An official website of the United States government
Peripheral vision is fundamental for many real-world tasks, including walking, driving, and aviation. Nonetheless, there has been no effort to connect these applied literatures to research in peripheral vision in basic vision science or sports science. To close this gap, we analyzed 60 relevant papers, chosen according to objective criteria. Applied research, with its real-world time constraints, complex stimuli, and performance measures, reveals new functions of peripheral vision. Peripheral vision is used to monitor the environment (e.g., road edges, traffic signs, or malfunctioning lights), in ways that differ from basic research. Applied research uncovers new actions that one can perform solely with peripheral vision (e.g., steering a car, climbing stairs). An important use of peripheral vision is that it helps compare the position of one’s body/vehicle to objects in the world. In addition, many real-world tasks require multitasking, and the fact that peripheral vision provides degraded but useful information means that tradeoffs are common in deciding whether to use peripheral vision or move one’s eyes. These tradeoffs are strongly influenced by factors like expertise, age, distraction, emotional state, task importance, and what the observer already knows. These tradeoffs make it hard to infer from eye movements alone what information is gathered from peripheral vision and what tasks we can do without it. Finally, we recommend three ways in which basic, sport, and applied science can benefit each other’s methodology, furthering our understanding of peripheral vision more generally.
more »
« less
Distraction “Hangover”: Characterization of the Delayed Return to Baseline Driving Risk After Distracting Behaviors
Objective We measured how long distraction by a smartphone affects simulated driving behaviors after the tasks are completed (i.e., the distraction hangover). Background Most drivers know that smartphones distract. Trying to limit distraction, drivers can use hands-free devices, where they only briefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating and back to driving adds an underappreciated, potentially long period to the total distraction time. Method Ninety-seven 21- to 78-year-old individuals who self-identified as active drivers and smartphone users engaged in a simulated driving scenario that included smartphone distractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eye tracking. Results The participants’ lateral speed was larger than baseline for 15 s after the end of a voice distraction and for up to 25 s after a text distraction. Correct identification of peripheral cues dropped about 5% per decade of age, and participants from the 71+ age group missed seeing about 50% of peripheral cues within 4 s of the distraction. During distraction, coherence with the lead car in a following task dropped from 0.54 to 0.045, and seven participants rear-ended the lead car. Breadth of scanning contracted by 50% after distraction. Conclusion Simulated driving performance drops dramatically after smartphone distraction for all ages and for both voice and texting. Application Public education should include the dangers of any smartphone use during driving, including hands-free.
more »
« less
- Award ID(s):
- 1640909
- PAR ID:
- 10290368
- Date Published:
- Journal Name:
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- ISSN:
- 0018-7208
- Page Range / eLocation ID:
- 001872082110122
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Although driving is a complex and multitask activity, it is not unusual for drivers to engage simultaneously in other non-driving related tasks using secondary in-vehicle displays (IVIS). The use of IVIS and its potential negative safety consequences has been investigated over the years. However with the advent and advance of in-vehicle technologies such as augmented-reality head-up displays (AR HUDs), there are increasing opportunities for improving secondary task engagement and decreasing negative safety consequences. In this study, we aim to understand the effects of AR HUD low cognitive load tasks on driving performance during monotonous driving. Adapting NHTSA’s driver distraction guidelines, we conducted a user-study with twenty-four gender-balanced participants that performed secondary AR HUD tasks of different durations while driving in a monotonous environment using a medium-fidelity driving simulator. We performed a mixed-methods analysis to evaluate driver’s perceived workload (NASA-TLX), lateral, and longitudinal driving performance. Although we found that drivers subjectively perceive AR HUD tasks to have a higher cognitive demand; AR tasks resulted in improved driving performance. Conversely, the duration of the secondary tasks had no measurable impacts on performance which suggests that the amount of time spent on tasks has no negative or positive implications on driving performance. We provide evidence that there are potential benefits of secondary AR task engagement; in fact, there are situations in which AR HUDs can improve driver’s alertness and vigilance.more » « less
-
null (Ed.)Objective: The objective of this study was to explore how age and sex impact the ability to respond to an emergency when in a self-driving vehicle. Methods: For this study, 60 drivers (male: 48%, female: 52%) of different age groups (teens: aged 16–19, 32%, adults: aged 35–54, 37%, seniors: aged 65þ, 32%) were recruited to share their perspectives on self-driving technology. They were invited to ride in a driving simulator that mimicked a vehicle in autopilot mode (longitudinal and lateral control). Results: In a scenario where the automated vehicle unexpectedly drives toward a closed highway exit, 21% of drivers did not react at all. For this event, where drivers had 6.2 s to avoid a crash, 40% of drivers crashed. Adults aged 35–54 crashed less than other age groups (33% crash rate), whereas teens crashed more (47% crash rate). Seniors had the highest crash rate (50% crash rate). Males (38% crash rate) crashed less than females (43% crash rate). All participants with a reaction time less than 4 s were able to avoid the crash. Conclusions: The results from the simulation drives show that humans lose focus when they do not actively drive so that their response in an emergency does not allow them to reclaim control quickly enough to avoid a crash.more » « less
-
ObjectiveThis study examined the impact of monitoring instructions when using an automated driving system (ADS) and road obstructions on post take-over performance in near-miss scenarios. BackgroundPast research indicates partial ADS reduces the driver’s situation awareness and degrades post take-over performance. Connected vehicle technology may alert drivers to impending hazards in time to safely avoid near-miss events. MethodForty-eight licensed drivers using ADS were randomly assigned to either the active driving or passive driving condition. Participants navigated eight scenarios with or without a visual obstruction in a distributed driving simulator. The experimenter drove the other simulated vehicle to manually cause near-miss events. Participants’ mean longitudinal velocity, standard deviation of longitudinal velocity, and mean longitudinal acceleration were measured. ResultsParticipants in passive ADS group showed greater, and more variable, deceleration rates than those in the active ADS group. Despite a reliable audiovisual warning, participants failed to slow down in the red-light running scenario when the conflict vehicle was occluded. Participant’s trust in the automated driving system did not vary between the beginning and end of the experiment. ConclusionDrivers interacting with ADS in a passive manner may continue to show increased and more variable deceleration rates in near-miss scenarios even with reliable connected vehicle technology. Future research may focus on interactive effects of automated and connected driving technologies on drivers’ ability to anticipate and safely navigate near-miss scenarios. ApplicationDesigners of automated and connected vehicle technologies may consider different timing and types of cues to inform the drivers of imminent hazard in high-risk scenarios for near-miss events.more » « less
-
null (Ed.)People interacting with voice assistants are often frustrated by voice assistants' frequent errors and inability to respond to backchannel cues. We introduce an open-source video dataset of 21 participants' interactions with a voice assistant, and explore the possibility of using this dataset to enable automatic error recognition to inform self-repair. The dataset includes clipped and labeled videos of participants' faces during free-form interactions with the voice assistant from the smart speaker's perspective. To validate our dataset, we emulated a machine learning classifier by asking crowdsourced workers to recognize voice assistant errors from watching soundless video clips of participants' reactions. We found trends suggesting it is possible to determine the voice assistant's performance from a participant's facial reaction alone. This work posits elicited datasets of interactive responses as a key step towards improving error recognition for repair for voice assistants in a wide variety of applications.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/00187208211012218
