Studying freeway merging conflicts using virtual reality technology

Are virtual reality applications effective for construction safety training and education? A systematic review and meta-analysis

INTRODUCTION

Virtual reality (VR) gains attention in construction safety training because it allows users to simulate real activities without the risks of real activities. However, a literary work comprehensively describing the effectiveness of VR in construction safety training and education (CSTE) is lacking.

METHOD

This study provides a systematic review of the research related to VR applications for CSTE over the past decade using meta-analysis techniques. Standardized mean differences between traditional training methods and VR training were grouped by measurement. Potential moderators possibly affecting the effectiveness of VR in CSTE were analyzed.

RESULTS

Results showed that VR is significantly more effective in construction training and education than traditional methods. The effectiveness of VR was 0.593, 0.432, and 0.777 higher than that of traditional methods for behaviors, skills, and experience measurements, respectively. The training context and mean work experience of trainees were two important moderators that significantly affected the effectiveness of VR in CSTE (p < 0.001).

PRACTICAL APPLICATIONS

The presented results suggested the need for targeted development and management of VR technology in the construction industry and the early promotion of VR for general safety training among young, inexperienced construction workers.

Assessing bicycle-vehicle conflicts at urban intersections utilizing a VR integrated simulation approach

Animosity between drivers and cyclists has existed on urban road networks for many years. Conflicts between these two groups of road users are exceptionally high in the shared right-of-way environments. Benchmarking methods of conflict assessments are mostly based on statistical analysis with limited data sources. The actual crash data would be valuable to understand the features of bike-car collisions, however the available data are spatially and temporally sparse. To this end, this paper proposes a simulation-based bicycle-vehicle conflict data generation and assessment approach. The proposed approach uses a three-dimensional visualization and virtual reality platform, integrating traffic microsimulation to reproduce a naturalistic driving/cycling-enabled experimental environment. The simulation platform is validated to reflect the human-resembled driving/cycling behaviors under different infrastructure designs. Comparative experiments are carried out on bicycle-vehicle interactions under different conditions, with data collected from a total of 960 scenarios. Based on the results of the surrogate safety assessment model (SSAM), the obtained key insights include: (1) scenarios of a high conflict probability do not lead to actual crashes, which suggests that the classic SSM-based measurements such as TTC or PET values may not sufficiently reflect real cyclist-driver interactions; (2) the major cause of conflicts is variation in vehicle acceleration, which suggests that drivers are considered to be the main party responsible for bicycle-vehicle conflict/crash occurrence; (3) the proposed approach is able to generate near-miss events and reproduce interaction patterns between cyclists and drivers, facilitating experiments and data collections which would be typically unavailable for this type of study.

Adjustment of key lane change parameters to develop microsimulation models for representative assessment of safety and operational impacts of adverse weather using SHRP2 naturalistic driving data

INTRODUCTION

Adverse weather has a considerable negative impact on safety and mobility of transportation networks. Microsimulation models are one of the potential tools that could be used to evaluate the safety and operational impacts of adverse weather. The development of a realistic microsimulation model requires the adjustment of driving behavior parameters with disaggregate trajectory-level data. This study presented a novel approach to update and adjust lane change model parameters for the development of realistic microsimulation models in different weather conditions by leveraging the trajectory-level data from SHRP2 Naturalistic Driving Study (NDS).

METHOD

Representative key lane change parameters in various weather conditions were extracted from an automatic identification algorithm. These lane change parameters were used to develop microsimulation models in VISSIM in an attempt to assess the safety and operational impacts of adverse weather on a freeway weaving segment.

RESULTS

The evaluation of safety impacts of adverse weather with regard to three Surrogate Measures of Safety (SMoS) namely Time-to-Collision (TTC), Post Encroachment Time (PET), and Deceleration Rate to Avoid Collision (DRAC) suggested that extreme adverse weather (including heavy rain, heavy snow, and heavy fog) produced a higher total number of simulated conflicts compared to clear weather. The operational analysis results revealed that adjusted parameters in most of the adverse weather produced lower average speeds with higher total travel times and total delays than clear weather.

CONCLUSIONS

The outcomes of safety and operational assessments for the adjusted parameters showed that the development of microsimulation models should be based on weather-specific, rather than default parameters.

PRACTICAL APPLICATIONS

The methodology presented in this study could be adopted by transportation agencies to develop weather-specific microsimulation models. Moreover, the demonstrated approach could be used to evaluate different Connected Vehicle (CV) applications related to lane change in terms of safety and operations in microsimulation platforms.

A Systematic Review of Virtual Reality Applications for Automated Driving: 2009–2020

While virtual reality (VR) interfaces have been researched extensively over the last decades, studies on their application in vehicles have only recently advanced. In this paper, we systematically review 12 years of VR research in the context of automated driving (AD), from 2009 to 2020. Due to the multitude of possibilities for studies with regard to VR technology, at present, the pool of findings is heterogeneous and non-transparent. We investigated N = 176 scientific papers of relevant journals and conferences with the goal to analyze the status quo of existing VR studies in AD, and to classify the related literature into application areas. We provide insights into the utilization of VR technology which is applicable at specific level of vehicle automation and for different users (drivers, passengers, pedestrians) and tasks. Results show that most studies focused on designing automotive experiences in VR, safety aspects, and vulnerable road users. Trust, simulator and motion sickness, and external human-machine interfaces (eHMIs) also marked a significant portion of the published papers, however a wide range of different parameters was investigated by researchers. Finally, we discuss a set of open challenges, and give recommendation for future research in automated driving at the VR side of the reality-virtuality continuum.

Incorporating Virtual Reality Technology in Safety Training Solution for Construction Site of Urban Cities

The lack of experiential training has become the primary cause of incidents that could have been easily avoided in construction areas, and the implementation of experimental training is crucial in minimizing incidents at construction sites. The limitation on the available training tools has made it impossible to build up a real test site or reproduce the constructing scenarios. This research aims to develop an immersive and interactive multiplayer-based training platform that incorporates virtual reality (VR) technology to improve the safety awareness of workers. The developed simulation platform serves as a training solution, enabling the provision of repeatable and flexible procedures within a secure environment. An evaluation survey was conducted to make a comparison between traditional training methods and the proposed VR solution. Promisingly, the results indicate that workers were better trained under the developed immersive environment, and they could memorize critical points more effectively because the implementation of VR technology can allow people to experience hazardous situations without being physically injured, thus creating a safer and more efficient training environment. This study reveals that the nomination of the proposed VR platform could reap many benefits and become an advantageous tool for construction training, as well as stimulate human-machine interaction research.