Human Factors最新文献

{"title":"A Vehicle Simulation Study Examining the Effects of System Interface Design Elements on Performance in Different Vibration Environments Below 3 Hz.","authors":"Xing Tang, Suihuai Yu, Birsen Donmez, Jianjie Chu, Hao Fan, Feilong Li, Gang Jiang","doi":"10.1177/00187208231213470","DOIUrl":"10.1177/00187208231213470","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to explore the relationship between system interface elements' design features and interaction performance in simulated vehicle vibration environments.</p><p><strong>Background: </strong>Touch screens have been widely used in vehicle information systems, but few studies have focused on the decline of touchscreen interaction performance and task load increase when driving on unpaved roads.</p><p><strong>Method: </strong>The interaction performance (reaction time and task accuracy rate) with vibration frequencies below 3 Hz (1.5, 2.0, and 2.5 Hz) and different interface design elements was investigated employing a touch screen computer and E-prime software.</p><p><strong>Results: </strong>The results indicate that vehicle vibration (below 3 Hz) can significantly reduce interaction performance with a vehicle information system interface.</p><p><strong>Conclusion: </strong>An appropriate increase in the physical size of the interface design features (visual stimulus materials and touch buttons) can help to mitigate this negative effect of vibration.</p><p><strong>Application: </strong>The results and findings of this study can be utilized for the design of information system interfaces as it relates to the vibration scenario of unpaved roads.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2345-2365"},"PeriodicalIF":2.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136400550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Integrated Electroencephalography and Eye-Tracking Analysis Using eXtreme Gradient Boosting for Mental Workload Evaluation in Surgery.","authors":"Somayeh B Shafiei, Saeed Shadpour, James L Mohler","doi":"10.1177/00187208241285513","DOIUrl":"10.1177/00187208241285513","url":null,"abstract":"<p><strong>Objective: </strong>We aimed to develop advanced machine learning models using electroencephalogram (EEG) and eye-tracking data to predict the mental workload associated with engaging in various surgical tasks.</p><p><strong>Background: </strong>Traditional methods of evaluating mental workload often involve self-report scales, which are subject to individual biases. Due to the multidimensional nature of mental workload, there is a pressing need to identify factors that contribute to mental workload across different surgical tasks.</p><p><strong>Method: </strong>EEG and eye-tracking data from 26 participants performing Matchboard and Ring Walk tasks from the da Vinci simulator and the pattern cut and suturing tasks from the Fundamentals of Laparoscopic Surgery (FLS) program were used to develop an eXtreme Gradient Boosting (XGBoost) model for mental workload evaluation.</p><p><strong>Results: </strong>The developed XGBoost models demonstrated strong predictive performance with <i>R</i>² values of 0.82, 0.81, 0.82, and 0.83 for the Matchboard, Ring Walk, pattern cut, and suturing tasks, respectively. Key features for predicting mental workload included task average pupil diameter, complexity level, average functional connectivity strength at the temporal lobe, and the total trajectory length of the nondominant eye's pupil. Integrating features from both EEG and eye-tracking data significantly enhanced the performance of mental workload evaluation models, as evidenced by repeated-measures t-tests yielding <i>p</i>-values less than 0.05. However, this enhancement was not observed in the Pattern Cut task (repeated-measures t-tests; <i>p</i> > 0.05).</p><p><strong>Conclusion: </strong>The findings underscore the potential for machine learning and multidimensional feature integration to predict mental workload and thereby improve task design and surgical training.</p><p><strong>Application: </strong>The advanced mental workload prediction models could serve as instrumental tools to enhance our understanding of surgeons' cognitive demands and significantly improve the effectiveness of surgical training programs.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"187208241285513"},"PeriodicalIF":2.9,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11936844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vigilance Decrement During On-Road Partially Automated Driving Across Four Systems.","authors":"Francesco N Biondi, Amy S McDonnell, Mobina Mahmoodzadeh, Noor Jajo, Balakumar Balasingam, David L Strayer","doi":"10.1177/00187208231189658","DOIUrl":"10.1177/00187208231189658","url":null,"abstract":"<p><strong>Objective: </strong>This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems.</p><p><strong>Background: </strong>Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven.</p><p><strong>Method: </strong>Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods.</p><p><strong>Results: </strong>Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found.</p><p><strong>Conclusion: </strong>Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo.</p><p><strong>Application: </strong>The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2179-2190"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10235149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preface to the Special Issue on Assessment and Effectiveness of Driver Monitoring Systems.","authors":"Francesco N Biondi, William J Horrey, Birsen Donmez","doi":"10.1177/00187208231206073","DOIUrl":"10.1177/00187208231206073","url":null,"abstract":"<p><p>With vehicle automation becoming more commonplace, the role of the human driver is shifting from that of system operator to that of system supervisor. With this shift comes the risk of drivers becoming more disengaged from the task of supervising the system functioning, thus increasing the need for technology to keep drivers alert. This special issue includes the most up-to-date research on how drivers use vehicle automation, and the safety risks it may pose. It also investigates the accuracy that driver monitoring systems have in detecting conditions like driver distraction and drowsiness, and explores ways future drivers may respond to the broader introduction of this technology on passenger vehicles.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2161-2165"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89720902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Do Drivers Perceive Risks During Automated Driving Scenarios? An fNIRS Neuroimaging Study.","authors":"Jaume Perello-March, Christopher G Burns, Roger Woodman, Stewart Birrell, Mark T Elliott","doi":"10.1177/00187208231185705","DOIUrl":"10.1177/00187208231185705","url":null,"abstract":"<p><strong>Objective: </strong>Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving.</p><p><strong>Background: </strong>Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers' states posits it could become a novel method for measuring risk perception.</p><p><strong>Methods: </strong>Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports.</p><p><strong>Results: </strong>Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario.</p><p><strong>Conclusion: </strong>This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario.</p><p><strong>Application: </strong>Little is known regarding the effects of drivers' risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2244-2263"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9677120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploratory Development of Algorithms for Determining Driver Attention Status.","authors":"Eileen Herbers, Marty Miller, Luke Neurauter, Jacob Walters, Daniel Glaser","doi":"10.1177/00187208231198932","DOIUrl":"10.1177/00187208231198932","url":null,"abstract":"<p><strong>Objective: </strong>Varying driver distraction algorithms were developed using vehicle kinematics and driver gaze data obtained from a camera-based driver monitoring system (DMS).</p><p><strong>Background: </strong>Distracted driving characteristics can be difficult to accurately detect due to wide variation in driver behavior across driving environments. The growing availability of information about drivers and their involvement in the driving task increases the opportunity for accurately recognizing attention state.</p><p><strong>Method: </strong>A baseline for driver distraction levels was developed using a video feed of 24 separate drivers in varying naturalistic driving conditions. This initial assessment was used to develop four buffer-based algorithms that aimed to determine a driver's real-time attentiveness, via a variety of metrics and combinations thereof.</p><p><strong>Results: </strong>Of those tested, the optimal algorithm included ungrouped glance locations and speed. Notably, as an algorithm's performance of detecting very distracted drivers improved, its accuracy for correctly identifying attentive drivers decreased.</p><p><strong>Conclusion: </strong>At a minimum, drivers' gaze position and vehicle speed should be included when designing driver distraction algorithms to delineate between glance patterns observed at high and low speeds. Distraction algorithms should be designed with an understanding of their limitations, including instances in which they may fail to detect distracted drivers, or falsely notify attentive drivers.</p><p><strong>Application: </strong>This research adds to the body of knowledge related to driver distraction and contributes to available methods to potentially address and reduce occurrences. Machine learning algorithms can build on the data elements discussed to increase distraction detection accuracy using robust artificial intelligence.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2191-2204"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41152711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to the Preface for the Special Section on Driver Monitoring Systems.","authors":"","doi":"10.1177/00187208241274639","DOIUrl":"10.1177/00187208241274639","url":null,"abstract":"","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2264"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11451305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141898998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driving Aggressively or Conservatively? Investigating the Effects of Automated Vehicle Interaction Type and Road Event on Drivers' Trust and Preferred Driving Style.","authors":"Yuni Lee, Miaomiao Dong, Vidya Krishnamoorthy, Kumar Akash, Teruhisa Misu, Zhaobo Zheng, Gaojian Huang","doi":"10.1177/00187208231181199","DOIUrl":"10.1177/00187208231181199","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to investigate the impact of automated vehicle (AV) interaction mode on drivers' trust and preferred driving styles in response to pedestrian- and traffic-related road events.</p><p><strong>Background: </strong>The rising popularity of AVs highlights the need for a deeper understanding of the factors that influence trust in AV. Trust is a crucial element, particularly because current AVs are only partially automated and may require manual takeover; miscalibrated trust could have an adverse effect on safe driver-vehicle interaction. However, before attempting to calibrate trust, it is vital to comprehend the factors that contribute to trust in automation.</p><p><strong>Methods: </strong>Thirty-six individuals participated in the experiment. Driving scenarios incorporated adaptive SAE Level 2 AV algorithms, driven by participants' event-based trust in AVs and preferences for AV driving styles. The study measured participants' trust, preferences, and the number of takeover behaviors.</p><p><strong>Results: </strong>Higher levels of trust and preference for more aggressive AV driving styles were found in response to pedestrian-related events compared to traffic-related events. Furthermore, drivers preferred the trust-based adaptive mode and had fewer takeover behaviors than the preference-based adaptive and fixed modes. Lastly, participants with higher trust in AVs favored more aggressive driving styles and made fewer takeover attempts.</p><p><strong>Conclusion: </strong>Adaptive AV interaction modes that depend on real-time event-based trust and event types may represent a promising approach to human-automation interaction in vehicles.</p><p><strong>Application: </strong>Findings from this study can support future driver- and situation-aware AVs that can adapt their behavior for improved driver-vehicle interaction.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2166-2178"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9591470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"European NCAP Driver State Monitoring Protocols: Prevalence of Distraction in Naturalistic Driving.","authors":"Megan Mulhall, Kyle Wilson, Shiyan Yang, Jonny Kuo, Tracey Sletten, Clare Anderson, Mark E Howard, Shantha Rajaratnam, Michelle Magee, Allison Collins, Michael G Lenné","doi":"10.1177/00187208231194543","DOIUrl":"10.1177/00187208231194543","url":null,"abstract":"<p><strong>Objective: </strong>examine the prevalence of driver distraction in naturalistic driving when implementing European New Car Assessment Program (Euro NCAP)-defined distraction behaviours.</p><p><strong>Background: </strong>The 2023 introduction of Occupant Status monitoring (OSM) into Euro NCAP will accelerate uptake of Driver State Monitoring (DSM). Euro NCAP outlines distraction behaviours that DSM must detect to earn maximum safety points. Distraction behaviour prevalence and driver alerting and intervention frequency have yet to be examined in naturalistic driving.</p><p><strong>Method: </strong>Twenty healthcare workers were provided with an instrumented vehicle for approximately two weeks. Data were continuously monitored with automotive grade DSM during daily work commutes, resulting in 168.8 hours of driver head, eye and gaze tracking.</p><p><strong>Results: </strong>Single long distraction events were the most prevalent, with .89 events/hour. Implementing different thresholds for driving-related and driving-unrelated glance regions impacts alerting rates. Lizard glances (primarily gaze movement) occurred more frequently than owl glances (primarily head movement). Visual time-sharing events occurred at a rate of .21 events/hour.</p><p><strong>Conclusion: </strong>Euro NCAP-described driver distraction occurs naturalistically. Lizard glances, requiring gaze tracking, occurred in high frequency relative to owl glances, which only require head tracking, indicating that less sophisticated DSM will miss a substantial amount of distraction events.</p><p><strong>Application: </strong>This work informs OEMs, DSM manufacturers and regulators of the expected alerting rate of Euro NCAP defined distraction behaviours. Alerting rates will vary with protocol implementation, technology capability, and HMI strategies adopted by the OEMs, in turn impacting safety outcomes, user experience and acceptance of DSM technology.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2205-2217"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10088980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drowsiness Mitigation Through Driver State Monitoring Systems: A Scoping Review.","authors":"Suzan Ayas, Birsen Donmez, Xing Tang","doi":"10.1177/00187208231208523","DOIUrl":"10.1177/00187208231208523","url":null,"abstract":"<p><strong>Objective: </strong>To explore the scope of available research and to identify research gaps on in-vehicle interventions for drowsiness that utilize driver monitoring systems (DMS).</p><p><strong>Background: </strong>DMS are gaining popularity as a countermeasure against drowsiness. However, how these systems can be best utilized to guide driver attention is unclear.</p><p><strong>Methods: </strong>A scoping review was conducted in adherence to PRISMA guidelines. Five electronic databases (ACM Digital Library, Scopus, IEEE Xplore, TRID, and SAE Mobilus) were systematically searched in April 2022. Original studies examining in-vehicle drowsiness interventions that use DMS in a driving context (e.g., driving simulator and driver interviews) passed the screening. Data on study details, state detection methods, and interventions were extracted.</p><p><strong>Results: </strong>Twenty studies qualified for inclusion. Majority of interventions involved warnings (<i>n</i> = 16) with an auditory component (<i>n</i> = 14). Feedback displays (<i>n</i> = 4) and automation takeover (<i>n</i> = 4) were also investigated. Multistage interventions (<i>n</i> = 12) first cautioned the driver, then urged them to take an action, or initiated an automation takeover. Overall, interventions had a positive impact on sleepiness levels, driving performance, and user evaluations. Whether interventions effective for one type of sleepiness (e.g., passive vs. active fatigue) will perform well for another type is unclear.</p><p><strong>Conclusion: </strong>Literature mainly focused on developing sensors and improving the accuracy of DMS, but not on the driver interactions with these technologies. More intervention studies are needed in general and for investigating their long-term effects.</p><p><strong>Application: </strong>We list gaps and limitations in the DMS literature to guide researchers and practitioners in designing and evaluating effective safety systems for drowsy driving.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2218-2243"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}