User Experience in Immersive VR-Based Serious Game: An Application in Highly Automated Driving Training

Ebnali, Mahdi; Kian, Cyrus; Ebnali-Heidari, Majid; Mazloumi, Adel

doi:10.1007/978-3-030-20503-4_12

Cited by 11 publications

(8 citation statements)

References 20 publications

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“…This is because these factors were labeled as the least influential factor in all of the patterns. This also was confirmed by other studies [34] [35]. Although a driver can be affected by vehicle deficiency, improper road conditions, severe environmental conditions, and the ability to control the occurrence of an accident through a concentrated and healthy driver is crucial.…”

Section: Findings and Comparison To Previous Studiessupporting

confidence: 78%

Analyzing Drivers’ Mental Patterns Using Q-Methodology

Sheykhfard¹,

Haghighi²,

Soltaninejad³

et al. 2020

JTTs

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The human factor is the most important cause of road accidents. Investigating the drivers' mental patterns can lead to a better understanding of the factors that affect drivers to make a mistake and thus increase the likelihood of an accident. In this study, mental patterns of drivers as a human characteristic are determined through a questionnaire survey. To do this, 166 participants (18 -65 years) were asked to express their opinion on the possible effect of 25 factors on the occurrence of accidents. These factors were selected through the investigation of the accident database during the last three years in different areas of the case study. To analyze the data extracted from the survey, Q-methodology was used. The results of the factor analysis showed that there are 5 mental patterns among the participants. Based on the driver's opinion, human factors and road conditions were the most and least influential accident-generating items, respectively. The most significant reason for accidents determined by drivers was human errors including 1) unauthorized overtaking, 2) unauthorized speed, 3) driver distractions (such as cell phone), and 4) driver physical disability (such as visual impairment). Moreover, the failure of the vehicle was mostly reported as another influential contributor to accidents. It is worth mentioning that the results of this study can be used to minimize accidents resulted from drivers' behavioral errors by suggesting strategies for enhancing their performance through new manuals which is a step towards a safer road.

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Section: Findings and Comparison To Previous Studiessupporting

confidence: 78%

Analyzing Drivers’ Mental Patterns Using Q-Methodology

Sheykhfard¹,

Haghighi²,

Soltaninejad³

et al. 2020

JTTs

View full text Add to dashboard Cite

show abstract

“…The results also indicated a positive impact of the use of VR and interactive practices on takeover time in both critical and non-critical situations. Similarly, [23,35] revealed a faster takeover time as a result of training with VR. It seems that VR with interactive takeover tasks is an effective delivery method to educate first-time users to respond faster upon receiving TORs.…”

Section: Impact Of Familiarization Tours On Takeover Timementioning

confidence: 87%

“…Despite the growing number of VR applications being developed for training in various domains, little research has been conducted to explore the effects of such technology in automated driving training. [23,35], for example, used an HMD VR to examine its application in the acquisition of skills required for automated driving. Their results highlight the effectiveness of this training approach on reaction time (transition time from automated mode to manual mode) and users' self-reported measures (e.g.…”

Section: Importance Of Trainingmentioning

confidence: 99%

Virtual reality tour for first-time users of highly automated cars: Comparing the effects of virtual environments with different levels of interaction fidelity

et al. 2021

Self Cite

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“…This is in line with a growing body of literature on the positive utility of travel ( 41 – 44 ). Besides, the heterogeneity in individuals’ travel behavior in different incidents was reported in the literature ( 45 – 47 ).…”

Section: Methodsmentioning

confidence: 95%

Analysis of Transit Users’ Response Behavior in Case of Unplanned Service Disruptions

Rahimi

Shamshiripour

Shabanpour

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Public transit disruption is becoming more common across different transit services, and can have a destructive influence on the resiliency of the transportation system. Even though transit agencies have various strategies to mitigate the probability of failure in the transit system by conducting preventative actions, some disruptions cannot be avoided because of their either unpredictable or uncontrollable nature. Utilizing recently collected data of transit users in the Chicago Metropolitan Area, the current study aims to analyze how transit users respond to an unplanned service disruption and disclose the factors that affect their behavior. In this study, a random parameter multinomial logit model is employed to consider heterogeneity across observations as well as panel effects. The results of the analysis reveal that a wide range of factors including socio-demographic attributes, personal attitudes, trip-related information, and built environment are significant in passengers’ behavior in case of unplanned transit disruptions. Moreover, the effect of service recovery time on passengers is not the same among all types of disrupted services; rail users are more sensitive to the recovery time as compared with bus users. The findings of this study provide insights for transportation authorities to improve the transit service quality in relation to user satisfaction and transportation resilience. These insights help transit agencies to implement effective recovery strategies.

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User Experience in Immersive VR-Based Serious Game: An Application in Highly Automated Driving Training

Cited by 11 publications

References 20 publications

Analyzing Drivers’ Mental Patterns Using Q-Methodology

Analyzing Drivers’ Mental Patterns Using Q-Methodology

Virtual reality tour for first-time users of highly automated cars: Comparing the effects of virtual environments with different levels of interaction fidelity

Analysis of Transit Users’ Response Behavior in Case of Unplanned Service Disruptions

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