Observation of drivers' behavior at narrow roads using immersive car driving simulator

2010 ◽  
Author(s):  
Yoshisuke Tateyama ◽  
Hiroki Yamada ◽  
Junpei Noyori ◽  
Yukihiro Mori ◽  
Keiichi Yamamoto ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Car Driving

scholarly journals Research and training car driving simulator with weight up to 3.5 tons dedicated to physical disabled drivers

Mechanik ◽  
2019 ◽  
Vol 92 (8-9) ◽  
pp. 571-573
Author(s):  
Jarosław Jankowski
Keyword(s):  
Degrees Of Freedom ◽  
Driving Simulator ◽  
Physical Disabilities ◽  
Motion Platform ◽  
Six Degrees Of Freedom ◽  
Work Related ◽  
The Creation ◽  
People With Physical Disabilities ◽  
Car Driving ◽  
And Training

The article presents the continuation of work related to the creation of a car driving simulator with a weight of up to 3.5 tons adapted to selected disabilities. The article contains a description of the developed motion platform with six degrees of freedom and the cockpit. In order to ensure the possibility of being managed by the largest group of people with physical disabilities, selected support solutions were implemented. These devices can be easily dismantled to test others. The platform together with the cockpit is controlled from the simulator application and the image is presented to the simulation participant in 3D projection glasses and optionally on a three-segment screen.

scholarly journals AN EVALUATION OF AIRPORT WAYFINDING AND SIGNAGE ON SENIOR DRIVER BEHAVIOUR AND SAFETY OF AIRPORT ROAD ACCESS DESIGN

2017 ◽  
Vol 8 (1) ◽  
pp. 108-129
Author(s):  
Nur Khairiel Anuar ◽  
Romano Pagliari ◽  
Richard Moxon
Keyword(s):  
Road Safety ◽  
Driving Simulator ◽  
Age Groups ◽  
Driver Behaviour ◽  
Driving Behaviour ◽  
Road Design ◽  
Driver Performance ◽  
Study Participants ◽  
The Impact ◽  
Car Driving

The purpose of this study was to investigate the impact of different wayfinding provision on senior driving behaviour and road safety. A car driving simulator was used to model scenarios of differing wayfinding complexity and road design. Three scenario types were designed consisting of 3.8 miles of airport road. Wayfinding complexity varied due to differing levels of road-side furniture. Experienced car drivers were asked to drive simulated routes. Forty drivers in the age ranges: 50 to 54, 55 to 59 and those aged over 60 were selected to perform the study. Participants drove for approximately 20 minutes to complete the simulated driving. The driver performance was compared between age groups. Results were analysed by Mean, Standard Deviation and ANOVA Test, and discussed with reference to the use of the driving simulator. The ANOVA confirmed that age group has a correlation between road design complexity, driving behaviour and driving errors.

Development of Pedal Operation Analysis Method for Car Driving Simulator Using Head-mounted Display

2020 ◽  
Vol 2020.30 (0) ◽  
pp. 2408
Author(s):  
Kazunori KAEDE ◽  
Kaito KOBAYASHI ◽  
Keiichi MURAMATSU ◽  
Keiichi WATANUKI
Keyword(s):  
Driving Simulator ◽  
Analysis Method ◽  
Operation Analysis ◽  
Head Mounted Display ◽  
Car Driving

scholarly journals Effects of Smoking Cannabis on Visual Function and Driving Performance. A Driving-Simulator Based Study

2020 ◽  
Vol 17 (23) ◽  
pp. 9033
Author(s):  
Sonia Ortiz-Peregrina ◽  
Carolina Ortiz ◽  
José J. Castro-Torres ◽  
José R. Jiménez ◽  
Rosario G. Anera
Keyword(s):  
Visual Function ◽  
Driving Simulator ◽  
Driving Performance ◽  
Driving Under The Influence ◽  
Driving Safety ◽  
Limited Information ◽  
Baseline Session ◽  
Visual Effects ◽  
Awareness Campaigns ◽  
Car Driving

Cannabis is the most widely used illegal drug in the world. Limited information about the effects of cannabis on visual function is available, and more detail about the possible impact of visual effects on car driving is required. This study investigated the effects of smoking cannabis on vision and driving performance, and whether these effects are correlated. Twenty drivers and occasional users were included (mean (SE) age, 23.3 (1.0) years; five women). Vision and simulated driving performance were evaluated in a baseline session and after smoking cannabis. Under the influence of cannabis, certain visual functions such as visual acuity (p < 0.001), contrast sensitivity (p = 0.004) and stereoacuity (far, p < 0.001; near, p = 0.013) worsened. In addition, there was an overall deterioration of driving performance, with the task of keeping the vehicle in the lane proving more difficult (p < 0.05). A correlation analysis showed significant associations between driving performance and visual function. Thus, the strongest correlations were found between the distance driven onto the shoulder and stereoacuity, for near (ρ = 0.504; p = 0.001) and far distances (ρ = 0.408; p = 0.011). This study provides the first evidence to show that the visual effects of cannabis could impact driving performance, compromising driving safety. The results indicate that information and awareness campaigns are essential for reducing the incidence of driving under the influence of cannabis.

3108 Observation of Driving Behavior on Narrow Road in an Immersive Car Driving Simulator

2011 ◽  
Vol 2011.21 (0) ◽  
pp. 529-532
Author(s):  
Yoshisuke Tateyama ◽  
Junji Yamada ◽  
Junpei Noyori ◽  
Keiichi Yamamoto ◽  
Kana Kumeta ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Driving Behavior ◽  
Narrow Road ◽  
Car Driving

scholarly journals Neural basis for adaptive motor behavior during car driving

2021 ◽  
Author(s):  
Ryu Ohata ◽  
Kenji Ogawa ◽  
Hiroshi Imamizu
Keyword(s):  
Driving Simulator ◽  
Parietal Lobe ◽  
Motor Behavior ◽  
Functional Neuroimaging ◽  
Daily Practice ◽  
Posterior Cingulate Cortex ◽  
Unique Contribution ◽  
Neural Basis ◽  
Sensitivity Change ◽  
Car Driving

AbstractCar driving is supported by motor skills trained through continuous daily practice. One of the skills unique to expert drivers is the ability to detect abrupt changes in the driving environment and then quickly adapt their operation mode to the changes. Previous functional neuroimaging studies on motor control investigated the mechanisms underlying behaviors adaptive to changes in control properties of simple experimental devices such as a computer mouse or a joystick. The switching of multiple internal models mainly engages adaptive behaviors and underlies the interplay between the cerebellum and frontoparietal network (FPN) regions as the neural process. However, it remains unclear whether the neural mechanisms identified through an experimental paradigm using such simple devices also underlie practical driving behaviors. In the current study, we measure functional magnetic resonance imaging (fMRI) activities while participants control a realistic driving simulator inside the MRI scanner. Here, the accelerator sensitivity of a virtual car is abruptly changed, requiring participants to respond to this change as quickly as possible. We first compare brain activities before and after the sensitivity change. As a result, sensorimotor areas, including the left cerebellum, increase their activities after the sensitivity change. Moreover, after the change, activity significantly increases in the inferior parietal lobe and dorsolateral prefrontal cortex, parts of the FPN regions. By contrast, the posterior cingulate cortex, a part of the default mode network, deactivates after the sensitivity change. Our results suggest that the neural bases found in previous experiments using the simpler devices can serve as the foundation of adaptive car driving. At the same time, this study also highlights the unique contribution of non-motor-related regions to addressing the high cognitive demands of driving.

Evaluation of Driver's Cognitive Distracted State Considering the Ambient State of a Car

2019 ◽  
Vol 13 (1) ◽  
pp. 13-24
Author(s):  
Hiroaki Koma ◽  
Taku Harada ◽  
Akira Yoshizawa ◽  
Hirotoshi Iwasaki
Keyword(s):  
Machine Learning ◽  
Driving Simulator ◽  
Support Vector ◽  
Biometric Data ◽  
State Data ◽  
The Road ◽  
Machine Learning Model ◽  
Vector Machines ◽  
Car Driving ◽  
Machine Learning Models

The effectiveness of considering the ambient state of a driving car for evaluating the driver's cognitive distracted state is evaluated. In this article, Support Vector Machines and Random Forest, which are representative machine learning models, are applied. As input data for the machine learning model, in addition to a driver's biometric data and car driving data, an ambient state data of a driving car are used. The ambient state data of a driving car considered in this study are that of the preceding car and the shape of the road. Experiments using a driving simulator are conducted to evaluate the effectiveness of considering the ambient state of a driving car.

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