Study on response time measurement of distracted driving by virtual reality driving simulator

Kim, Sunwoo; Park, Seongsoo; Jeong, Hyo-Won; Sung, Junghwan

doi:10.19101/ijacr.mul16002

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…After simulated driver's training, participants can: park the vehicle more accurately at the stop line and significantly reduce traffic violations. Compared with actual road driving tests, VR driving simulation is expected to improve the immersion and cost-effectiveness of simulated driving, and it can provide a safe test environment and objective data to measure driving performance (Kim et al, 2019). Jung and Kim (2012) use intelligent devices (like iPod and iPhone) to develop modified vehicles suitable for people with physical disabilities and evaluate their performance in VR driving simulation experiments.…”

Section: Introductionmentioning

confidence: 99%

Usability evaluation for driving simulation with the mechanical and joystick manual controllers

Hsieh

Lin³

et al. 2022

Int. J. Appl. Sci. Eng.

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For lower limb disabled driving a car, mechanical manual controllers are mainly used to control the brake and accelerator. However, the joystick is used chiefly to drive an airplane; is seldom used in driving a car. This study aimed to evaluate the usability of the joystick-style and mechanical manual controller for the lower-limb disabled driving vehicle. Twenty participants were divided into experimental and control groups (10 persons for each group). The experimental group was lower disabled the control group was the non-disabled. Each subject performed a driving simulator experiment with the two manual controllers. Driving performance, physiological load, and SUS score were collected during the investigation. From the statistical results of this study, it can be found that there are significant differences in the average completion time between the two manual controllers, and both groups spent less time with the joystick-type. For the SUS (System Usability Scale) results, the control group thinks the joystick-type is more suitable for use. Both groups had a significantly better driving performance with the joystick-type manual controller than the mechanical manual one. They also had a considerably lower physiological load (relative heart rate) with the joystick-type manual controller than the mechanical manual one. However, they had similar subjective assessments between the two tested controllers. This study provides an advanced investigation for applying a joystick in driving a car. But, further experiments should be conducted on the road to confirm safety and efficiency.

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Section: Introductionmentioning

confidence: 99%

Usability evaluation for driving simulation with the mechanical and joystick manual controllers

Hsieh

Lin³

et al. 2022

Int. J. Appl. Sci. Eng.

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Simulation-Oriented Analysis and Modeling of Distracted Driving

Zhu,

Yue

2024

Applied Sciences

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Distracted driving significantly affects the efficiency and safety of traffic flow. Modeling distracted driving behavior in microscopic traffic flow simulation is essential for understanding its critical impacts on traffic flow. However, due to the influence of various external factors and the considerable uncertainties in behavior characteristics, modeling distracted driving behavior remains a challenge. This study proposed a model which incorporates distraction features into the microscopic traffic flow model to simulate distracted driving behavior. Specifically, the study first examines the characteristics of distracted driving, including the intervals and durations of distraction events, as well as the patterns and environments of distraction. It then introduces distraction parameters into the Intelligent Driver Model (IDM), including reaction time delays and perception deviations in both speed difference and following distance. These parameters are quantified by probabilistic distributions to reflect the uncertainty and individual differences in driving behavior. The model is calibrated and validated using 772 distracted following events from the Shanghai Naturalistic Driving Study (SH-NDS) data. Three patterns of distraction (excessive, moderate, mild) are distinguished and modeled separately. The results show that the model’s accuracy surpasses that of the IDM under various road types and traffic volumes, with an average improvement in model accuracy of about 11.30% on expressways with high traffic volume, 4.54% on expressways with low traffic volume, and 4.46% on surface roads. Meanwhile, the model can effectively simulate the variations in reaction times and perceptual deviations in both speed and following distance for different distraction modes at the individual level, maintaining consistency with reality. Finally, the study simulates distracted driving behavior under different road environments and traffic volumes to explore the impact of distracted driving on traffic flow. The simulation results indicate that an increase in the proportion of distraction reduces the efficiency and safety of traffic flow, which is consistent with real-world observations. Since the model considers human distraction factors, it can generate more dangerous driving scenarios in simulations, which holds significant importance for safety-related research. The findings from this study are expected to be helpful for understanding distracted driving behavior and mitigate its negative influence on the efficiency and safety of traffic flow.

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Study on response time measurement of distracted driving by virtual reality driving simulator

Cited by 2 publications

References 3 publications

Usability evaluation for driving simulation with the mechanical and joystick manual controllers

Usability evaluation for driving simulation with the mechanical and joystick manual controllers

Simulation-Oriented Analysis and Modeling of Distracted Driving

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