Numerous techniques have been developed in order to explain the reasoning process of black-box models. Among them is a class of models that are designed to be inherently interpretable: select-then-predict models (a.k.a. rationale-based models). These models are meant to explain their prediction by highlighting part of the input as evidence. The evidence, called the rationale, should consist of the most salient parts of the input text that contribute the most to the model's decision.
However, according to some recent studies, these models are not truly interpretable, because they do not provide faithful explanations (i.e., explanations that accurately reflect the true reasoning process of the model).
In this thesis we give a formal definition of the degree of unfaithfulness to quantify unfaithful behavior. Then, we introduce an experiment to test the faithfulness of select-then-predict models and prove that select-then-predict models can provide unfaithful rationales. Lastly, we introduce a loss function, which we call the unfaithfulness loss, which minimizes the degree of unfaithfulness of select-then-predict models and teaches them to produce more faithful and plausible rationales.

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.

Advanced Driver Assistance Systems (ADAS) play a crucial role in enhancing road safety and driver comfort by providing real-time alerts and support. However, the effectiveness of ADAS signals in influencing driver situational awareness (SA) remains an area requiring further investigation. This study examines how different ADAS speed limit alert designs impact the three levels of SA: perception, comprehension, and projection.

A real-world driving experiment was conducted in Amersfoort along a 4.3 km route, comparing two ADAS systems with distinct signal designs. System A provided both the current and upcoming speed limits, while System B displayed only the current limit. Data collection included eye-tracking for SA1 (perception), self-reported questionnaires for SA2 (comprehension), and speed analysis for SA3 (projection). Statistical analyses, including significance tests, correlation analysis, and Linear Mixed Models (LMM), were employed to evaluate the influence of ADAS design and driver-related factors on SA.

Results indicate that while most SA indicators did not show significant differences between the two ADAS systems, System A consistently demonstrated advantages in visual attention allocation and comprehension. Additionally, driver experience and familiarity with ADAS were found to influence SA, with experienced drivers exhibiting reduced cognitive load and faster responses. A weak correlation between different SA levels suggests that drivers integrate ADAS alerts with external environmental cues.

This study highlights the importance of designing ADAS alerts that provide anticipatory information to enhance driver awareness and decision-making. Future research should expand sample sizes, incorporate additional SA indicators, and explore the long-term effects of ADAS exposure. These findings offer valuable insights for optimizing ADAS signal design to improve road safety and driving performance.

Robotic rehabilitation systems that provide proprioceptive information offer a promising approach to helping stroke survivors regain lost proprioceptive functions. One potential way to provide proprioceptive information is viscosity rendering. However, how the modulated viscosity rendering affects brain activity remains unclear. To investigate the correlation between viscosity rendering and brain activity and to provide a neurological basis for the design of robotic rehabilitation systems, an experimental setup was developed to deliver various viscosity rendering and fixed stiffness rendering during hand movement. In the experiment, twelve healthy participants interacted with virtual bottles with the same bottle stiffness but filled with liquids of different viscosities under the same movement speed, providing different levels of proprioceptive information in the form of force on muscles and joints. Control conditions without viscosity and stiffness rendering were also tested, involving both passive and active hand movements at the same speed. Results showed that stronger mu-ERD and beta-ERD were observed during movements with viscosity and stiffness rendering compared to control conditions. No significant evidence suggested that different viscosity in rendering caused variations in mu-ERD or beta-ERD. Additionally, no significant differences were found between active movement without haptic rendering and passive movement in EEG activities. These findings suggest that while the existence of viscosity and stiffness rendering during movement strengthens brain activity, modulating viscosity rendering does not significantly affect this response. This insight is particularly valuable for designing robotic rehabilitation systems that incorporate viscosity rendering.

Relocated haptic feedback from the fingertip to the proximal phalanx can alter the perception of physical interactions by simultaneously displaying relocated multimodal tactile cues. However, how these relocated tactile cues alter the vibrotactile sensitivity of the fingertip remains unclear. This two-site stimulation study employs a customdesigned multimodal haptic ring for the proximal phalanx to evaluate the effect of relocated cold, hot, and pressure stimuli on the vibrotactile sensitivity of the fingertip. Our results show no significant difference between the vibratory detection thresholds of six multimodal tactile conditions. In contrast to single-site multimodal stimulation, where vibrotactile sensitivity is significantly altered by pressure and thermal stimuli, these results imply the feasibility of applying thermal and pressure stimuli without significantly altering fingertip sensitivity. Relocation of tactile stimuli keeps the vibrotactile sensitivity of the fingertip intact and opens up the possibility of altering tactile perception while interacting with the physical environment, making this technology feasible for mixed reality applications.

Lely is developing a tethered manure applicator robot, designed to work on the fields of farms. This vehicle called the Jojo, is constrained in its movement due to the attached tether. It can not make tight turns and can only reverse by backtracking the driven path. This thesis explores the path planning issue and provides a novel solution. The problem is best described as the shortest path from a starting point to a set of strokes that cover the field, with a constrained turning radius. We first explore existing solutions and discover that none exist that are suitable. Further research finds that the behaviour of this vehicle is best compared with steerable needles, but at a different scale. First, we construct a simulation environment for the vehicle. This simulation is used to develop the plan execution engine, path follower and safety checks. This is necessary for testing the solutions in the simulation and in the real world. Secondly, with some inspiration from the solutions provided for the steerable needles, we arrive at a method for finding a Jojo path. The method makes use of a large graph that represents translations associated with a given location. We also provide methods for attaching the starting point and destinations in the form of strokes, either via the start or end of the strokes or via the middle. A Jojo plan is then obtained by performing a directed Steiner tree approximation on this graph, where the starting point is the root and the terminals are the strokes. In order to make the approach fast enough, we spend a substantial amount of time profiling and analysing the program and design a number of performance improvements. These allow for the approach to solve instances that are larger than necessary. Thirdly, we test, compare and improve the approach. We look at both small instances to perform numerical analysis in order to optimise a set of parameters. We then inspect larger instances that are based on real world scenarios. Lastly, we perform a set of real world tests using the vehicle. One of these tests highlights a shortcoming of the algorithm, which we discuss and provide a solution for

Patients with neuromuscular diseases that are unable to speak, but whose cognitive ability has been maintained, can be benefited from Brain Computer Interfaces (BCIs). The decoding of inner (covert) speech from EEGs consists of one of the state of the art methods that aim to tackle this issue. High variability between subjects, as well as low signal to noise ratio (SNR) undermine the methods used, and introduce the need for computer assisted solutions. Thus, machine learning models as well as large amounts of recorded data are required to design effective algorithms and produce substantial results. In this study, covert vowel classification was performed in a systematic way, by making use of two openly shared databases from literature; the Coretto database, that contains EEG recordings of native Spanish speakers, and the DAIS dataset, which includes EEG recordings of native Dutch speakers. Six classifiers were initially selected to perform 5-class classification: a Random Forest (RF), a k Nearest Neighbours (kNN), a Gaussian Naive Bayers (GNB), a Deep Convolutional Neural Network (DCNN), a Shallow Convolutional Neural Network (SCNN) and a Long Short Term Memory Recurrent Neural Network (LSTM). The DCNN outperformed the other methods, with average intra-subject accuracies of 35% for Coretto and 39% for DAIS (chance level 20%). Afterwards, an Overt versus Covert trials experiment was implemented, to test the limits of overt speech decoding from EEGs. The overt result was slightly higher than covert, with an intra-subject average value of 37.8% for Coretto and 40.5% for DAIS (chance level 20%). Finally, binary classification was performed to identify those pairs of vowels that can be classified more efficiently. Vowels /a/ and /u/ seemed to perform better in average in both datasets (average of 64.8% for Coretto and 64.4% for DAIS with a chance accuracy of 50%). Future work should focus on identifying the useful parts of the EEG recordings, increasing the SNR and the resolution of the electrodes, and defining the most appropriate dictionaries of words/vowels for a BCI. Also, more studies should follow systematic ways of comparisons between datasets, to obtain less ambiguous insights and lead this field to improvements.

The agricultural industry is undergoing a transformative shift towards efficiency and large-scale production. Manual labor is unable to meet the increasing demand, leading to the development of automation techniques. During production, objects are adjusted and moved along a line to maintain productivity. This work investigates the feasibility of developing a system that detects and determines the position and rotation of a small clipping object in a cluttered environment. To achieve this feasibility, a neural network has been trained on a custom dataset with images containing 6-D pose annotations of a clip that do not require manual annotations. The designed system also executes a validation step with a second set of sensors in the form of a stereo camera. The entire methodology has been evaluated on a test setup. The small and round properties of the clip cause a rotational error that does not fall in positional and rotational requirements set by a mechanical grabber on a robotic arm. Higher positional precision of annotation data and depth information from the stereo camera is necessary to make this methodology feasible.

Feedback error learning (FEL) is a classical computational model that describes human motor learning. It consists of forward and inverse models representing internal dynamics and environmental disturbances. Such models can be used as controllers that represent the function of the motor cortex. On top of FEL, a model has been built with jointly trained feedforward and feedback controllers using a neural network. The controllers that actuated six muscles driving a two-degrees-of-freedom arm model were trained offline. This model successfully simulated human learning of point-to-point reaching movements in a horizontal plane when it was tasked to adapt itself in a null field (NF) and a velocity-dependent force field (VF). In this study, we further tested this model in the divergent force fields (DF) and a channelled force field (CF) to observe its performance. The comparison between the simulation results and the experimental evidence suggests that this model can predict some of the key features of the learning process, such as the kinematics, the muscle dynamics, and the impedance profiles. The learning decay was hindered when the lateral error was artificially eliminated by the CF, as reported in the literature. Overall, the model could converge towards realistic

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.

Background: For rigorous software testing, integration and end-to-end tests are essential to ensure the expected behavior of multiple interacting components of the system. When software is subjected to integration or end-to-end tests, it is often unfeasible to test every code change individually, as the runtime of these tests is usually significantly larger compared to unit tests. For this reason, batches of code changes from multiple authors are often tested simultaneously. Problem: An issue with testing multiple changes simultaneously is that it can be unclear which change form which author caused the failure when tests fail, as all changes from all authors included in the test can be at fault. Design: To solve this, a new automatic fault localization algorithm called GitFL is introduced, which combines state-of-the-art fault localization with version control history information for enhanced performance. GitFL was evaluated on a C++ repository at Adyen where tests are considered to be end-to-end. Findings: It showed that the addition of version control history information significantly increases the performance of fault localization for systems where multiple changes are tested simultaneously. Societal implications: This work provides insights on improved fault localization for these systems, which could enable organizations which develop these systems to speed up their testing and development processes. Originality: This work contributes by focusing on fault localization specifically for systems where multiple changes are tested simultaneously, which was not researched before.

The problem of assisting users in comprehending the robotic scenario information in a retail setting has been studied. To design the system, an integrated ontology composed of several IEEE standard ontologies and a labelled property graph (LPG)-based ontology modified from the Web Ontology Language (OWL)-based ontology was proposed to symbolize information in the robotic environment. Then, a knowledge graph (KG)-based chatbot was developed to provide natural language interaction with users. A case study in a retail setting was designed, and the results were analyzed. The effectiveness of our designed system has been experimentally validated in both static and dynamic scenarios, with at least 1.5 times improvements.

Background: A brain-computer interface (BCI) is a system that enables humans to control a computer by their brain signals. This can be achieved by modulating the sensorimotor rhythm (SMR) through both motor execution and motor imagery. The potential enhancement of spinal reflexes and motor control through SMR training is attributed to the hypothesis that activity-dependent brain plasticity guides spinal plasticity during motor skill learning. However, the signal processing needed for conversion from raw brain signals to a robust control signal is challenging. The recorded electroencephalogram (EEG) signals are contaminated by multiple unknown sources and suffer from inter-subject variability, complicating the development of the BCI.
Objective: To obtain a robust control signal the study 1) investigated the relation between event-related desynchronization (ERD) and mechanical stretch reflex size in the flexor carpi radialis across four muscle pre-loads consisting of 0%, 5%, 25% and 40% of maximum voluntary contraction (MVC), 2) investigated the ability of three offline signal processing paradigms in distinguishing between periods of rest and activity using EEG data associated with motor execution and motor imagery, 3) built a pseudo-online signal processing paradigm to simulate real-time signal processing based on a single trial and a continuous data stream.
Method: Mechanical stretch perturbations were applied to the wrist under four percentages of MVC during motor execution and imagery conditions in six healthy subjects. The data anal- ysis encompassed signal processing techniques including pre- processing with a large Laplacian filter, feature extraction through autoregressive modelling (AR), power spectral density (PSD), or discrete wavelet transform (DWT), and classification using linear discriminant analysis (LDA).
Results: Mechanical stretch reflex sizes and ERD amplitude significantly increased with increasing percentage of MVC for motor execution trials. For motor imagery trials, no significant correlation was found between the stretch reflex size and ERD amplitude. The offline signal processing paradigms resulted in classification accuracies of 73.55% (PSD), 71.96% (DWT) and 57.13% (AR). The classification accuracies significantly increased with increasing percentage of MVC. The pseudo-online paradigm resulted in a mean classification accuracy of 51.38%.
Conclusions: The EEG-based BCI shows potential for enhancing the functional recovery of patients with motor disorders. The findings demonstrate that feature extraction methods PSD and DWT could effectively distinguish between periods of rest and activity in motor execution data. Nevertheless, for the intended application, including real-time processing based on single trial motor imagery data, BCI performance should be improved. Future research should focus on motor imagery EEG data encompassing motor imagery training and feedback on motor imagery performance.

Vulnerable road users account for more than 50% of traffic fatalities, and among these, pedestrians are the most susceptible to fatalities due to their distraction and misperception of other road users. To mitigate their plight, systems that warn drivers and pedestrians in case of a possible collision have been developed. Among systems that focus on the pedestrian's perspective, existing concepts are capable of predicting collisions but lack elements that monitor the visual attention of pedestrians. We address this gap by developing a gaze-based pedestrian warning system based on the Tobii Pro Glasses 2, a head-mounted eye-tracker. The system consists of: (1) a custom trained fast neural network (YOLO v4) on the KITTI object detection dataset that processes the video feed of the eye-tracker to detect approaching vehicles and (2) a module that uses the pedestrian's gaze to identify whether their attention falls on the closest moving vehicle that is approaching the pedestrian, both in real-time. If the pedestrian does not look at the approaching vehicle, they are given an auditory alert that warns them of a possible collision. In a pilot study conducted on a busy road in an urban environment, the system was evaluated under different pedestrian walking speeds and gaze behaviours to test the algorithm's robustness. The pilot study revealed that our system alerted the inattentive pedestrian with an accuracy of 67%. The mean vehicle detection accuracy and a mean moving vehicle identification accuracy from the pilot were 93% and 60%, respectively, a promising result given the use of only a mono camera. Despite the use of computer vision techniques, the system worked at an inference speed of 50 FPS due to the multi-processing capabilities of our algorithm. Our efforts are a first step in developing pedestrian warning systems based on eye-tracking technology to improve road safety in the future. The algorithm (Python-based) code used for this work has been made publicly available.

The design of a guide-path map is a well-known problem in various industries, such as automated guided vehicle (AGV) systems, road networks, and train track design. Nowadays, designing a path map is a manual, time-consuming, and expensive process. The solutions often turn out to be far from optimal. Several approaches are known to generate path maps automatically. The main challenges are computation efficiency due to large solution spaces and the lack of feasibility of solutions for application to real-world layouts. Little research focuses on the importance of computational efficiency to keep up with the pace of dynamic user requirements and constraints.

The goal is to design a framework that balances computational efficiency with feasibility in automated path map generation. A feasible path map can continuously distribute requested flow targets and is practically useable without many manual changes. It is expected that less optimal but still feasible solutions can be generated more efficiently by using a smaller solution space.

The framework consists of four stages: a practical problem, a mathematical model, a mathematical solution, and a practical solution. All solutions are theoretically validated on two aspects: capacity and performance.

The framework is tested using three layouts: low complexity and small-scale, complex and large scale, and one in between. The exploratory model outperforms in the complex large-scale layout while the goal-oriented models outperform in the other layouts.

Path maps are generated in a reasonable and controllable time. The settings allow a trade-off between the efficiency and feasibility of solutions. Due to its efficient computation, the model has the potential to generate path maps that are directly usable in real applications, if several realistic implementations are included to improve the feasibility of solutions.

Driving often requires dividing attention between scanning the environment and following navigation instructions. The aim of this study was to investigate the effects of conformal and non-conformal navigation symbology on drivers' visual search. A driving simulator study was conducted where participants drove with conformal and non-conformal navigation symbology while performing an active visual search task consisting of detecting visual stimuli. The stimuli were highly transparent, yellow spheres projected on the side of the road. Eighteen participants drove three trials using different navigation symbology while their eye-movements were being recorded. Results showed a significant reduction in miss rate by 14% (from 44% to 38%) and a significant increase in mean fixation duration on visual stimuli by 16% (from 227 ms to 263 ms) when using conformal symbology. Eye-movements had a trend to be calmer during the use of conformal symbology indicated by lower saccade lengths. Furthermore, subjective ratings showed a clear preference for the conformal symbology. The findings of this study provide additional evidence for the benefits of conformal symbology within an automotive context, showing drivers to have an improved ability to sample the outside scene.

Newly educated sailors have a tendency to neglect the outside view when sailing [1], instead relying on systems like the Electronic Chart Display and Information System (ECDIS) [2] for information on ships in the area.Augmented Reality (AR) has been proposed as a solution to this problem, by visualising ships in the area in 3D at their physical location using a Head-Mounted Display (HMD), including a data table shown above the ship containing information useful for navigation. This thesis describes the design of an AR application to achieve this goal. The designed application is shown to work anywhere where Automatic Identification System (AIS)data is available, and can show ship information close to the sending ship’s actual location. The app is then evaluated, and its problems are identified. Subsequently, two of these problems, namely occlusion and limited Field of View (FoV), are further evaluated. Four possible solutions for the occlusion problem are presented,which work by moving the data table that is shown above the occluded ship hologram so that the user will still be able to access the data when a ship is occluded. Two solutions move the data of the occluded ship vertically up and down respectively, so that the user can access the data of the farther ship by looking up or down. The other two solutions move the data horizontally, showing the data of the farther ship to the left or right of the closer ship, depending on which side the farther ship is located. In the last solution, the data table of the closer ship is also moved, in the opposite direction of the data of the farther ship. These solutions are tested on four participants: two students and two experts. The main results of the thesis are a working application that visualises ships in the area in AR using live AIS data, an overview of identified problems with the app and an analysis of four possible solutions for the occlusion problem. Although no definite best solution for the occlusion problem was found, the results indicate that the solution where the data of both the near and far ship is moved horizontally shows the most potential out of the four tested to be used for marine navigation.

Traffic is a self-organising, regulated system, but there are several ambiguous situations where no rules apply. In such situations, communication is important in order to achieve a smooth traffic flow and a safe situation. However, in the majority of traffic conflicts, adequate coordination between road users is lacking. Communication is particularly important for vulnerable users such as pedestrians, given their significant share in traffic accident statistics worldwide.

The increasing amount of automation in vehicles creates a potential social interaction void, which could further impede safety and a smooth traffic flow, as the chances of misinterpreting the behaviour of another road users might increase. Some researchers and companies have suggested that these problems could be addressed by adding additional means of communication to a vehicle. However, there is limited consensus as to what the most effective form and content of such communication would be.

The aim of this study is to investigate the effect of two different types of information on a pedestrian's crossing behaviour. This work describes the development and evaluation of a textual, external human-machine interface (eHMI), with the aim of complementing existing signals, such as vehicle movements, with explicit information addressed to human road users. Three conditions, specifically: (0) no information, (1), a pedestrian advice (Wait/Walk) and (2) a vehicle based status (Drive/Brake) are compared with respect to their effect on four variables related to the pedestrian: the minimum distance maintained to the vehicle, measured as a virtual 'Time to Collision', changes in the decision to cross (Decision Certainty), the feeling of safety as a percentage of the duration of a scenario (Decision Efficiency), and subjective acceptance.

28 participants participated in three repetitions of the same experiment in three different environments: a field test on a public road, an experiment in an animated virtual reality environment and an experiment using 360* video recordings. Participants stood on the pavement along an urban road in a European setting, and were asked to press a button when they felt safe to cross. During the experiment, a car drove by while showing one of the three types of information.

The total time that participants felt safe was significantly higher in scenarios where the car stopped, and significantly lower if the car did not stop, when information was offered. Time to Collision, decision changes and subjective acceptance also showed statistically significant differences in most scenarios, as these variables show a strong correlation among each other. However, one difference was found between the types of information for the Decision Efficiency variable. Here, effect sizes for the Wait/Walk eHMI (egocentric information) were larger than for Drive/Brake (allocentric information).

The results show that providing additional information could improve safety and traffic flow, although the type of information has a limited influence on the behaviour of a pedestrian. This suggests that when choosing a certain type of information, other factors should be taken into account that could perhaps be more decisive. Ultimately, this research contributes to finding the optimal characteristics of a standardised eHMI design.

A large number of pedestrian fatalities and the emergence of autonomous vehicles (AVs) creates a need for research in vehicle to pedestrian (V2P) interaction. Gestures (e.g., hand movement, eye contact) used for communication between pedestrians and manually driven vehicle plays a significant role to reduce ambiguities and make safe road crossing decisions. With autonomous vehicles on the roads in the near future, it may be possible that pedestrians are not able to understand the vehicle behaviour or vice a versa. Virtual reality (VR) simulations are used to test a new method of communications such as external human-machine interface (eHMI) between AVs and pedestrians. Virtual reality has emerged as an effective and cost-efficient tool in various fields such as surgical training, technician training and driver simulators. Among many benefits of virtual reality, is it’s ability to expose its users to critical situations where the real environment would be dangerous and therefore, could be a viable tool for conducting V2P research. However, it is essential to understand how pedestrians’ behaviour and perception match in VR or 360◦ video with real life to rely on them
as an experimental method. The main goal of this study was to examine the
effectiveness of VR and 360-degree video environments in terms of pedestrian perception and behaviour to draw actionable conclusions on the reliability of VR.
An experiment was conducted to test the interaction between pedestrians and autonomous vehicles using three different environments: a field test on a a public road, a replication of the test in an animated virtual reality (VR) simulation and a third repetition using a 360-degree video recording of the same location. The animated virtual environment was designed using Unity and C#, and the 360-degree videos were recorded at the field test location from participants’ position at a height of 180 cm. The participant was standing on the sidewalk on a two-lane road in Delft in each environment. To compare human perception of distance and speed and behaviour in terms of gap accepted during road crossing scenarios in 360-degree video and animated VR, three tasks being: 1) Distance estimation, 2) Speed estimation and 3) Gap acceptance was conducted in each environment with a within-subject design (N = 30).

Results showed that there were no significant differences between the three environments for distance estimation, gap acceptance tasks and measures of presence. However, significant differences were found between animated VR and 360-degree video environment for perceived speed of 30 km/h. According to the median values, the perceived distance in real life and animated virtual environment deviated below 5% from the ground truth values. However, the median values for perceived distance in 360-degree video environment for
distances of 40 m and 63 m were overestimated. The deviation in perceived distance increased as the ground truth value increased, wherein the animated virtual environment showed the highest amount of variance amongst the three environments. Underestimation of the speed of about 5 - 10 km/h was observed in both field tests and animated virtual environment for speeds below 50 km/h. The median values show that the perceived speed was most accurate in a 360-degree video environment. However, the animated virtual environment was more consistent with real life perceived speed. A significant positive correlation between distance and speed estimates showed consistency in human perception across the three environments. A negative correlation was found between perceived distance and gap acceptance ratio, whereas a positive correlation was found between perceived speed and gap acceptance ratio. The presence scores were relatively higher for field tests and 360-degree video environments compared to the animated virtual environment. However, the scores were high enough to validate the three environments subjectively. Only one participant experience motion
sickness in virtual reality tasks with a score of above 2 out of 3, experiencing a moderate sickness but was successfully able to complete the whole experimental task. Overall results show that pedestrian perception and behaviour in 360-degree video and animated VR environment doesn’t differ significantly from real life. These results have a strong implication on the usage of virtual environments for studying pedestrians’ perception and behaviour when interacting with vehicles.

Monitoring selectively directed auditory attention in groups can be helpful in a range of contexts, such as in education. In real-world settings, selectively directed attention cannot be monitored by relating physiological signals to known event markers. Determining the similarity of physiological responses across individuals in a group (i.e., physiological synchrony - PS) may provide a solution to this problem, as it has been shown reflective of shared attentional engagement toward audiovisual stimuli. This study was aimed at examining whether PS in autonomic and neural measures are suitable markers to recover selectively directed attentional focus and moments of emotional or task-related relevance in a composite auditory stimulus.
Electroencephalography (EEG), electrodermal activity (EDA) and cardiac inter-beat interval (IBI) were monitored from participants who heard a composite auditory stimulus, consisting of a narrative
audiobook, interspersed with short stimuli. One group of the participants (n = 13) was instructed to attend to the narrative, and the other group (n = 13) was instructed to attend to the short stimuli,
that were tones that attending participants needed to keep track of, emotional sounds and a stress test attending participants needed to perform.
EEG and EDA signals of participants were more strongly synchronized with those of participants in the same attentional condition than with those of participants in the other attentional condition. No such effect was found in IBI. For a single individual, PS in EEG allowed attribution to the correct attentional group in 85% of the cases, for EDA this was 81% and for IBI accuracy was below chance level. Further analyzing EEG, PS was higher during relevant stimulus presentation than over the entire audiobook. PS across stimulus-attending participants was higher than across book-attending participants during these short stimuli.
It is concluded that PS in both autonomic and neural measures can be suitable to recover selectively directed attention, as indicated by the high classification accuracy for EEG and EDA. The results of
this study also indicate that PS may be suited for the detection of moments of emotional or task-related relevance, as PS in EEG was higher during relevant stimulus presentation than over the entire experiment. Future research should investigate whether PS in autonomic measures could also be suited for detection of relevant events.