• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.3-7
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• 2005

Recently, vehicle simulators are widely used to evaluate driver s responses and driver assistance systems. It needs much effort to construct the virtual driving environment for a vehicle simulator. In this study, it is described how to make effectively the roads and the driving environment for a vehicle simulator. GIS (Geographic Information System) is used to construct the roads and the environment effectively. Because the GIS is the integrated system of geographical data, it contains useful data to make virtual driving environment. First, boundaries and centerlines of roads are extracted from the GIS. From boundaries, the road width is calculated. Using centerlines, mesh models of roads are constructed. The final graphic model of roads is constructed by mapping road images to those mesh models considering the number of lanes and the kind of surface. Data of buildings from the GIS are extracted. Each shape and height of building is determined considering the kind of building to construct the final graphic model of buildings. Then, the graphic model of roadside trees is constructed to decide their locations. Finally, the driving environment for driving simulator is constructed by converting the three graphic models with the graphic format of Direct-X and by joining the three graphic models.

• Journal of the Ergonomics Society of Korea
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• v.28 no.2
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• pp.9-16
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• 2009

The aim of this study was to evaluate the effects of the driving performance and workload according to changing driving environment and types of steering wheel. Twelve drivers who participated in this study consisted of two groups; six Japanese as the left-lane drivers who was accustomed to driving on left-hand side of the road, and six Europeans, Americans, and Korean as the right-lane drivers who was accustomed to driving on right-hand side of the road. They were asked to operate a driving simulator while using two different types of steering wheel (for the left-hand side driving and the right-hand side driving). During the experiment, a range of data were measured including driving performance, mental workload, and eye movements which were recorded in order to identify the amount of time looking towards the in-vehicle route guidance. Results indicated that the use of the steering wheel by parallel moving led to increase high attentional demand and worse glance behavior to traffic signs for the left-lane drivers. In the case of the right-lane drivers, the effects by changing driving direction were more effective than the types of steering wheel due to their habit or traits.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.273-280
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• 2022

Along with the advancement of deep learning technology, securing high-quality dataset for verification of developed technology is emerging as an important issue, and developing robust deep learning models to the domestic road environment is focused by many research groups. Especially, unlike expressways and automobile-only roads, in the complex city driving environment, various dynamic objects such as motorbikes, electric kickboards, large buses/truck, freight cars, pedestrians, and traffic lights are mixed in city road. In this paper, we built our dataset through multi camera-based processing (collection, refinement, and annotation) including the various objects in the city road and estimated quality and validity of our dataset by using YOLO-based model in object detection. Then, quantitative evaluation of our dataset is performed by comparing with the public dataset and qualitative evaluation of it is performed by comparing with experiment results using open platform. We generated our 2D dataset based on annotation rules of KITTI/COCO dataset, and compared the performance with the public dataset using the evaluation rules of KITTI/COCO dataset. As a result of comparison with public dataset, our dataset shows about 3 to 53% higher performance and thus the effectiveness of our dataset was validated.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.202-217
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• 2021

Following the fatal accident of pedestrians caused by Autonomous Vehicle by Uber, the world's largest ride-hailing company, two people were killed in a self-driving car accident by Tesla in April. There is a need to ensure the safety of road users. Accordingly, in order to secure the safety of Autonomous Vehicle driving, it is necessary to evaluate Autonomous Vehicle driving technologies in various situations based on the road and traffic environment in which the Autonomous vehicle will actually drive. Therefore, this study used UC-win/Road ver.14.0 based on general driver's license test questions to present a virtual reality-based Autonomous Vehicles driving ability evaluation tool among various driving ability test method. Based on this, it was intended to test driving ability for unexpected situations in complex and diverse driving environments, and to confirm its practical applicability as an optimal tool for Autonomous vehicle ability test and evaluation.

• Journal of Auto-vehicle Safety Association
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• v.8 no.4
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• pp.31-37
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• 2016

Since real road experiments have many restrictions, a multi-vehicle traffic simulator can be an effective tool to develop and evaluate fully automated driving systems. This paper presents multi-vehicle environment simulation tool to develop and evaluate motorway automated driving systems. The proposed simulation tool consists of following two main parts: surrounding vehicle model and environment sensor model. The surrounding vehicle model is designed to quickly generate rational complex traffic situations of motorway. The environment sensor model depicts uncertainty of environment sensor. As a result, various traffic situations with uncertainty of environment sensor can be proposed by the multi-vehicle environment simulation tool. An application to automated driving system has been conducted. A lane changing algorithm is evaluated by performance indexes from the multi-vehicle environment simulation tool.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.2
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• pp.191-206
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• 2018

In this study, the HAPs(Harzard Air Pollutants) emission factor level of Starex and Grand Carnival was tested using PEMS (Portable Emissions Measurement System) on real road driving. As a result of RDE (Real Driving Emission test), the overall vehicle speed pattern showed similar characteristics. The real-road driving test at constant speed revealed emission factor is inversely proportional relationship to constant speed. Results of accelerating with speed limit on the real-road were shown as followings; Uran (less than 45 km/h)>Rural (<45 km/h, less than 80 km/h)>Motorway (>80 km/h). Moreover, the sudden acceleration and deceleration in driving at high speed was the increasing factor to the HAPs emission factor. This tendency is considered to be influenced by the operating environment on real roads.

• International Journal of Highway Engineering
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• v.19 no.1
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• pp.107-119
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• 2017

PURPOSES : The desire of drivers to increase their driving speeds is increasing in response to the technological advancements in vehicles and roads. Therefore, studies are being conducted to increase the maximum design speed in Korea to 140 km/h. The stopping sight distance (SSD) is an important criterion for acquiring sustained road safety in road design. Moreover, although the perception-reaction time (PRT) is a critical variable in the calculation of the SSD, there are not many current studies on PRT. Prior to increasing the design speed, it is necessary to confirm whether the domestic PRT standard (2.5 s) is applicable to high-speed driving. Thus, in this study, we have investigated the influence of high-speed driving on PRT. METHODS : A driving simulator was used to record the PRT of drivers. A virtual driving map was composed using UC-Win/Road software. Experiments were carried out at speeds of 100, 120, and 140 km/h while assuming the following three driving scenarios according to driver expectation: Expected, Unexpected, and Surprised. Lastly, we analyzed the gaze position of the driver as they drove in the simulated environment using Smarteye. RESULTS : Driving simulator experimental results showed that the PRT of drivers decreased as driving speed increased from 100 km/h to 140 km/h. Furthermore, the gaze position analysis results demonstrated that the decrease in PRT of drivers as the driving speed increased was directly related to their level of concentration. CONCLUSIONS : In the experimental results, 85% of drivers responded within 2.0 s at a driving speed of 140 km/h. Thus, the results obtained here verify that the current domestic standard of 2.5 s can be applied in the highways designated to have 140 km/h maximum speed.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.5
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• pp.656-667
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• 2013

The non-exhaust coarse, fine, and ultrafine particles were characterized by on-road driving measurements using a mobile sampling system. The on-road driving measurements under constant speed driving revealed that mass concentrations of roadway particles (RWPs) were distributed mainly in a size range of 2~3 ${\mu}m$ and slightly increased with increasing vehicle speed. Under braking conditions, the mode diameters of the particles were generally similar with those obtained under constant speed conditions. However, the PM concentrations emitted during braking condition were significantly higher than those produced under normal driving conditions. Higher number concentrations of ultrafine particles smaller than 70 nm were observed during braking conditions, and the number concentration of particles sampled 90 mm above the pavement was 6 times higher than that obtained 40 mm above the pavement. Under cornering conditions, the number concentrations of RWPs sampled 40 mm above the pavement surface were higher than those sampled 90 mm above the pavement. This might be explained that a nucleation burst of a lot of vapor evaporated from the interaction between the tire and the road pavement under braking conditions continuously occurred by cooling during the transport to the sampling height 90 mm, while, for the case of cornering situations, the ultrafine particle formation was completed before the transport to the sampling height of 40 mm.

• Journal of Drive and Control
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• v.19 no.4
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• pp.104-109
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• 2022

For autonomous driving research using AI, datasets collected from road environments play an important role. In other countries, various datasets such as CityScapes, A2D2, and BDD have already been released, but datasets suitable for the domestic road environment still need to be provided. This paper analyzed and verified the dataset reflecting the Korean driving environment. In order to verify the training dataset, the class imbalance was confirmed by comparing the number of pixels and instances of the dataset. A similar A2D2 dataset was trained with the same deep learning model, ConvNeXt, to compare and verify the constructed dataset. IoU was compared for the same class between two datasets with ConvNeXt and mIoU was compared. In this paper, it was confirmed that the collected dataset reflecting the driving environment of Korea is suitable for learning.

• Journal of the Korean Society of Safety
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• v.24 no.5
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• pp.101-112
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• 2009

Recently, with the existing speed-management by law enforcements and physical speed-reduction facilities, the country newly adopted Driver Feedback Sign (DFS) system, which displays driving speed in order to guide the driver to an advisable driving condition. DFS is mainly used in school zones due to reasons related to the ITS. Accordingly, because it is predictable that DFS will result in speed-reduction without legal forces and would have an effect on physical speed-reduction facilities, intersection, crosswalks, and road-alignments, this study will try to verify the efficiency of DFS by researching the vehicle speed in national highways and school zones, which have similar conditions to the urban arterial road. In consequence, on national highways, the drivers had a tendency to travel according to the road-environment such as urban arterial road and not reduce speed voluntarily. In school zones, drivers tend to reduce speed in mornings and afternoons when children travel to school or home, showing that the resulting effect is different according to the road-environment where DFS is installed, and the time slot of the DFS.