index volume, dose-length product, and size-specific dose estimate.

**Results**
Under matched dynamic conditions at 53 keV, flash and prospective acquisition showed broadly comparable preservation of tube size, cross-sectional area, shape, edge definition, attenuation, and spatial overlap. The clearest protocol-related difference was observed for CNR, which was more favorable for flash acquisition. When the most favorable prospective reconstruction phase was selected, prospective acquisition improved overlap-based preservation.

Within the flash protocol, 40 keV provided the strongest iodine contrast and highest CNR, whereas 53 keV reduced equivalent diameter and cross-sectional area errors. Within the prospective protocol, reconstruction phase was the dominant optimization parameter, with later phases around 70–80% of the R–R interval generally outperforming mid-cycle phases. Prospective acquisition resulted in substantially higher scanner-reported dose indices than flash.

Heart-rate effects were non-monotonic and depended on acquisition timing, reconstruction phase, tube orientation, and z-position. The 1.1 mm tubes represented the most demanding condition, showing greater proportional errors, reduced overlap, lower CNR, and higher segmentation instability.

**Conclusion**
High-pitch ECG-triggered flash PCCT is supported as the preferred practical acquisition protocol for neonatal coronary origin visualization in neonates with TGA. For the primary visual task, 40 keV VMI is preferred because it provides the strongest iodine contrast and highest CNR, while 53 keV remains valuable as a complementary reconstruction when geometric preservation or size-based assessment is important. Prospective acquisition may be reserved for selected cases in which phase-resolved information is expected to improve interpretation sufficiently to justify the additional dose and complexity.
... Read more

This study evaluates recent large language models for extracting meta-analytic data from scientific articles in which relevant results are reported in text, tables, and figures. The study compares GPT-5.2, Claude Opus 4.6, Gemini 3.1 Pro Preview, and Gemini 3.1 Flash Lite Preview across three input representations: the original PDF, structured Markdown derived from the PDF, and a combined input consisting of the PDF together with cropped tables and figures. The final analyzed corpus contains 56 papers from a social robotics meta-analysis and includes 193 statistical data rows for which pre- and post-intervention values had to be extracted. Each row was scored on five numerical fields relevant for effect size calculation (Pre-Mean, Pre-SD, Post-Mean, Post-SD, and n), yielding 965 scored cells in total. Model outputs were compared against manually corrected ground-truth values from the original meta-analysis, which were verified against the source papers. Predicted rows were first matched to the corresponding ground-truth rows, after which the individual numerical values were scored using predefined numerical tolerances. This design allowed extraction errors to be interpreted not only as aggregate model failures, but also in relation to source format, input representation, target-row construction, and numerical reading. Across the full evaluation corpus, Markdown was often the strongest or near-strongest input representation overall, although the strongest representation differed across text-, table-, and figure-dominant papers. The highest overall performance was achieved by Claude Opus 4.6 with Markdown input. Papers in which the target values had to be extracted mainly from figures were the most difficult. A diagnostic follow-up on 17 papers that repeatedly showed errors in identifying the correct paper-condition combinations found that performance improved when the relevant condition was specified in advance and the model only had to extract the corresponding values. This suggests that many remaining errors were associated with identifying the correct extraction target row rather than with numerical reading alone. ... Read more

Current evaluations of Large Language Model (LLM) spatial reasoning focus on several isolated competencies rather than a unified task, and use an array of different input formats. As a result, the influence of spatial representation and output Frame of Reference (FoR) on performance in navigation tasks remains unclear. This study asks: how do spatial representations and frames of reference influence LLMs' spatial reasoning capabilities, and which combinations are conducive to it?
This research investigates the spatial reasoning and navigation capabilities of one reasoning and one non-reasoning LLM. Using perfect mazes as a controlled testbed, this thesis examines how various input spatial representations, including visual (JPG and ASCII), grid-based (JSON and Tagged per-cell), and graph-based (Adjacency List) formats, interact with different output FoRs to influence model performance.
The methodology involves an evaluation using Gemini 2.5 Pro (reasoning) and Gemini 2.5 Flash-Lite (non-reasoning) across 11 spatial representations and three output FoRs: allocentric using absolute coordinates ("coordinates"), allocentric using absolute directions ("absolute directions"), and egocentric (relative directions). Performance is measured using two metrics: a "completion score", defined as the percentage of the path navigated correctly before the first error, and the mean number of output tokens generated, used as a proxy for efficiency.
The findings of this research indicate that performance is highest when mazes are expressed using structured graph-based spatial representations, particularly Adjacency List JSON (a graph-based representation formatted as a JSON file), across model types, while the choice of output FoR strongly shapes outcomes, with absolute coordinate responses yielding substantially better results than egocentric ones that require continuous relational analysis and state tracking and therefore lead to markedly lower completion scores, especially for the non-reasoning model. In addition, inspection of internal reasoning traces suggests that the use of formal graph-solving algorithms is positively correlated with success, while exclusive reliance on heuristics and unfounded declarations of confidence are negatively correlated with completion scores.
By systematically varying input spatial representation and output FoR this work provides the first integrated evaluation of these factors, addressing the lack of unified benchmarks and clarifying how methodological choices shape observed LLM spatial reasoning performance. ... Read more

Vacuum extraction (VE) is an important obstetric intervention used to assist vaginal delivery during prolonged or complicated labour. Safe and effective use of VE requires technical skill and experience, yet in many low- and middle-income countries (LMICs), access to suitable training opportunities and realistic simulation models remains limited. Existing training simulators are often too expensive, insufficiently durable, or unable to reproduce the anatomical and biomechanical conditions of assisted vaginal delivery. Building on earlier work by Wang, this thesis aimed to improve the VE training simulator by focusing on three main objectives: modularity, durability, and biomechanical realism.To support the redesign, a clinical validation of the existing simulator was first considered together with stakeholder input and previous recommendations by Wang to define the design requirements. Based on these findings, several concepts for the muscle-bone interface were developed and evaluated to improve modularity. A silicone bulb-end connection combined with a sliding PLA cover plate was selected as the most suitable solution, as it enabled disassembly and replacement of individual soft-tissue components without permanent bonding.To improve durability, repeated mechanical robustness testing was performed to identify structural weaknesses during simulated VE procedures. These tests showed that failure was governed by the interaction between geometry, fixation, reinforcement, and loading conditions. Based on the identified failure mechanisms, targeted design changes were implemented in the final prototype. To improve biomechanical realism, different silicone material configurations were evaluated based on the traction forces generated during VE. A hybrid configuration with a DragonSkin ™10 first-layer pelvic muscle and a DragonSkin ™20 levator ani provided the best balance between force realism and repeated-use performance.The final prototype completed 100 consecutive VE cycles without functional failure and generated an average peak pulling force of 92 N. Overall, this thesis demonstrates that a redesign strategy centered on modularity, durability, and biomechanical realism can substantially improve the functional performance of a low-cost VE training simulator.
... Read more

Affordances, or action possibilities, have been explored to enable robotic manipulation with everyday objects, however the effect of an agent's embodiment has not received much attention. Here we investigate how embodiment changes affordances between a human and robot. We present a method to automatically generate affordance pseudo-labels from a robotic manipulator for the task of grounding (localising) affordances on an object, as there is no such existing dataset. We then propose a general model for embodiment-conditioned affordance grounding, and explore three ways to condition on the embodiment. Our model learns to perform an affine transformation on image embeddings based on the effect of embodiment on the affordance. We evaluate all three variants of our model and compare them to a variant without embodiment conditioning and a state-of-the-art affordance grounding method. The results show that our best performing model decreases affordance prediction error by 25% when compared to the variant without embodiment conditioning and by 68% when compared to the state-of-the-art method. Through our results we demonstrate that embodiment matters when perceiving affordances. ... Read more

This study investigates how font size, text-drawing style, and drop shadow affect the visibility, legibility, and comprehension of overlaid text. The purpose of this study is to better understand the role of these design factors and to offer practical guidelines for presenting information effectively on see-through displays. Twenty-five participants completed a visual search task, where they were asked to locate a target word presented under varying conditions on a complex background. A full factorial design was employed, incorporating four font sizes (0.10°, 0.15°, 0.20°, and 0.60°), six text-drawing styles (plain green text, plain white text, white text on blue billboards with 30%, 50%, 75% or 100% opacity), and the presence or absence of a drop shadow. Performance was evaluated across conditions in terms of noticeability (as a measure of visibility), processing time (as a measure of legibility), and word identification accuracy to determine significant differences. Applying a drop shadow improved legibility in plain text, while billboards lowered the upper performance threshold for font size. Although billboard conditions outperformed plain text conditions, varying billboard opacity had no significant effect on processing time, word identification, or noticeability. Overall, the findings suggest that font size, text styling, and background complexity interact to influence text visibility and legibility. ... Read more

Reaction-time paradigms are widely used to investigate multisensory processing and sensorimotor integration, yet their application to postural control has been limited. This may stem from the view that most self-generated balance-correcting motor actions occur without conscious awareness. However, this contrasts with evidence that reaction times to external stimuli can still be modulated during balance tasks (e.g., sitting, standing, walking), suggesting that even automatic balance control can interact with higher-level cognitive processing. Yet the temporal dynamics distinguishing automatic postural from consciously mediated responses to balance perturbations remain underexplored. In this study, we used a robotic balance simulator to impose disruptions to ongoing balance and dissociate balance-correcting and perceptual mechanisms of standing balance control. Participants stood on a robotic simulator that applied 200 ms torque perturbations of varying amplitudes. Additional ankle torque perturbations mimicking natural balance-control statistics were delivered to artificially increase ongoing motor noise. We recorded both EMG-based corrective muscle responses and perceptual reaction times via button presses, enabling direct comparison of automatic and conscious responses. Both response types decreased with increasing perturbation amplitude, consistent with findings from other sensory domains. Crucially, perceptual reaction times increased with higher noise amplitudes, whereas automatic postural responses did not. This dissociation highlights distinct neural mechanisms underlying conscious perception and postural control based on the functional purposes these processes fulfill. ... Read more

Current visuomotor manipulators jointly train their perception-planning-action components simultaneously using an end-to-end framework to avoid hand-engineering components. Despite this, methods for human-to-robot object handover tasks require a perception component that segments the hand from the object, which can introduce error propagation. For this reason, this study investigates the applicability of an end-to-end framework that eliminates the need for hand-object segmentation in a simulated human-to-robot object handover task using HandoverSim.

To address this, a behavior cloning agent is used to convert camera input into RGB-D voxel space and output discretized 6-DoF manipulation to directly discover features for the handover task. This study introduces a framework that combines the behavior cloning agent with the HandoverSim, which allows experimenting with various training configurations. These configurations consist of experiments with: 1) expert demonstration data; 2) optimal camera setup; 3) handover objects; and 4) voxel-based RGB augmentation techniques.

The trained model is evaluated on its generalization to diverse handover conditions in the HandoverSim Benchmark. The results demonstrate that the behavior cloning agent can learn features for the handover task without requiring a perception component. The model learns the grasp-object relation whilst minimizing contact with the hand. Despite this, performance is limited by sparse training data and grasping accuracy. ... Read more

The vestibular system plays a central role in maintaining upright balance by encoding head motion and integrating this information with visual and somatosensory cues. When the relationship between self-motion and vestibular input becomes unreliable, the central nervous system (CNS) adapts to preserve postural stability. Previous studies demonstrated that adaptation occurs when altered vestibular input remains coherently linked to head movement; however, it remains unclear whether recalibration persists when this motion--afference relationship is degraded by non-coherent noise (\cite{Heroux2015, Chen2020}).

This report investigates vestibular recalibration under two forms of galvanic vestibular stimulation: a coherent, head-coupled stimulus derived from a validated motion-to-current conversion model, and the same stimulus combined with high-amplitude non-coherent noise. Fourteen participants completed standing-balance trials assessing baseline sway, externally replayed vestibular perturbations, and short-term learning during a brief eyes-open calibration period. Postural stability was quantified using T1 lateral displacement, and adaptation was assessed by comparing sway variability before and after calibration.

Under coherent stimulation, participants exhibited clear recalibration: sway variability increased immediately after stimulation onset but decreased during calibration, returning toward baseline levels. In contrast, non-coherent stimulation produced substantially greater sway and reduced adaptive improvement, indicating that noise limits the CNS’s ability to reinterpret vestibular input. Nonetheless, some recalibration was still observed, although highly variable across individuals. Additional findings revealed transient post-stimulation after-effects and modest order-dependent influences, though these did not reach statistical significance.

Overall, the results indicate that vestibular recalibration depends critically on the coherence and reliability of motion-linked vestibular input. When the motion–afference mapping is degraded by an external noise source, the CNS down-weights vestibular cues and exhibits limited adaptive learning. ... Read more

This thesis investigates how temporal design choices affect the real-time feasibility of human motion prediction models. Two state-of-the-art models were evaluated: GCNext, a data-driven graph convolutional model, and PhysMoP, a hybrid model combining a physics-based and data-driven branch. Controlled experiments showed the influence of input history length, temporal resolution, and the model architecture on prediction accuracy and latency. Results showed that longer observation windows do not necessarily improve accuracy, while increasing the latency. Both models were sensitive to changes in temporal resolution, as they implicitly assumed a fixed sampling rate. Real-time performance analysis indicated that single-pass architectures were favoured, while autoregressive models suffered from compounding delay. Retraining GCNext with shorter input histories and optimising autoregressive passes achieved substantial latency reduction with minimal accuracy loss. These results show that temporal configurations are critical design choices for achieving real-time feasibility of human motion prediction models. The code for this paper is available at https://github.com/AndrewHutani/HMP ... Read more

With mobile robotics being applied for more and more complex applications, their autonomy should be preserved. While a lot of research is performed into the direction of failure prediction for autonomous processes or systems, the field of mobile robots has received less attention. Proactive failure prediction for mobile robots is a useful tool to prevent unwanted downtime and undesired damages. This work attempts to fill this research gap by showing the applicability of anomaly detection methods for failure prediction in the field of mobile robots. Specifically, we employ an unsupervised Variational Autoencoder to predict failures in the operational data from the Discovery Collector, a manure cleaning robot developed by Lely Industries. We elaborately showcase the feature engineering steps which yield the best performance, provide the performance of three general datasets, and state promising next steps for root cause classification which is enabled by accurate failure prediction. All in all, our work shows that the use of feature offsets, calculated from desired values compared to actual values, enhances the model performance tremendously. The provided datasets showcase F1-scores ranging from 0.64-0.76, showing the proposed solution is able to solve the failure prediction problem in the field of mobile robots, while highlighting the encountered limitations for future improvement.
... Read more

Large Language Models (LLMs) possess significant semantic knowledge about the world, making them valuable for high level control for robots through Vision-Language-Action (VLA) models. These models integrate an LLM to deduce semantic knowledge from a robot’s vision and natural language inputs, facilitating real-world actions. Despite their potential, VLA models are a relatively new research area, with applications mostly limited to simulations or household tasks and insufficient validation in broader contexts. This study aims to develop a LLM-Enhanced Robotic Affordance and Control System (LERACS) for robotic manipulation in applied cases such as the management and maintenance of electrical grid infrastructure. LERACS is designed to visually ground manipulable objects and decompose tasks based on user instructions within a human-robot interaction chat interface, using ChatGPT. A system validation and an AI user experiment were conducted to evaluate its effectiveness in interpreting and performing actions based on pre-made and synthetically generated user instructions. These assessed LERACS’ performance across various settings and instructions. Results indicate high success rates in environmental interpretation and task execution, with robust labeling accuracy, especially in complex settings. Feedback from the AI user experiment highlighted LERACS’ adaptability, identified areas for improvement, and demonstrated its practical utility across diverse settings and task complexities. ... Read more

Surgical navigation involves transferring preoperative imaging data, along with preplanned information, onto the patient in the operating theater without using constant radiation. This technique has proven effective and is widely adopted across various surgical specialties. Research has shown a consistent trend in surgical robotics, with numerous initiatives using this technology to navigate the robot’s end-effector within the patient’s anatomy. For this purpose, commercially available surgical navigation systems are often employed. However, these systems, which are primarily dominated by optical tracking, are not necessarily suited for robotic systems and exhibit limitations such as low update frequency and line-of-sight issues. Additionally, performance reporting in current surgical robotic research is highly inconsistent, and clear guidelines are lacking. This research aims to develop a surgical robotic navigation system to work towards establishing a performance benchmark and systematically assess various error components as a first step toward guiding the field of surgical robotic navigation. To this end, two systems, the Haply System and the Dual-Robot System, have been developed and evaluated for technical accuracy and registration accuracy in both static and dynamic environments. Furthermore, sensor fusion methods have been explored to enhance performance in the Haply system. The results and analysis indicate that the Dual-Robot System is the most accurate in dynamic navigation and presents a viable alternative to optical tracking systems in terms of performance. However, its clinical adoptability remains questionable. ... Read more

With the advancement of Artificial Intelligence leading to increasingly human-like outputs, assessing a machine’s ability to exhibit human-like intelligence has become more essential than ever. This study aims to investigate how human-like chess players perceive four conditions: one human opponent and three different types of algorithms. One of these algorithms, Maia, has been trained on human data and aims to play the most human-like move. In a custom-designed experiment similar to a Turing test, chess players faced off against Maia, Stockfish and a human without knowing their opponent’s nature. After each game, the chess player assessed how human-like the moves of the opponent were and estimated whether they played against an engine or a human opponent. During the game, participants were asked to think aloud about their next move and react towards the moves of the opponent. Additionally, the gaze of the player was captured with the SR EyeLink Portable Duo at 1000Hz, with the goal of finding differences within the player’s gaze while participants tried to discover the nature of their opponent. Results from the experiment revealed that, based on responses to a subjective questionnaire, the perceived humanness of Maia is statistically similar to a human and different from the other two chess engines. From the analysis of the voice recordings, categories of sentences were identified that could suggest recognition of the opponent, specifically: "expected", "unexpected", "human-like" and "engine-like". From the eye-tracking results, the average fixation duration and pupil diameter changes following the opponent’s move were compared for each condition, but showed no statistical differences between conditions. In summary, Maia was perceived more human-like compared with other chess engines. However, differences in underlying cognitive processes on how the human perceived this difference in a Turing Test experiment were not identified. ... Read more

As technology continues to evolve at a rapid pace, robots are becoming an increasingly common sight in our daily lives.
Robots that work with humans need to adapt to a variety of users and tasks, and learn to optimise their behaviour. For non-specialist users to interact with such robots, the robot's learning process needs to be transparent through its behaviour. Reinforcement Learning (RL) is a promising learning method to achieve this adaptability. However, the behaviour generated by RL is not inherently transparent because of the exploration/exploitation trade-off that is needed to optimise a policy for a specific task.

A RL algorithm is Temporal Difference (TD) learning. In TD learning, the algorithm updates a Q-table to keep track of Q-values. Q-values represent the expected future rewards that the agent (the actor that decides what action to take) can receive by taking a specific action in a certain state. Calculating the Q-values involves a value called the Temporal Difference, which is the difference between the current Q-value with the received reward added and the Q-value for the future state and chosen action.

Emotions are a natural way of communicating intent and situational appraisal for humans. In this study, emotional expressions based on Temporal Differences were implemented as a means to increase the transparency of a robot's learning progress. The effects on the robot's learning progress, learning result, and user experience were analysed.

A between-subject experiment with 61 participants on the following three robot modes was performed: no emotions, simulated emotions, and simulated emotions with matching attribution (see Table \ref{table:robotModes}). The simulated emotions are hope, fear, joy, and distress, which were expressed by a humanoid robot. The robot mode with simulated emotions and matching attributions would explain for what task it was feeling hope or fear. The task was a simple task where a human teacher had to help a humanoid robot to learn to express three different colours based on human commands.

The results demonstrate minimal differences between these three conditions. This means that for simple tasks, emotional expressions grounded in RL do not have a significant effect, and thus do not help nor hurt. The findings are discussed, and it is proposed that emotion simulation is beneficial for tasks that are more complex, afford some robot autonomy, and for which the emotion is informative about how the user should influence the robot's actions to the benefit of the robot's policy. ... Read more

This document lays out the design and development of a specialized device aimed at monitoring the eye movements of premature babies, which creates an opportunity to create better sleep-monitoring systems.

Monitoring sleep has great significance, especially for premature babies, because it allows for the adaptation of care to accommodate for better and more sleep. Different sleep stages can be identified, but the identification does differ for adults compared to premature babies. Different characteristics including brain activity, heart rate, respiration, eye movement and face and body movements are linked to those sleep stages.

Eye movement is a characteristic that could be used more optimally for the creation of a sleep-monitoring system. Eye movement can be tracked using different methods, but electrooculography shows the greatest promise. For this specific method, different options exist concerning electrode placement and materials. The use of this system for premature babies during sleep also creates limitations related to the fragility of the skin of prematurely born babies. Furthermore, these babies normally lie in the Neonatal Intensive Care Unit, creating additional context-specific considerations.

Different research methods were used, resulting in a complete picture of the steps taken after premature birth occurs. These steps are portrayed in a scenario. The requirements and wishes drawn from these are used to develop ideas. During the development, a choice was made to create two parts for the device, namely the shell and the electronics module.

In the final design, the shell contains specialized electrode places, named snap-rings, as well as features that create adaptability and additional details to accommodate the use of multiple devices that are already being used on premature babies. The electronics module includes an electrode configuration that can be placed into the snap-rings in the shell. The electrode protrudes the snap-ring and is pushed onto the skin. The force needed to place the electrode is decreased by using a material with a low Young's modulus, ensuring contact between the electrode and the skin without causing damage to it. This novel configuration enables the use of dry electrodes, eliminating the need for gels and adhesives.

Some recommendations are given, which concern further the development of the device, the eventual certification and the embedding of the device within a system that monitors sleep.

Overall, a complete overview of the concept design of a device that can monitor eye movement of premature babies is given, which creates an opportunity for the improvement of sleep monitoring systems for these babies. ... Read more

With the introduction of automated driving systems come benefits such as the improvement of traffic safety. However, with an increasing level of automation in vehicles also comes an increase in interaction with in-vehicle technology by drivers while they are meant to supervise the automated driving systems. Due to more interaction with in-vehicle technology and a vigilance decrement of the driver in Level 2 driving, an increase in reaction time of the driver is seen when intervention is needed by the means of a take over request. This delay in reaction by the driver opposes the benefit of the introduction of automated driving systems and causes hazardous situations. To try and circumvent the effect of vigilance decrement, this paper attempts to demonstrate the reduction of noticing time of the Hands-On-Wheel warning message for drivers of Level 2 vehicles while interacting with in-vehicle technology through the implementation of a gaze-contingent interface. The results of this experiment indicate a 79.3% lower noticing time of the Hands-On-Wheel warning message when the stimulus is placed in a gazecontingent manner, while the participants engage in secondary tasks on the in-vehicle technology. The placement of the stimulus on the head unit when the participant is already looking at it reduces the primary task load of touching the steering wheel and causes for the stimulus to be seen quicker as compared to a static interface. However, the performance of the secondary task seems to decrease when using a gaze-contingent interface. This is due to the intrusive nature of the placement of the stimulus, which demands the driver to store information regarding the secondary task in their working memory while they attend to the primary task. Despite the decline in secondary task performance, the reduction of noticing times of time critical messages when placed in a gaze-contingent manner could be beneficial to the safety of autonomous driving functions where the driver has a vigilance task and is engaging in secondary tasks. ... Read more

Human operators who are tasked with monitoring automation systems may experience a high visual demand to process the information streams from these systems. The visual sampling behavior of human operators can be described using mathematical models. These models can help designers improve environments where multiple signals are present for human operators to monitor, to a configuration that can be processed properly.

This study consisted of two parts. The first part investigated how peripheral vision plays a role in visual sampling behavior and task performance, specifically in the experimental eye-tracking setup presented in Eisma et al. (2018). In this setup, participants were instructed to monitor a bank of six dials, of which each dial pointer had a threshold indicator, and press a response key whenever a dial pointer crossed the threshold indicator. In the second part, the sampling models as presented by Senders (1983) are implemented to predict sampling trajectories. The sampling characteristics that resulted from the
predictions were then evaluated.

The results of the experiments show that peripheral vision plays a role in visual sampling and task performance. More specifically, sampling behavior is more evenly distributed among dials, and task performance is lower when peripheral vision is absent. The main attractor in the peripheral vision is shown to be the pointer speed. Moreover, the learning effect presented in Eisma et al. (2018) is not apparent when peripheral vision is absent.

The results of the predictions showcase the sampling behavior characteristics, some of which show similarities with the results from the experimental data. ... Read more

With the increase in development of self-driving cars, research has been conducted to retain humanized interaction between cars and other road users, such as pedestrians. One way to retain this type of interaction is through the use of external Human-Machine Interfaces (eHMIs). This project aims to contribute to this field of research by exploring the case of eHMI-equipped, self-driving cars yielding for a crossing pedestrian. From literature it is known that light- and text-based signals are both promising ways of utilizing an eHMI. Due to the often simplified nature of these models, the goal of this project was to investigate if their findings hold up when increasing the cognitive load of the pedestrian. An eye-tracking experiment was conducted where two promising eHMI signal types (i.e., flashing lights and message) were tested in a realistic scenario, where the participant took the role of pedestrian. In the experiment, the cognitive load of the participants was varied, by applying different sizes of gaze contingent windows to the stimuli.

A range of 63 different trials of 10s each were shown to 23 participants. This set of trials included 7 different presets, aimed to test the effect of the signal type and the gaze window size independently. The participants' task during the experiment was to indicate when they deemed it safe to cross the road, by pressing the space bar. Their gaze was measured with an eye-tracker, after which it was transformed into three different metrics: saccade count, saccade amplitude and fixation duration. Additionally, a novel metric, the dispersion of the grouped gaze data, was introduced and explored. Finally, a questionnaire was conducted, investigating the self-reported clarity of the different signal types. Together with the reaction time, these metrics aimed to answer the following research question: What influence does raising the cognitive load for crossing pedestrians have on the effectiveness of text-based eHMIs as opposed to light-based eHMIs and no eHMI?

The results show that, as has been previously established, light- and text-based signals showed very similar response times and feature similar gaze characteristics when compared to each other. Both outperformed the condition where no signal was shown. Raising the cognitive load shows a decrease in saccade count and saccade amplitude, paired with a higher fixation duration. However, no proof could be found that raising the cognitive load has an influence on the effectiveness on any of the different signal types, meaning that either, the cognitive load was not raised by enough, or there is no actual effect. The dispersion showed that for the light-based signal, the focus on the stopping car is lost the quickest after the signal was shown.

The results of this study may help in explaining how pedestrians base their traffic decision on the type of signals being shown. This may also serve as a basis to further explore the possible effects of cognitive load on the effectiveness of these types of signals. ... Read more