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Total results: 87

Vehicle Braking Performance and Saccadic Eye Movement with Different Illuminance Transmission Exposures in Digital Driving Simulation

Year: 2023

Authors: A Ahmad,SA Rosli,AH Chen

During driving, the eye moves as we shift the focus of our eye from one point of interest to another point, known as saccadic eye movement. Although the eye movement is not affected under different illuminance conditions during driving, the movement is involved in the ability to drive. This study investigates the correlation between saccadic eye movement and vehicle braking performance when the illuminance transmission was reduced by introducing a neutral density filter in front of the eyes. This is conducted by exposing four levels of illuminance transmission which are 100%, 50%, 30%, and 15% with driving simulation as braking performance is measured. Based on the baseline data from our preceding saccadic investigation on the same subjects using the Dikablis eye tracker, the braking performance is analyzed together with the eye movement data. Twenty-eight young adults with proper license and driving experience, as well as a good history of systemic, ocular, and binocular vision health, are involved in this study. The driving task is conducted via driving simulation, with the subjects instructed to drive naturally. There is no significant correlation between the number of saccadic eye movements and all investigated vehicle braking performances (speed, time, and length) under reduced illuminance transmissions of 30% and 15% (p>0.05). While our previous investigation reveals that the saccadic eye movement is not affected by different illuminance transmissions when driving, this current study concludes that the vehicle braking performance is not correlated with the saccades while driving under those low illuminance exposures.

Eye Tracking Glasses
Simulator

1 version available:

Where to gaze during take-over: eye gaze strategy analysis of different situation awareness and hazard perception levels

Year: 2023

Authors: W Ding, Y Murzello,S Cao

While autonomous vehicles are being developed for the future of surface transportation, drivers today still need to be prepared for takeover. The objective of this study is to understand the optimal gaze strategy during the take-over process. First, an affine transfer method was used to link the eye tracking coordinates and pre-defined Aera-of-Interests (AOIs) locations. Then, independent t-tests were applied to analyze the relevance between the gaze strategy determined by the gaze time percentages on various AOIs and the Situation Awareness (SA) and Hazard Perception (HP) levels. The results showed that drivers with higher SA used different gaze strategies before and after they detected the hazards, while drivers with higher HP kept focusing on the center of the road. Explanations and implications of take-over request design are discussed.

Eye Tracking Glasses
Software

4 versions available

Adaptive Driving Assistant Model (ADAM) for advising drivers of autonomous vehicles

Year: 2022

Authors: SJ Hsieh, AR Wang,A Madison,C Tossell

Fully autonomous driving is on the horizon; vehicles with advanced driver assistance systems (ADAS) such as Tesla's Autopilot are already available to consumers. However, all currently available ADAS applications require a human driver to be alert and ready to take control if needed. Partially automated driving introduces new complexities to human interactions with cars and can even increase collision risk. A better understanding of drivers’ trust in automation may help reduce these complexities. Much of the existing research on trust in ADAS has relied on use of surveys and physiological measures to assess trust and has been conducted using driving simulators. There have been relatively few studies that use telemetry data from real automated vehicles to assess trust in ADAS. In addition, although some ADAS technologies provide alerts when, for example, drivers’ hands are not on the steering wheel, these systems are not personalized to individual drivers. Needed are adaptive technologies that can help drivers of autonomous vehicles avoid crashes based on multiple real-time data streams. In this paper, we propose an architecture for adaptive autonomous driving assistance. Two layers of multiple sensory fusion models are developed to provide appropriate voice reminders to increase driving safety based on predicted driving status. Results suggest that human trust in automation can be quantified and predicted with 80% accuracy based on vehicle data, and that adaptive speech-based advice can be provided to drivers with 90 to 95% accuracy. With more data, these models can be used to evaluate trust in driving assistance tools, which can ultimately lead to safer and appropriate use of these features.

Eye Tracking Glasses
Software

2 versions available

Analyzing the influencing factors and workload variation of takeover behavior in semi-autonomous vehicles

Year: 2022

Authors: H Zhang, Y Zhang, Y Xiao, C Wu

There are many factors that will influence the workload of drivers during autonomous driving. To examine the correlation between different factors and the workload of drivers, the influence of different factors on the workload variations is investigated from subjective and objective viewpoints. Thirty-seven drivers were recruited to participant the semi-autonomous driving experiments, and the drivers were required to complete different NDRTs (Non-Driving-Related Tasks): mistake finding, chatting, texting, and monitoring when the vehicle is in autonomous mode. Then, we introduced collision warning to signal there is risk ahead, and the warning signal was triggered at different TB (Time Budget)s before the risk, at which time the driver had to take over the driving task. During driving, the NASA-TLX-scale data were obtained to analyze the variation of the driver’s subjective workload. The driver’s pupil-diameter data acquired by the eye tracker from 100 s before the TOR (Take-Over Request) to 19 s after the takeover were obtained as well. The sliding time window was set to process the pupil-diameter data, and the 119-s normalized average pupil-diameter data under different NDRTs were fitted and modeled to analyze the variation of the driver’s objective workload. The results show that the total subjective workload score under the influence of different factors is as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s and TB = 3 s have no significant difference; and mistake finding > chatting > texting > monitoring. The results of pupil-diameter data under different factors are as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s > TB = 3 s; and monitoring type (chatting and monitoring) > texting type (mistake finding and texting). The research results can provide a reference for takeover safety prediction modeling based on workload.

Eye Tracking Glasses
Simulator

13 versions available

Does a faster takeover necessarily mean it is better? A study on the influence of urgency and takeover-request lead time on takeover performance and safety

Year: 2022

Authors: H Wu, C Wu, N Lyu, J Li

During conditionally automated driving, drivers are sometimes required to take over control of the vehicle if a so-called takeover request (TOR) is issued. TORs are generally issued due to system limitations. This study investigated the effect of different urgency scenarios and takeover-request lead times (TORlts) on takeover performance and safety. The experiment was conducted in a real vehicle-based driving simulator. Manual driving, 7-second TORlt and 5-second TORlt were each tested. Participants experienced three progressively urgent driving scenarios: one cut-in scenario and two obstacle-avoidance scenarios. The results indicate that the TORlt significantly affected takeover performance and safety. Within a certain range, the longer the TORlt, the safer the takeover. However, while takeover reaction time depended mainly on the length of the TORlt and was not significantly related to other factors, such as workload, greater workloads that were caused by the TORlt were associated with shorter reaction times and decreased safety. This is evidence that the reaction time should not be used as the preferred indicator to evaluate takeover performance and safety. Indicators, such as workload, minimum TTC, feature point distribution position and slope of the obstacle avoidance trajectory, can better measure and evaluate takeover performance and safety. This study can provide data support for takeover safety evaluation of conditionally automated driving.

Eye Tracking Glasses
Simulator

4 versions available

Evaluation of a dynamic blocking concept to mitigate driver distraction: three simulator studies

Year: 2022

Authors: J Leipnitz, A Gross, J Dostert, T Baumgarten

In recent years, the number and complexity of in-vehicle infotainment systems has been steadily increasing. While these systems certainly improve the driving experience, they also increase the risk for driver distraction. International standards and guidelines provide methods of measuring this distraction along with test criteria that help automakers decide whether an interface task is too distracting to be used while driving. Any specific function failing this test should therefore be locked out for use by the driver. This study implemented and tested a dynamic approach to this blocking by algorithmically reacting to driver inputs and the pace of the interaction in order to prevent drivers from having prolonged or too intense sequences of in-vehicle interactions not directly related to driving. Three simulated driving experiments in Germany and the United States were conducted to evaluate this dynamic function blocking concept and also cater for differences in the status quo of either no blocking or static blocking. The experiments consisted of a car following scenario with various secondary interface tasks and always included a baseline condition where no blocking occurred as well as an implementation of the dynamic function blocking. While Experiments 1 and 3 were aimed at collecting and analyzing gaze and driving data from more than 20 participants, Experiment 2 focused on the user experience evaluation of different visual feedback implementations from 13 participants. The user experience as rated by these participants increased throughout the course of all three studies and helped further improve both the concept and feedback design. In the experiments the total glance time towards the road was significantly higher in the dynamic function blocking condition compared to the baseline, already accounting for the increase in total task time inherent to the dynamic condition. Participants developed two strategies of interacting with the dynamic function blocking. They either operated at their normal baseline speed and incurred task blockings or operated slower to avoid the blockings. In the latter strategy, participants chunked their interactions into smaller steps with the present data suggesting that they used the pauses in between chunks to look back onto the road ahead. Theoretical and practical implications of this first evaluation of a dynamic function blocking concept are discussed.

Simulator
Software

6 versions available

Impact of sender and peer-feedback characteristics on performance, cognitive load, and mindful cognitive processing

Year: 2022

Authors: M Berndt,JW Strijbos,F Fischer

Mixed research findings on peer feedback indicate that its efficiency seems to be influenced by its characteristics, its mindful cognitive processing, and the presence of justifications. Further, these aspects seem to positively or negatively affect cognitive load in the recipient. In a 2 × 3 design, we systematically varied types of peer feedback (elaborated specific feedback with/without justifications) on an essay and sender’s competence (high/average/low). We measured cognitive load during the peer-feedback reading and performance tasks and correlated eye tracking data with performance measures to infer mindful cognitive processing. We found an interaction effect of justifications and sender’s competence on text-revision performance. The impact of justifications on text-revision performance was moderated by cognitive load. Mindful cognitive processing seemed to increase when more transitions between text elements occurred, however, more intense mindful cognitive processing did not necessarily lead to better performance but rather served a compensatory purpose to sustain performance.

Eye Tracking Glasses
Simulator

4 versions available

Interaction strategies with advanced driver assistance systems

Year: 2022

Authors: N Neuhuber,P Pretto,B Kubicek

When using advanced driver assistance systems (ADAS) drivers need to calibrate their level of trust and interaction strategy to changes in the driving context and possible consequent reduction of system reliability (e.g. in harsh weather conditions). By investigating and identifying categories of drivers who choose inadequate interaction strategies, it is possible to address unsafe usage with e.g. tutoring lessons tailored to the respective driver category. This paper presents two studies investigating categories of drivers who apply different interaction strategies when using ADAS. Study I was designed as an exploratory field study with 37 participants interacting with a SAE level 2 system. For the exploratory study, it was important to observe and understand the interaction strategies in a driving context which entails the real complexity of the driving task. The experimental set-up of study II (simulator study), however, allowed to clearly interpret the interaction strategies as either calibrated or un-calibrated by varying the situational risk. Participants (N = 33) were driving in a situation where the system was either working reliably (low-risk condition) or in a situation where the system displayed repeatedly errors under harsh weather conditions (high-risk condition). Cluster analyses with the variables trust, monitoring behavior towards the system and usage behavior were performed to analyze potential categories of drivers. Extreme driver categories with interaction strategies indicative for both misuse and disuse were observed in both studies. In study I, drivers were categorized as either highly trusting attentive, moderately trusting attentive, moderately inattentive, inattentive or skeptical. In study II, drivers were categorized as either un-calibrated, calibrated, inconsistent or skeptical. Taken together, results underline the need of tutoring systems that are tailored for different driver categories.

Eye Tracking Glasses
Simulator

5 versions available

Stereoscopic 3D dashboards: An investigation of performance, workload, and gaze behavior during take-overs in semi-autonomous driving

Year: 2022

Authors: F Weidner,W Broll

When operating a conditionally automated vehicle, humans occasionally have to take over control. If the driver is out of the loop, a certain amount of time is necessary to gain situation awareness. This work evaluates the potential of stereoscopic 3D (S3D) dashboards for presenting smart S3D take-over-requests (TORs) to support situation assessment. In a driving simulator study with a 4 × 2 between-within design, we presented 3 smart TORs showing the current traffic situation and a baseline TOR in 2D and S3D to 52 participants doing the n-back task. We further investigate if non-standard locations affect the results. Take-over performance indicates that participants looked at and processed the TORs’ visual information and by that, could perform more safe take-overs. S3D warnings in general, as well as warnings appearing at the participants’ focus of attention and warnings at the instrument cluster, performed best. We conclude that visual warnings, presented on an S3D dashboard, can be a valid option to support take-over while not increasing workload. We further discuss participants’ gaze behavior in the context of visual warnings for automotive user interfaces.

Eye Tracking Glasses
Simulator

8 versions available

The influence of visual-manual distractions on anticipatory driving

Year: 2022

Authors: D He,B Donmez

Objective: The aim of this study is to investigate how anticipatory driving is influenced by distraction. Background: The anticipation of future events in traffic can allow potential gains in recognition and response times. Anticipatory actions (i.e., control actions in preparation for potential traffic changes) have been found to be more prevalent among experienced drivers in simulator studies when driving was the sole task. Despite the prevalence of visual-manual distractions and their negative effects on road safety, their influence on anticipatory driving has not yet been investigated beyond hazard anticipation. Methods: A simulator experiment was conducted with 16 experienced and 16 novice drivers. Half of the participants were provided with a self-paced visual-manual secondary task presented on a dashboard display. Results: More anticipatory actions were observed among experienced drivers; experienced drivers also exhibited more efficient visual scanning behaviors as indicated by higher glance rates toward and percent times looking at cues that facilitate the anticipation of upcoming events. Regardless of experience, those with the secondary task displayed reduced anticipatory actions and paid less attention toward anticipatory cues. However, experienced drivers had lower odds of exhibiting long glances toward the secondary task compared to novices. Further, the inclusion of glance duration on anticipatory cues increased the accuracy of a model predicting anticipatory actions based on on-road glance durations. Conclusion: The results provide additional evidence to existing literature supporting the role of driving experience and distraction engagement in anticipatory driving. Application: These findings can guide the design of in-vehicle systems and guide training programs to support anticipatory driving.

Eye Tracking Glasses
Simulator

12 versions available