Virtual Reality Headset Training: Can It Be Used to Improve Young Drivers’ Latent Hazard Anticipation and Mitigation Skills

2018 ◽  
Vol 2672 (33) ◽  
pp. 20-30 ◽  
Author(s):  
Ravi Agrawal ◽  
Michael Knodler ◽  
Donald L. Fisher ◽  
Siby Samuel
Keyword(s):  
Virtual Reality ◽  
Training Program ◽  
Driving Simulator ◽  
Motor Vehicle ◽  
Wide Field ◽  
Training Interventions ◽  
Head Mounted Display ◽  
Young Drivers ◽  
Vehicle Data ◽  
Risk Driving

Young drivers are overrepresented in motor-vehicle crashes compared to experienced drivers. Research shows that young drivers are generally clueless, not careless, failing to anticipate and mitigate latent hazards. There are several error-feedback training interventions that emphasize the teaching of latent hazard anticipation skills (e.g., risk awareness and perception training, RAPT) and a few that emphasize both the teaching of hazard anticipation and hazard mitigation skills (e.g., the driver simulation ACCEL). In the current study, a virtual reality, headset-based latent hazard anticipation and mitigation training program (V-RAPT) was developed on a head-mounted display (Oculus Rift). The headset provides the participant with a 100-degree wide field of view of six high-risk driving scenarios, the view changing appropriately as the participant rotates his or her head. Thirty-six young drivers were exposed to one of three training programs—V-RAPT, RAPT, and a placebo—and then evaluated on a driving simulator. Eye movement and vehicle data were collected throughout the simulator evaluation. The drives included the six scenarios used in training and four other scenarios dissimilar to the ones used in training, but previously validated as measures of hazard anticipation. The drivers trained on V-RAPT were found to anticipate a significantly greater proportion (86.25%) of latent hazards than the RAPT (62.36%) and placebo (30.97%) trained drivers. The V-RAPT trained drivers were also found to be better at mitigating potential threats. The virtual reality, headset-based training program holds out the promise of improving drivers’ ability to anticipate and mitigate latent threats and thereby reduce crashes.

Advanced Virtual Reality Based Training to Improve Young Drivers’ Latent Hazard Anticipation Ability

2017 ◽  
Vol 61 (1) ◽  
pp. 1995-1999 ◽  
Author(s):  
Ravi Agrawal ◽  
Michael Knodler ◽  
Donald L. Fisher ◽  
Siby Samuel
Keyword(s):  
Virtual Reality ◽  
Training Program ◽  
Driving Simulator ◽  
Training Programs ◽  
Risk Awareness ◽  
Novice Drivers ◽  
Feedback Training ◽  
Young Drivers ◽  
And Control ◽  
Risk Driving

The crash rate for young novice drivers is at least eight times higher than that of their experienced counterparts. Literature shows that the young novice drivers are not careless drivers but they are clueless drivers’ - clueless because of their inability to predict the risk ahead of time that might materialize on the forward roadway. Other error-feedback training programs exist that emphasize the teaching of risk awareness and perception skills to young drivers. In the current study, a Virtual reality based risk awareness and perception training program (V-RAPT) was developed on the Oculus Rift and evaluated on a driving simulator. The training program provides 360 degrees’ views of 6 high risk driving scenarios towards training the young driver to anticipate and mitigate latent hazards. Twenty-four participants in three experiment groups were trained on one of 3 training programs- VRAPT, RAPT and Control, and were evaluated on a driving simulator. Eye movements were collected throughout the experiment. The simulator evaluation drives included six near-transfer scenarios used in the training and four far-transfer scenarios not used in the training but validated previously in other similar studies. The young drivers trained on the V-RAPT were found to anticipate a significantly greater proportion (86.25%) of the potential latent hazards as compared to the RAPT trained young drivers (62.36%) and control trained drivers (30.97%). The VR-based training program is shown to be effective in improving young drivers’ ability to anticipate latent threats.

Latent Hazard Anticipation in Young Drivers: Review and Meta-Analysis of Training Studies

2018 ◽  
Vol 2672 (33) ◽  
pp. 11-19 ◽  
Author(s):  
James Unverricht ◽  
Siby Samuel ◽  
Yusuke Yamani
Keyword(s):  
Training Program ◽  
Driving Simulator ◽  
Motor Vehicle ◽  
Training Programs ◽  
Meta Analysis ◽  
Mode Of Delivery ◽  
Young Drivers ◽  
Training Studies ◽  
Target Skills ◽  
Method Of Evaluation

Young drivers are overrepresented in motor vehicle crashes, and are shown to be poorer at anticipating potential threats on the roadway compared with their more experienced peers. Literature demonstrates the effectiveness of driver training programs at improving young drivers’ latent hazard anticipation performance. Various hazard anticipation training studies have been undertaken on different population demographics using different training scenario presentation modes and multiple evaluation testbeds. These error-based feedback training programs (3M) allow trainees to make a mistake, show them how to mediate the mistake, and provide an opportunity to master the target skills. The current meta-analytical review focused on 19 peer-reviewed training studies that utilized eye movements to measure improvements in drivers’ latent hazard anticipation performance following training. The role of four moderating factors (mode of delivery – PC-based or non PC-based; presentation of training – egocentric or exocentric; method of evaluation – on-road or driving simulator; and age of sample – teen novices aged 16–17 or young drivers aged 18–21) on the training effects were explored. Overall, the current meta-analysis suggest that: (a) superficial improvements in training programs does not necessarily further improve the drivers’ latent hazard anticipation; (b) drivers who completed a training program with both egocentric and exocentric training views achieved greater levels of latent hazard anticipation performance than those who completed a training program that contained either view, but not both; and (c) the effect sizes of the 3M-based training programs on latent hazard anticipation were greater for drivers aged 18–21 years than drivers aged 16–17.

Minding the Gap: Effects of an Attention Maintenance Training Program on Driver Calibration

2020 ◽  
pp. 001872082096529
Author(s):  
James Unverricht ◽  
Yusuke Yamani ◽  
Jing Chen ◽  
William J. Horrey
Keyword(s):  
Training Program ◽  
Cognitive Skills ◽  
Driving Simulator ◽  
Training Programs ◽  
Driving Safety ◽  
Driver Training ◽  
Actual Performance ◽  
Young Drivers ◽  
Maintenance Behaviors ◽  
Target Skills

Objective The present study examines the effect of an existing driver training program, FOrward Concentration and Attention Learning (FOCAL) on young drivers’ calibration, drivers’ ability to estimate the length of their in-vehicle glances while driving, using two different measures, normalized difference scores and Brier Scores. Background Young drivers are poor at maintaining attention to the forward roadway while driving a vehicle. Additionally, drivers may overestimate their attention maintenance abilities. Driver training programs such as FOCAL may train target skills such as attention maintenance but also might serve as a promising way to reduce errors in drivers’ calibration of their self-perceived attention maintenance behaviors in comparison to their actual performance. Method Thirty-six participants completed either FOCAL or a Placebo training program, immediately followed by driving simulator evaluations of their attention maintenance performance. In the evaluation drive, participants navigated four driving simulator scenarios during which their eyes were tracked. In each scenario, participants performed a map task on a tablet simulating an in-vehicle infotainment system. Results FOCAL-trained drivers maintained their attention to the forward roadway more and reported better calibration using the normalized difference measure than Placebo-trained drivers. However, the Brier scores did not distinguish the two groups on their calibration. Conclusion The study implies that FOCAL has the potential to improve not only attention maintenance skills but also calibration of the skills for young drivers. Application Driver training programs may be designed to train not only targeted higher cognitive skills but also driver calibration—both critical for driving safety in young drivers.

The Promise of Virtual Reality Headsets: Can They be Used to Measure Accurately Drivers’ Hazard Anticipation Performance?

2019 ◽  
Vol 2673 (10) ◽  
pp. 455-464 ◽  
Author(s):  
Ganesh Pai Mangalore ◽  
Yalda Ebadi ◽  
Siby Samuel ◽  
Michael A. Knodler ◽  
Donald L. Fisher
Keyword(s):  
Virtual Reality ◽  
Driving Simulator ◽  
Dependent Measure ◽  
Middle Aged ◽  
Driving Simulators ◽  
Experimental Conditions ◽  
Young Drivers ◽  
The Difference ◽  
Open Road ◽  
Measures Of Performance

The objective of the current study is to evaluate the use of virtual reality (VR) headsets to measure driving performance. This is desirable because they are several orders of magnitude less expensive than simulators and, if validated, could greatly extend the powers of simulation. Out of several possible measures of performance that could be considered for evaluating VR headsets, the current study specifically examines drivers’ latent hazard anticipation behavior both because it has been linked to crashes and because it has been shown to be significantly poorer in young drivers compared with their experienced counterparts in traditional driving simulators and in open road studies. In a between-subject design, 48 participants were equally and randomly assigned to one of four experimental conditions—two young driver cohorts (18–21 years) and two middle-aged driver cohorts (30–55 years) navigating either a fixed-based driving simulator or a VR headset-based simulator. All participants navigated six unique scenarios while their eyes were continually tracked. The proportion of latent hazards anticipated by participants which constituted the primary dependent measure, was found to be greater for middle-aged drivers than young drivers across both platforms. The difference in the magnitude of performance between the young and middle-aged drivers was similar across the two platforms. The study provides some justification for the use of VR headsets as a way of understanding drivers’ hazard anticipation behavior.

Virtual reality and computer games in the treatment of driving phobia induced by a motor vehicle accident

2003 ◽  
Author(s):  
David Walshe ◽  
Elizabeth Lewis ◽  
Kathleen O'Sullivan ◽  
Brenda K. Wiederhold ◽  
Sun I. Kim
Keyword(s):  
Virtual Reality ◽  
Computer Games ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Vehicle Accident

341 Acute effects of seltorexant, a selective orexin-2 antagonist (JNJ- 42847922), on driving after bedtime administration

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A136-A136
Author(s):  
S Brooks ◽  
R G J A Zuiker ◽  
G E Jacobs ◽  
I Kezic ◽  
A Savitz ◽  
...  
Keyword(s):  
Postural Stability ◽  
Driving Simulator ◽  
Motor Vehicle ◽  
Subjective Effects ◽  
Driving Ability ◽  
Post Dose ◽  
Double Blind ◽  
Increased Risk ◽  
Subjective Sleepiness ◽  
The Difference

Abstract Introduction Seltorexant (JNJ-42847922), a potent and selective antagonist of the human orexin-2 receptor, is being developed for the treatment of major depressive disorder. Seltorexant also has sleep-promoting properties. Investigating the effects of sleep-promoting medications on driving is important because some of these agents (e.g. GABAA receptor agonists) may be associated with increased risk of motor vehicle accidents. We evaluated the effect of seltorexant on driving after forced awakening at night, using a validated driving simulator. Methods This double-blind, placebo and active-controlled, randomized, 3-way cross-over study was conducted in 18 male and 18 female healthy subjects. All subjects received seltorexant 40 mg, zolpidem 10 mg, or placebo 15 minutes before bedtime. Eighteen subjects were awakened at 2- and 6-hours post-dose, and the other 18 at 4- and 8-hours post-dose. At those timepoints, pharmacokinetics, objective (standard deviation of the lateral position [SDLP]) and subjective effects (using Perceived Driving Quality and Effort Scales) on driving ability, postural stability and subjective sleepiness were assessed. Results For seltorexant, the SDLP difference from placebo (95% confidence interval) at 2-, 4-, 6- and 8-hours post-dose was 3.9 cm (1.26, 6.60), 0.9 cm (-1.08, 2.92), 1.1 cm (-0.42, 2.63), and 0.6 cm (-2.75, 1.55), respectively vs. 9.6 cm (6.97, 12.38), 6.6 cm (3.53, 9.60), 4.7 cm (1.46, 7.85), and 1.3cm (-1.16, 3.80), respectively for zolpidem. The difference from placebo was significant at 2-hours after taking seltorexant, while the difference from placebo was significant at 2, 4 and 6-hours after zolpidem. Subjective driving quality was decreased for both drugs at all time points and driving effort was increased up to 4-hours post-dose for both medications. Subjective sleepiness showed a significant increase compared to placebo 2- and 4-hours after administration of either drug. Postural stability was decreased up to 2-hours after administration of seltorexant, and up to 4-hours after administration of zolpidem. Conclusion Compared to zolpidem, objective effects on driving performance were more transient after seltorexant administration and largely normalized by 4–6 hours post-dose. Support (if any) This work was sponsored by Janssen R&D.

scholarly journals Physically Based Soap Bubble Synthesis for VR

2021 ◽  
Vol 11 (7) ◽  
pp. 3090
Author(s):  
Sangwook Yoo ◽  
Cheongho Lee ◽  
Seongah Chin
Keyword(s):  
Virtual Reality ◽  
Virtual Space ◽  
Soap Bubble ◽  
Temporal Constraints ◽  
High Expectations ◽  
Soap Bubbles ◽  
Physical Motion ◽  
Head Mounted Display ◽  
Interesting Topic ◽  
Physically Based

To experience a real soap bubble show, materials and tools are required, as are skilled performers who produce the show. However, in a virtual space where spatial and temporal constraints do not exist, bubble art can be performed without real materials and tools to give a sense of immersion. For this, the realistic expression of soap bubbles is an interesting topic for virtual reality (VR). However, the current performance of VR soap bubbles is not satisfying the high expectations of users. Therefore, in this study, we propose a physically based approach for reproducing the shape of the bubble by calculating the measured parameters required for bubble modeling and the physical motion of bubbles. In addition, we applied the change in the flow of the surface of the soap bubble measured in practice to the VR rendering. To improve users’ VR experience, we propose that they should experience a bubble show in a VR HMD (Head Mounted Display) environment.

scholarly journals Comparative Analysis of Kinect-Based and Oculus-Based Gaze Region Estimation Methods in a Driving Simulator

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 26
Author(s):  
David González-Ortega ◽  
Francisco Javier Díaz-Pernas ◽  
Mario Martínez-Zarzuela ◽  
Míriam Antón-Rodríguez
Keyword(s):  
Virtual Reality ◽  
Comparative Analysis ◽  
Driving Simulator ◽  
Estimation Method ◽  
Estimation Methods ◽  
Gaze Estimation ◽  
Driving Simulators ◽  
Oculus Rift ◽  
Gaze Patterns ◽  
And Performance

Driver’s gaze information can be crucial in driving research because of its relation to driver attention. Particularly, the inclusion of gaze data in driving simulators broadens the scope of research studies as they can relate drivers’ gaze patterns to their features and performance. In this paper, we present two gaze region estimation modules integrated in a driving simulator. One uses the 3D Kinect device and another uses the virtual reality Oculus Rift device. The modules are able to detect the region, out of seven in which the driving scene was divided, where a driver is gazing at in every route processed frame. Four methods were implemented and compared for gaze estimation, which learn the relation between gaze displacement and head movement. Two are simpler and based on points that try to capture this relation and two are based on classifiers such as MLP and SVM. Experiments were carried out with 12 users that drove on the same scenario twice, each one with a different visualization display, first with a big screen and later with Oculus Rift. On the whole, Oculus Rift outperformed Kinect as the best hardware for gaze estimation. The Oculus-based gaze region estimation method with the highest performance achieved an accuracy of 97.94%. The information provided by the Oculus Rift module enriches the driving simulator data and makes it possible a multimodal driving performance analysis apart from the immersion and realism obtained with the virtual reality experience provided by Oculus.

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