This study investigates reading performances and eye movements in individuals with eccentric fixation due to age-related macular degeneration (AMD). Overall, 17 individuals with bilateral AMD (7 males; mean age 77.47 ± 5.96 years) and 17 controls (10 males; mean age 72.18 ± 5.98 years) were assessed for reading visual acuity (VA), reading speed (Minnesota low vision reading chart in Slovene, MNREAD-SI), and near contrast sensitivity (Pelli-Robson). Microperimetry (NIDEK MP-3) was used to evaluate preferential retinal locus (PRL) location and fixation stability. Eye movements were recorded with Tobii Pro-glasses 2 and analyzed for reading duration, saccade amplitude, peak velocity, number of saccades, saccade duration, and fixation duration. Individuals with AMD exhibited significantly reduced reading indices (worse reading VA (p < 0.001), slower reading (p < 0.001), and lower near contrast sensitivity (p < 0.001)). Eye movement analysis revealed prolonged reading duration, longer fixation duration, and an increased number of saccades in individuals with AMD per paragraph. The number of saccades per paragraph was significantly correlated with all measured reading indices. These findings provide insights into reading adaptations in AMD. Simultaneously, the proposed approach in analyzing eye movements puts forward eye trackers as a prospective diagnostic tool in ophthalmology.

Introduction: Conditionally automated vehicles promise significant advances in road safety, efficiency, and comfort. Objective: This study investigates the effects of driver demographic characteristics on stabilization times after taking over from fully automated driving. Methods: Using data sets from two driving simulator studies, we analyzed 704 takeover attempts by 58 drivers with diverse backgrounds. The study used linear mixed models to assess the influence of age, gender, driving experience, and previous simulator experience on stabilization times for speed, deceleration, heart rate, pupil diameter and off-road gaze duration ratio. Results: The analysis showed that age had a significant effect on driver speed, while gender, driving experience and simulator experience affected deceleration. Discussion: These results suggest that current models of post-takeover stabilization do not apply uniformly to all demographic groups and should better account for demographic differences. This highlights the need for personalized approaches in automated vehicle systems to improve safety and user experience.

In the realm of conditionally automated driving, understanding the crucial transition phase after a takeover is paramount. This study delves into the concept of post-takeover stabilization by analyzing data recorded in two driving simulator experiments. By analyzing both driving and physiological signals, we investigate the time required for the driver to regain full control and adapt to the dynamic driving task following automation. Our findings show that the stabilization time varies between measured parameters. While the drivers achieved driving-related stabilization (winding, speed) in eight to ten seconds, physiological parameters (heart rate, phasic skin conductance) exhibited a prolonged response. By elucidating the temporal and cognitive dynamics underlying the stabilization process, our results pave the way for the development of more effective and user-friendly automated driving systems, ultimately enhancing safety and driving experience on the roads.

Supporting drivers in different levels of automation was one of the key goals in the HADRIAN project. Following the approach of a "fluid interface", i.e., an interface that considers the state of the driver, vehicle, and environment, and which uses different modalities to support the driver, a human–machine interface (HMI) was developed and compared to a baseline HMI in a simulator study (n = 39). The integrated fluid HMI aimed at supporting the driver in automated driving in SAE level 2 and 3 by providing mode relevant information, supporting the driver during take-over requests by the system, and supporting engagement in non-driving related tasks when allowed. The fluid HMI consisted of several components (head-up display, LEDs, haptic icons, sound, tablet) and featured driver monitoring and adaptive tutoring. Study results did not show significant differences between the HMIs regarding subjective measures such as user experience, usability, acceptance, or safety feeling. The various factors contributing to this conclusion are thoroughly discussed. However, objective measures in terms of eye movements and a safety analysis including driving data showed a significant benefit of the integrated fluid HMI over the baseline HMI. Participants had better mode awareness and a higher safety score with the integrated fluid HMI. Furthermore, valuable insights on how to further improve the HMI could be gained during the study.

For a secure transfer of the web content, we generally use a combination of the HyperText Transfer Protocol (HTTP) for communication with a web server, Transport Layer Security (TLS) for the encryption of the content and Transmission Control Protocol (TCP) for a reliable transfer. Using a combination of the protocols results in a poorer performance than using a single protocol, as each protocol has its own overhead, which is mainly reflected in an increased latency of the transfer. Moreover, the development of the World Wide Web and other Internet applications has created a need for more efficient transport mechanisms, which are difficult to implement in practice due to the ossification of the transport layer. Improvements to transport mechanisms have therefore been implemented as part of application protocols, in particular HTTP. The Quick UDP Internet Connections (QUIC) protocol combines the transport mechanisms implemented in HTTP, TLS and TCP under one roof and thus improves the efficiency of the web content transfer. QUIC is not a completely independent transport protocol, as it uses the User Datagram Protocol (UDP) to transport its messages and thus ensures that it is not rejected by the network devices. The main advantages of QUIC over the combination of the TCP, TLS and HTTP protocols are the faster connection establishment, the solution to the head-of-line blocking problem and its implementation in the user space of operating systems, which enables faster upgrades.

In conditional driving automation (SAE L3), a vehicle may issue a take-over request at any time, and the driver must intervene by resuming control. Normally, the driver would manually resolve a situation that the vehicle could not handle and then switch back to full driving automation mode. In this video, however, we focus only on unsuccessful take-over attempts that resulted in collisions and their causes. An expert driver observed and labeled video recordings of 18 unsuccessful take-over attempts out of 216 take-overs, performed in a driving simulator user study. The unsuccessful take-overs were then categorized into five groups in which the driver: 1) did not brake; 2) took over too late; 3) did not take over at all; 4) was too immersed in the secondary task; and 5) had problems with the user interface. The resulting video shows an example of each type of unsuccessful take-over event.

This paper discusses the concept of take-over (TO) in conditionally automated vehicles. Most of the current studies consider TO as a discrete event that is completed when the driver takes full control of the vehicle. Two problems with this approach are that the driver 1) needs time to gain sufficient situational awareness and 2) sometimes takes over only the lateral or only the longitudinal coordination of the vehicle, neglecting the other. To overcome these two problems and increase the quality (effectiveness, efficiency, and satisfaction) of TO, we propose two new approaches to the take-over process: partial take-overs (PTO) and assisted take-overs (ATO). The proposed PTO approach allows the driver to take over only the lateral or longitudinal coordination of the vehicle separately, instead of assuming a full TO. With ATO, the driver is monitored even after taking control of the vehicle and is assisted with automatic soft braking as well as additional warning and emergency braking if the time to collision falls below the appropriate critical levels. The approaches were evaluated in a user study with 44 participants in a driving simulator. We were able to confirm that the proposed ATO approach significantly improves the TO quality in terms of both effectiveness and efficiency without compromising driver satisfaction. Contrary to our expectations, the PTO approach did not have a significant effect on the effectiveness of TO, but only provided significantly lower reaction time to first braking and longer time to lane crossing. When combined, ATO and PTO were at least as useful as either approach individually and should be considered in future TOR user interfaces.

In conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet to be determined and are the motivation for our work. To determine these parameters, we used a dataset of 41 driving and non-driving parameters from a previous user study with 216 TOs while performing a non-driving-related task on a handheld device in a driving simulator. Eight take-over quality aspects, grouped into pre-TO predictors (attention), during-TO predictors (reaction time, solution suitability), and safety performance (off road drive, braking, lateral acceleration, time to collision, success), were modeled using multiple linear regression, support vector machines, M5', 1R, logistic regression, and J48. We interpreted the best-suited models by highlighting the most influential parameters that affect the overall quality of a TO. The results show that these are primarily maximal acceleration (88.6% accurate prediction of collisions) and the TOR-to-first-brake interval. Gradual braking, neither too hard nor too soft, as fast as possible seems to be the strategy that maximizes the overall TO quality. The position of the handheld device and the way it was held prior to TO did not affect TO quality. However, handling the device during TO did affect driver attention when shorter attention times were observed and drivers held their mobile phones in only one hand. In the future, automatic gradual braking maneuvers could be considered instead of immediate full TOs.

Before the introduction of fully autonomous vehicles with all their benefits and positive impact on quality of life (e.g., increased mobility options, reduced carbon footprint, road safety), researchers propose an era of conditionally automated vehicles where the driver must take over (resume control of the automated vehicle) in critical situations. In terms of human-computer interaction (HCI) during the take-over process, the driver's physiological signals seem promising as they could be read and understood by the vehicle. In this paper, we quantify the physiological responses to take-over requests (TOR), i.e., we determine their amplitudes, delays, and durations. We measured and examined drivers' heart rate, pupil diameter, horizontal gaze dispersion, blink rate, skin conductance response, and skin temperature. Values before the TOR were compared with values after the TOR, averaged over different time intervals. In addition, the duration until the first noticeable change in each physiological response (delay) and the duration until the signals stabilized to their normal values (duration) were measured. The results showed that the relatively greatest effect of TOR was observed in skin conductance (from -62% to 142%). The fastest response (on average) to TOR was observed in pupil diameter (2.24 s ± 2.48 s), followed by skin conductance and heart rate. Manual or automatic artifact correction has not yet been performed and should be included in further analysis.

The rapid development of driving simulators for the evaluation of automated driving experience is constrained by the simulator sickness-related nausea. The electrogastrogram (EGG)-based approach may be promising for immediate, objective, and quantitative nausea assessment. Given the relatively high EGG sensitivity to noises associated with the relatively low amplitude and frequency spans, we introduce an automated procedure comprising statistical analysis and machine learning techniques for EGG-based nausea detection in relation to the noise contamination during automated driving simulation. We calculate the root mean square of EGG amplitude, median and dominant frequencies, magnitude of Power Spectral Density (PSD) at dominant frequency, crest factor of PSD, and spectral variation distribution along with newly introduced parameters: sample and spectral entropy, autocorrelation zero-crossing, and parameters derived from the Poincaré diagram of consecutive EGG samples. Results showed outstanding robustness of sample entropy with moderate robustness of autocorrelation zero-crossing, dominant frequency, and its median. Machine learning reached an accuracy of 88.2% and revealed sample entropy as one of the most relevant and robust parameters, while linear analysis highlighted spectral entropy, spectral variation distribution, and crest factor of PSD. This study clearly indicates the need for customized feature selection in noisy environments, as well as a complementary approach comprising machine learning and statistical analysis for efficient nausea detection.

Monitoring the environment is an essential part of driving, as it increases the driver's situational awareness and enables them to make appropriate decisions for safe and comfortable driving. This paper presents a study to investigate how much and in which way should information be displayed to the driver in a semi-automatic vehicle with a head-up display (HUD) to achieve optimal situational awareness. This was evaluated from two perspectives: the user's experience and perceived usability. It additionally explored the users preferences on which information should be displayed in such HUDs. For this purpose, four prototypes of a visual HUD were created, displaying different amounts of information (MIN vs. MAX) and presented in two different modes - as a two-dimensional (2D) projection on the windshield and using augmented reality (AR) to highlight the information directly in the environment. The obtained results gave a clear indication that the test participants preferred to have more information displayed on a HUD, regardless of whether it was presented in 2D or AR.

Deficits in attentional and executive functioning may interfere with driving ability and result in a lower level of fitness to drive. Studies show mixed results in relation to the consistency of neuropsychological and driving simulator assessment. The objective of this study was to investigate the consistency of both types of assessment. Ninety-nine patients with various neurological impairments (72 males; M = 48.98 years; SD = 17.27) performed a 30-minute drive in a driving simulation in three different road settings; a (non-)residential rural area, a highway and an urban area. They also underwent neuropsychological assessment of attention and executive function. An exploratory correlational analysis was conducted. We found weak, but significant correlations between attention and executive function measures and more efficient driving in the driving simulator. Distractibility was associated with the most simulator variables in all three simulated road settings. Participants who were better at maintaining attention, eliminating irrelevant information and suppressing inappropriate responses, were less likely to drive above the speed limit, produced a less jerky ride, and used the rearview mirror more regularly. A lack of moderate or strong significant correlations (inconsistency) between traditional neuropsychological and simulator assessment variables may indicate that they don't evaluate the same cognitive processes.

General introduction of unconditionally and conditionally automated vehicles is expected to have a highly positive impact on the society, from increased accessibility to mobility and road traffic safety, to decreased environmental and economic negative impacts. However, there are several obstacles and risks slowing down the adoption of this technology, which are primarily related to the human-machine interaction (HMI) and exchange of control between the vehicle and the human driver. In this article, we present key takeaways for HMI design of take-over requests (TOR) that the vehicle issues to inform the driver to take over control of the vehicle. The key takeaways were developed based on the results of a user study, where directional tactile-ambient (visual) and auditory-ambient (visual) TOR user interfaces (UI) were evaluated with regards to commonly used take-over quality aspects (attention redirection, take-over time, correct interpretation of stimuli, off-road drive, brake application, lateral acceleration, minimal time-to-collision and occurrence of collision). 36 participants took part in the mixed design study, which was conducted in a driving simulator. The results showed that drivers' attention was statistically significantly faster redirected with the auditory-ambient UI, however using the tactile-ambient UI resulted in less off-road driving and slightly less collisions. The results also revealed that drivers correctly interpreted the directional TOR stimuli more often than the non-directional one. Based on the study results, a list of key takeaways was developed and is presented in the conclusion of the paper. The results from this study are especially relevant to the TOR UI designers and the automotive industry, which tend to provide the most usable UI for ensuring safer end efficient human-vehicle interaction during the TOR task.

In this paper, we explore a novel three-stage take-over (TO) concept for conditionally autonomous vehicles (SAE L3). In a user study in a driving simulator, we evaluated the usability of four different TO concepts: a) a basic concept including only a single TO request, b) a partial take-over (PTO) concept, where the longitudinal coordination of the vehicle remains automatic, when a TO is performed by steering only, c) a concept with an improved minimum safety manoeuvre (MSM) with additional warnings and automatic braking manoeuvres, and d) a TO concept including both PTO and MSM. The results showed that when PTO and MSM were both used, the minimal time-to-collision (TTC) was longer on average, and the maximal lateral acceleration was lower on average compared to the basic or PTO concepts. In addition, significantly fewer collisions were observed when using PTO+MSM compared to the basic TO concept.

Autonomous vehicles are expected to take complete control of the driving process, enabling the former drivers to act as passengers only. This could lead to increased sickness as they can be engaged in tasks other than driving. Adopting different sickness mitigation techniques gives us unique types of motion sickness in autonomous vehicles to be studied. In this paper, we report on a study where we explored the possibilities of assessing motion sickness with electrogastrography (EGG), a non-invasive method used to measure the myoelectric activity of the stomach, and its potential usage in autonomous vehicles (AVs). The study was conducted in a high-fidelity driving simulator with a virtual reality (VR) headset. There separate EGG measurements were performed: before, during and after the driving AV simulation video in VR. During the driving, the participants encountered two driving environments: a straight and less dynamic highway road and a highly dynamic and curvy countryside road. The EGG signal was recorded with a proprietary 3-channel recording device and Ag/AgCl cutaneous electrodes. In addition, participants were asked to signalize whenever they felt uncomfortable and nauseated by pressing a special button. After the drive they completed also the Simulator Sickness Questionnaire (SSQ) and reported on their overall subjective perception of sickness symptoms. The EGG results showed a significant increase of the dominant frequency (DF) and the percentage of the high power spectrum density (FSD) as well as a significant decrease of the power spectrum density Crest factor (CF) during the AV simulation. The vast majority of participants reported nausea during more dynamic conditions, accompanied by an increase in the amplitude and the RMS value of EGG. Reported nausea occurred simultaneously with the increase in EGG amplitude. Based on the results, we conclude that EGG could be used for assessment of motion sickness in autonomous vehicles. DF, CF and FSD can be used as overall sickness indicators, while the relative increase in amplitude of EGG signal and duration of that increase can be used as short-term sickness indicators where the driving environment may affect the driver.

The aim of the present study was to investigate the role of driving demands, neuroticism, and their interaction when predicting driving behavior. More precisely, we strived to examine how driving behaviors (i.e., speeding, winding, tailgating and jerky driving) unfold across low and high driving demands and whether they are contingent on a personality factor that has previously been linked to stress reactivity. In a driving simulator, 50 participants with a valid driver's license (56.6% female, age: M = 30.13, SD = 10.16) were exposed to driving scenarios of different levels of information processing and vehicle handling demands. Additionally, they filled-out a self-report questionnaire that measured their neuroticism. We found that driving behavior became safer in scenarios that were highly demanding in terms of information processing, while this pattern did not emerge with vehicle handling demands. Moreover, tentative support was found for the notion that individuals high in neuroticism are less able to adapt their behavior to higher information processing demands. The present study offers new insights on driving demands in a simulated driving context and points to the potential importance of exploring interactions between personality and situational factors when understanding driving behavior. Additionally, the results of the present study may be used to adapt driver's education programs. (C) 2020 Elsevier Ltd. All rights reserved.

The Intervention.net system is intended for the collection and dissemination of information on accidents to emergency services. For a long time, there has been a need to automatically collect and provide this kind of information and to inform the members of the rescue teams more effectively. The paper presents the Intervention.net system upgraded with a new communication channel based on the Internet network and application protocol for a reliable exchange of critical information between autonomous devices and the core part of the network. The Intervention.net Protocol (IntP) is described by its individual specification elements. Currently, IntP is being tested in a real-world environment with the focus on firefighting brigades. 50 autonomous clients and two servers have been communicating using the presented protocol for over 250,000 hours without detecting any operational anomalies.

The ability to measure drivers’ physiological responses is important for understanding their state and behavior under different driving conditions. Such measurements can be used in the development of novel user interfaces, driver profiling, advanced driver assistance systems, etc. In this paper, we present a user study in which we performed an evaluation of two commercially available wearable devices for assessment of drivers’ physiological signals. Empatica’s E4 wristband measures blood volume pulse (BVP), inter-beat interval (IBI), galvanic skin response (GSR), temperature, and acceleration. Bittium’s Faros 360 is an electrocardiographic (ECG) device that can record up to 3-channel ECG signals. The aim of this study was to explore the use of such devices in a dynamic driving environment and their ability to differentiate between different levels of driving demand. Twenty-two participants (eight female, 14 male) aged between 18 and 45 years old participated in the study. The experiment compared three phases: Baseline (no driving), easy driving scenario, and demanding driving scenario. Mean and median heart rate variability (HRV), standard deviation of R–R intervals (SDNN), HRV variables for shorter time frames (standard deviation of the average R–R intervals over a shorter period—SDANN and mean value of the standard deviations calculated over a shorter period—SDNN index), HRV variables based on successive differences (root mean square of successive differences—RMSSD and percentage of successive differences, greater than 50 ms—pNN50), skin temperature, and GSR were observed in each phase. The results showed that motion artefacts due to driving affect the GSR recordings, which may limit the use of wrist-based wearable devices in a driving environment. In this case, due to the limitations of the photoplethysmography (PPG) sensor, E4 only showed differences between non-driving and driving phases but could not differentiate between different levels of driving demand. On the other hand, the results obtained from the ECG signals from Faros 360 showed statistically significant differences also between the two levels of driving demand.