A5032421589- Human-Automation Interaction and Safety
- Safety Warnings and Signage
- Traffic and Road Safety
- Healthcare Technology and Patient Monitoring
- EEG and Brain-Computer Interfaces
- Gaze Tracking and Assistive Technology
- Older Adults Driving Studies
- Technology Use by Older Adults
- Transportation and Mobility Innovations
- Autonomous Vehicle Technology and Safety
- Mind wandering and attention
- Neural and Behavioral Psychology Studies
- Target Tracking and Data Fusion in Sensor Networks
- Intergenerational Family Dynamics and Caregiving
- Sleep and Work-Related Fatigue
- Aging and Gerontology Research
- Heart Rate Variability and Autonomic Control
- Sleep and Wakefulness Research
- Occupational Health and Safety Research
Texas Tech University
2024-2025
Purdue University West Lafayette
2019-2024
Maintaining situation awareness (SA) is essential for drivers to deal with the situations that Society of Automotive Engineers (SAE) Level 3 automated vehicle systems are not designed handle. Although advanced physiological sensors can enable continuous SA assessments, previous single-modality approaches may be sufficient capture SA. To address this limitation, current study demonstrates a multimodal sensing approach objective monitoring. Physiological sensor data from electroencephalogram...
This study developed a fixation-related electroencephalography band power (FRBP) approach for situation awareness (SA) assessment in automated driving.
Significant growth in the number of autonomous vehicles is expected coming years. With this technology, drivers will likely begin to disengage from driving task and often experience mind wandering. Research has examined effects wandering on manual performance, but little work been done understand its impact driving. In addition, it unclear what physiological measurements can reveal about context. Therefore, goals paper were (a) how affects warning signal detection, semi-autonomous responses,...
This work investigates driver takeover times in non-urgent, low consequence scenarios within conditionally automated driving. Using physiological and behavioral data from 46 participants a driving simulator, classification algorithms were applied to predict metrics of time following request (TOR). Eye-tracking, heart rate variability, computer-vision based body posture features analyzed for their predictive power. The Naïve Bayes algorithm outperformed other models, achieving an accuracy 78%...
Conditionally automated vehicles require drivers to take over control occasionally. To date, takeover performance has been mostly evaluated using only re-engagement time and quality metrics. However, the appropriateness of decisions, which not considered by previous research, should also be included as a indicator it reflects one’s situation awareness scenario. The goal this study was use eye-tracking, demographic factors, workload, non-driving-related task (NDRT) conditions predict...
Vehicle automation is developing at a rapid rate worldwide. However, even lower levels of automation, such as SAE Level-1, are expected to reduce drivers’ workload by controlling either speed or lane position. At the same time, however, engagement in secondary tasks may make up for this difference displaced automation. Previous research has investigated effects adaptive cruise control on driving performance and workload, but little attention been devoted keeping systems (LKS). In addition,...