Agrawal et al., 2021 - Google Patents
Evaluating the impacts of driver's pre-warning cognitive state on takeover performance under conditional automationAgrawal et al., 2021
- Document ID
- 10121536693680081365
- Author
- Agrawal S
- Peeta S
- Publication year
- Publication venue
- Transportation research part F: traffic psychology and behaviour
External Links
Snippet
To design better fallback procedures and enhance road safety for conditionally automated vehicles (SAE Level 3), it is important to understand the factors that affect driver's takeover performance (ie, driving performance while resuming manual control). This study …
- 230000001149 cognitive 0 title abstract description 80
Classifications
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B19/00—Teaching not covered by other main groups of this subclass
- G09B19/16—Control of vehicles or other craft
- G09B19/167—Control of land vehicles
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
- A61B5/18—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state for vehicle drivers or machine operators
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jarosch et al. | Effects of task-induced fatigue in prolonged conditional automated driving | |
| Wu et al. | Age-related differences in effects of non-driving related tasks on takeover performance in automated driving | |
| Stapel et al. | Automated driving reduces perceived workload, but monitoring causes higher cognitive load than manual driving | |
| Naujoks et al. | Noncritical state transitions during conditionally automated driving on german freeways: Effects of non–driving related tasks on takeover time and takeover quality | |
| Zeeb et al. | Why is steering not the same as braking? The impact of non-driving related tasks on lateral and longitudinal driver interventions during conditionally automated driving | |
| Niezgoda et al. | Towards testing auditory–vocal interfaces and detecting distraction while driving: A comparison of eye-movement measures in the assessment of cognitive workload | |
| Agrawal et al. | Evaluating the impacts of driver’s pre-warning cognitive state on takeover performance under conditional automation | |
| Wörle et al. | Sleep in highly automated driving: Takeover performance after waking up | |
| Tejero Gimeno et al. | On the concept and measurement of driver drowsiness, fatigue and inattention: implications for countermeasures | |
| Agrawal et al. | Evaluating the impacts of situational awareness and mental stress on takeover performance under conditional automation | |
| Wu et al. | Eye movements predict driver reaction time to takeover request in automated driving: A real-vehicle study | |
| Radhakrishnan et al. | Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving | |
| Jarosch et al. | Effects of non-driving related tasks in prolonged conditional automated driving–A Wizard of Oz on-road approach in real traffic environment | |
| Habibifar et al. | Relationship between driving styles and biological behavior of drivers in negative emotional state | |
| Stephenson et al. | Effects of an unexpected and expected event on older adults’ autonomic arousal and eye fixations during autonomous driving | |
| Radhakrishnan et al. | Using pupillometry and gaze-based metrics for understanding drivers’ mental workload during automated driving | |
| Zhang et al. | Electrophysiological frequency domain analysis of driver passive fatigue under automated driving conditions | |
| Zahabi et al. | Effect of advanced driver-assistance system trainings on driver workload, knowledge, and trust | |
| Li et al. | An adaptive time budget adjustment strategy based on a take-over performance model for passive fatigue | |
| Stapel et al. | On-road trust and perceived risk in Level 2 automation | |
| Pan et al. | How does drivers’ trust in vehicle automation affect non-driving-related task engagement, vigilance, and initiative takeover performance after experiencing system failure? | |
| Pawar et al. | A comparative assessment of subjective experience in simulator and on-road driving under normal and time pressure driving conditions | |
| Seet et al. | Objective assessment of trait attentional control predicts driver response to emergency failures of vehicular automation | |
| Orsini et al. | Music as a countermeasure to fatigue: a driving simulator study | |
| Coyne et al. | Assessing the physiological effect of non-driving-related task performance and task modality in conditionally automated driving systems: A systematic review and meta-analysis |