Discriminating between multiple driving events using physiological signals remains challenging. This study examined whether gaze dynamics and pupil responses could distinguish multiple driving events and reveal age-related physiological patterns. Twenty-seven participants (12 young, 15 old) completed a simulated driving scenario featuring nine events while physiological signals were recorded. Statistical analyses revealed significant event-specific modulation of gaze dispersion metrics ( = 0.571–0.582, ). Gradient boosted trees achieved 82.4% accuracy (95% CI [74.3%, 89.8%]) classifying four events (Bicycle, DeerAlert, LowGas, StopAtGasStation) using 7 gaze-based features, with SHAP analysis identifying horizontal deviation standard deviation and median as primary discriminators. Time-series motif discovery revealed consistent gaze motif amplitudes (3.0–3.4 z-scores) across events, while pupil motifs showed selective enhancement during monitoring tasks (3.1–3.8 z-scores). Age-stratified analyses uncovered distinct physiological patterns: younger drivers exhibited greater gaze variability with broader scanning patterns, whereas older drivers demonstrated elevated pupil motif amplitudes (particularly during LowGas: 3.88 vs. 3.18 z-scores) alongside more constrained visual exploration. The convergence of statistical, machine learning, and motif discovery approaches establishes gaze dynamics as sufficient for multiclass event discrimination in simulated driving. These findings demonstrate that gaze-based features alone can reliably distinguish between specific driving events, while motif analysis reveals temporal dynamics and age-related patterns that warrant further investigation in larger samples, providing a foundation for interpretable driver monitoring systems.

This study presents a computational method for generating eye movement data (specifically horizontal saccade velocity profiles) with the aim of providing benchmark input for the evaluation of data processing workflows and for the employment of machine and deep learning algorithms. The usability of generated saccades is further explored by comparison of two selected algorithms for saccade detection. Precisely, we assess and compare the modified and adaptive I-VT (Identification by Velocity Threshold) algorithm and the MAD (Median Absolute Deviation) algorithm. Both algorithms are tested on the publicly available dataset recorded in one subject while watching a movie trailer and on synthesized saccade velocity profiles. Obtained results suggest that both algorithms have exactly the same performance when generated saccade velocity is not scarcely contaminated with noise (F1Score of 94.42%), while for generated velocity with noise contamination, detection performance of both algorithms significantly degrades (F1 - Score of 36.57% for I-VT and slightly better performance of 40% for MAD algorithm). The proposed method for generating saccadic eye movements profiles could be further utilized to simulate other types of eye movements profiles, such as glissades. Also, the presented algorithm could be explored for the evaluation and design of novel algorithms for eye tracker data analysis, hopefully offering more reliability and higher precision in the eye tracker-based studies.

This study aimed to identify which information should be displayed on a head-up display (HUD) in semi-automated vehicles to enable the driver to maintain better situational awareness during the manual operation of the vehicle. It further explored how does the size of the field of view of the HUD affects such information presentation, while taking into consideration user's personal preferences and opinions. Four head-up display interfaces were developed differing in the amount, frequency and field of view of visual information presentation, and were implemented in a simulated semi-automated vehicle. The HUDs were evaluated in a user-study with a within-subject design with 30 participants. The obtained results revealed that versions with smaller and larger field of view HUDsevoke similar driving performance, levels of cognitive load, user experience and perceived usability, suggesting that the HUD's field of view size does not have an overall significant effect on the driver's situational awareness. The results reveal that display of information, which can help with obtaining and maintaining higher situational awareness levels, contribute to better driving performance. Drivers on the other hand, prefer also display of information for lower situational awareness levels.

This article presents a new Fast Hash-based File Existence Checking (FHFEC) method for archiving systems. During the archiving process, there are many submissions which are actually unchanged files that do not need to be re-archived. In this system, instead of comparing the entire files, only digests of the files are compared. Strong cryptographic hash functions with a low probability of collision can be used as digests. We propose a fast algorithm to check if a certain hash, that is, a corresponding file, is already stored in the system. The algorithm is based on dividing the whole domain of hashes into equally sized regions, and on the existence of a pointer array, which has exactly one pointer for each region. Each pointer points to the location of the first stored hash from the corresponding region and has a null value if no hash from that region exists. The entire structure can be stored in random access memory or, alternatively, on a dedicated hard disk. A statistical performance analysis has been performed that shows that in certain cases FHFEC performs nearly optimally. Extensive simulations have confirmed these analytical results. The performance of FHFEC has been compared to the performance of a binary search (BIS) and B+tree, which are commonly used in file systems and databases for table indices. The results show that FHFEC significantly outperforms both of them.

In this paper we propose a prototype of a spatialized text reader for visually impaired users. The basic functions of the system are reading arbitrary files, converting text into speech using different synthesized voices and spatializing synthesized speech. Visually impaired users can thus listen to the content of a file read by various synthesized voices at different spatial positions. Some metadata (e.g. pre-inserted tags) has to be added to the file before processing in order to define the voice, pitch, reading rate and originating spatial position for any part of the content. We believe such a way of electronic book reading can be a significant improvement for visually impaired users if compared to mundane and dull screen readers. The core of the system is based on Java platform using FreeTTS speech synthesizer and JOAL positioning library. The latter is improved by the external MIT Head Related Impulse Response (HRIR) library. The use of headphones is obligatory in order to perceive spatial sound correctly. The system is a work in progress and is currently under evaluation by twelve visually impaired test subjects.

In this paper we evaluate two different auditory interfaces for word processing application. The interfaces are in the form of hierarchical menu structures and use spatial sounds in two different spatial configurations. The first menu - AH - has a ring shaped horizontal configuration of the sound sources, whereas the second - AV - has a vertical configuration of the sound sources. Spatial sounds are used to increase the information flow between the user and the application. In this way, multiple sources (i.e. menu commands) can be played and perceived simultaneously. The main goal of the experiment was to choose the most efficient interface based on a user study with 16 test subjects. The test subjects were asked to perform five different tasks with two auditory interfaces and a normal visual GUI. The variables observed in the user study were: task completion times, navigation performance and various subjective evaluations. The AV interface proved to be the most efficient and user friendly and will therefore be used in further experiments.

In this paper we evaluate the safety of the driver when using an embedded communication device while driving. As a part of our research, four different tasks were preformed with the device in order to evaluate the efficiency and safety of the drivers under three different conditions: one visual and two different auditory conditions. In the visual condition, various menu items were shown on a small LCD screen attached to the dashboard. In the auditory conditions, the same menu items were presented with spatial sounds distributed on a virtual ring around the user's head. The same custom-made interaction device attached to the steering wheel was used in all three conditions, enabling simple and safe interaction with the device while driving. The auditory interface proved to be as fast as the visual one, while at the same time enabling a significantly safer driving and higher satisfaction of the users. The measured workload also appeared to be lower when using the auditory interfaces.