The Effects of PC-Based Training on Novice Drivers' Risk Awareness in a Driving Simulator

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.

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A Behavioral Model of Drivers’ Mean Speed Influenced by Weather Conditions, Road Geometry, and Driver Characteristics Using a Driving Simulator Study

Advances in Civil Engineering ◽

10.1155/2021/5542905 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Mehdi Zolali ◽

Babak Mirbaha ◽

Maziyar Layegh ◽

Hamid Reza Behnood

Keyword(s):

Latent Variables ◽

Latent Variable ◽

Structural Equation ◽

Driving Simulator ◽

Weather Conditions ◽

Geometric Design ◽

Equation Modeling ◽

Novice Drivers ◽

Sight Distance ◽

The Mean

Driving above the speed limit is one of the factors that significantly affect safety. Many studies examined the factors affecting the speed of vehicles in the simulated environment. The present study aimed to analyze drivers’ characteristics, time and weather conditions, and geometric features’ effect on mean speed in simulated conditions simultaneously. In this regard, the simulator experiment data of 70 drivers were collected in a two-lane rural highway at six different times, and weather scenarios and their socioeconomic characteristics were collected by a questionnaire. Structural equation modeling (SEM) was used to capture the complex relationships among related variables. Eleven variables were grouped into four latent variables in the structural model. Latent variables including “Novice Drivers,” “Experienced Drivers,” “Sight Distance,” and “Geometric Design” were defined and found significant on their mean speed. The results showed that “Novice Drivers” have a positive correlation with the mean speed. Meanwhile, “Experienced Drivers,” who drive 12% slower than the novice group, negatively affect the mean speed with a standard regression weight of −0.08. This relation means that young and novice drivers are more inclined to choose higher speeds. Among variables, the latent variable “Sight Distance” has the most significant effect on the mean speed. This model shows that foggy weather conditions strongly affect the speed selection behavior and reduce the mean speed by 40%. Nighttime also reduces mean speed due to poor visibility conditions. Furthermore, “Geometric design” as the latent variable indicates the presence of curves on the simulated road, and it can be concluded that the existence of a curve on the road encourages drivers to slow down, even young drivers. It is noteworthy that the parts of the simulated road with a horizontal curve act as a speed reduction tool for drivers.

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Evaluation of a Training Intervention to Improve Novice Drivers’ Hazard Mitigation When Approaching Left Turn Scenarios

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119860484 ◽

2019 ◽

Vol 2673 (12) ◽

pp. 474-484

Author(s):

Jeffrey W. Muttart ◽

Swaroop Dinakar ◽

Donald L. Fisher ◽

Teena M. Garrison ◽

Siby Samuel

Keyword(s):

Video Game ◽

Driving Simulator ◽

Phase 1 ◽

Hazard Mitigation ◽

Management Strategies ◽

Crash Risk ◽

Novice Drivers ◽

Left Turn ◽

Street Sign ◽

Teenage Drivers

Crash statistics reveal that newly licensed teenage drivers experience a higher risk of crashing than more experienced drivers, particularly when turning left across the path of approaching traffic. Research has also demonstrated that novice drivers exhibit poor hazard mitigation skills. The current study assesses the effectiveness of a training program aimed at improving novice drivers’ hazard mitigation and speed selection behaviors as both the through driver and turning driver in left turn across path scenarios. In this study, novice drivers were randomly assigned to one of two training cohorts: anticipation-control-terminate (ACT) or placebo. Phase 1 of ACT is a video game where drivers must select where to look, where they would steer, and when they would slow when observing the approach to known fatal crash risk scenarios. Placebo training involved reaction time tests and street sign definitions. In phase 2 the ACT-trained participants were shown where their choices were similar to, or different than, those of drivers aged 26 through 61who had not had crashed in the previous 10 years. In phase 3, ACT-trained drivers were compared with placebo-trained drivers at left turn scenarios both as through driver and turning driver, using a driving simulator. ACT-trained drivers were more likely to exhibit anticipatory glances and slowing behaviors, and were significantly less likely to crash than were placebo-trained drivers. The results indicate that ACT was effective as a countermeasure for training novice drivers to select better speed management strategies in the simulated scenarios utilized in this research.

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Influence of Driving Experience on Distraction Engagement in Automated Vehicles

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119843476 ◽

2019 ◽

Vol 2673 (9) ◽

pp. 142-151 ◽

Cited By ~ 6

Author(s):

Dengbo He ◽

Birsen Donmez

Keyword(s):

Secondary Task ◽

Driving Simulator ◽

Phase 1 ◽

Task Engagement ◽

Automated Vehicles ◽

Automated Driving ◽

Novice Drivers ◽

Driving Experience ◽

Driving Skill ◽

The Difference

State-of-the-art vehicle automation requires drivers to visually monitor the driving environment and the automation (through interfaces and vehicle’s actions) and intervene when necessary. However, as evidenced by recent automated vehicle crashes and laboratory studies, drivers are not always able to step in when the automation fails. Research points to the increase in distraction or secondary-task engagement in the presence of automation as a potential reason. However, previous research on secondary-task engagement in automated vehicles mainly focused on experienced drivers. This issue may be amplified for novice drivers with less driving skill. In this paper, we compared secondary-task engagement behaviors of novice and experienced drivers both in manual (non-automated) and automated driving settings in a driving simulator. A self-paced visual-manual secondary task presented on an in-vehicle display was utilized. Phase 1 of the study included 32 drivers (16 novice) who drove the simulator manually. In Phase 2, another set of 32 drivers (16 novice) drove with SAE-level-2 automation. In manual driving, there were no differences between novice and experienced drivers’ rate of manual interactions with the secondary task (i.e., taps on the display). However, with automation, novice drivers had a higher manual interaction rate with the task than experienced drivers. Further, experienced drivers had shorter average glance durations toward the task than novice drivers in general, but the difference was larger with automation compared with manual driving. It appears that with automation, experienced drivers are more conservative in their secondary-task engagement behaviors compared with novice drivers.

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Hazard Detection Prediction Model for Rural Roads Based on Hazard and Environment Properties

PROMET - Traffic&Transportation ◽

10.7307/ptt.v30i6.2638 ◽

2018 ◽

Vol 30 (6) ◽

pp. 683-692

Author(s):

Morteza Asadamraji ◽

Mahmoud Saffarzadeh ◽

Aminmirza Borujerdian ◽

Tayebeh Ferdousi

Keyword(s):

Driving Simulator ◽

Fuzzy Model ◽

Color Difference ◽

Rural Roads ◽

Novice Drivers ◽

Detection Ability ◽

Hazard Detection ◽

Safety Improvement ◽

Size Contrast ◽

The Right

A driver’s reaction time encountering hazards on roads involves different sections, and each section must occur at the right time to prevent a crash. An appropriate reaction starts with hazard detection. A hazard can be detected on time if it is completely visible to the driver. It is assumed in this paper that hazard properties such as size and color, the contrast between the environment and a hazard, whether the hazard is moving or fixed, and the presence of a warning are effective in improving driver hazard detection. A driving simulator and different scenarios on a two-lane rural road are used for assessing novice and experienced drivers’ hazard detection, and a Sugeno fuzzy model is used to analyze the data. The results show that the hazard detection ability of novice and experienced drivers decreases by 35% and 64%, respectively, during nighttime compared to daytime. Also, moving hazards increase hazard detection ability by 9% and 180% for experienced and novice drivers, respectively, compared to fixed hazards. Moreover, increasing size, contrast, and color difference affect hazard detection under nonlinear functions. The results could be helpful in safety improvement solution prioritization and in preventing vehicle-pedestrian, vehicle-animal, and vehicle-object crashes, especially for novice drivers.

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Train-driving simulator studies: Can novice drivers deliver the goods?

Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit ◽

10.1177/0954409717704260 ◽

2017 ◽

Vol 231 (10) ◽

pp. 1186-1194 ◽

Cited By ~ 2

Author(s):

David R Large ◽

David Golightly ◽

Emma Taylor

Keyword(s):

Driving Simulator ◽

Limited Data ◽

Novice Drivers ◽

Control Actions ◽

Dependent Variables ◽

Frequent Switching ◽

Train Drivers ◽

And Performance ◽

The University ◽

And Control

Early research suggests that, in a simulated train-driving environment, unskilled, novice drivers may exhibit comparable behaviour and performance to experienced, professional train drivers after receiving only minimal, task-specific training. However, this conclusion is based on exiguous performance indicators, such as speed limit exceedances, SPAD violations, etc. and considers only limited data. This paper presents further, detailed analysis of the driving performance data obtained from 20 drivers (13 novices and 7 experienced train drivers), who took part in a previous simulator-based research study, utilising more sensitive and perspicuous measures, namely acceleration noise and control actuation. The results indicate that, although both cohorts exhibited similar performance using the original metrics, and would thus support the same conclusions, the manner in which this performance was effected is fundamentally different between groups. Trained novice drivers (mainly comprising students and staff at the University of Nottingham) adopted far more erratic speed control profiles, characterised by longer control actions and frequent switching between power and brake actuation. In contrast, experienced drivers delivered smoother acceleration/braking profiles with more subtle (and shorter) control actions and less variance in speed. We conclude that although utilising trained non-drivers may offer an appealing solution in the absence of professional train drivers during simulator-based research, and their input remains of value, researchers should remain mindful when interpreting results and drawing conclusions from a contingent comprising non-drivers. The work also demonstrates the value of dependent variables such as acceleration noise, and quantitative measures of control actuation, which may offer an insightful portfolio of measures in train-driving research studies.

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Understanding the Effect of Feedback on Young Drivers’ Speeding Behavior

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931213601452 ◽

2016 ◽

Vol 60 (1) ◽

pp. 1986-1990 ◽

Cited By ~ 2

Author(s):

Oleksandra Krasnova ◽

Brett Molesworth ◽

Ann Williamson

Keyword(s):

Feedback Control ◽

Driving Simulator ◽

Control Group ◽

Management Behavior ◽

Management Performance ◽

Novice Drivers ◽

New Approach ◽

Young Drivers ◽

Speed Management ◽

Computer Based

The aim of the present study was to empirically investigate the effect of various types of feedback on young novice drivers’ speed management behavior. One hundred young drivers, randomly allocated to five groups, completed three test drives using a computer-based driving simulator. For four groups, feedback was provided after an 11km drive and focused on speeding behavior, the safety implications of speeding or the financial penalties if caught speeding or all three. The fifth group was a no-feedback control. Driver speed management performance was examined in two 11km drives immediately following the receipt of feedback and one week post feedback. The results showed that all types of Feedback were effective in improving young drivers’ speed management behavior compared to the control group. Providing feedback about financial implications of speeding was found to be the best in improving young drivers’ speed management behavior across all tested conditions. These findings have important implications for the development of a new approach to improve young drivers’ speed management behavior.

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