• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.81-90
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• 2016

As an autonomous driving vehicle (AV) need to cope with external road conditions by itself, its perception performance for road environment should be better than that of a human driver. A vision sensor, one of AV sensors, performs lane detection function to percept road environment for performing safe vehicle steering, which relates to define vehicle heading and lane departure prevention. Performance standards for a vision sensor in an ADAS(Advanced Driver Assistance System) focus on the function of 'driver assistance', not on the perception of 'independent situation'. So the performance requirements for a vision sensor in AV may different from those in an ADAS. In assuming that an AV keep previous steering due to lane detection failure, this study calculated lane departure distances between the AV location following curved road alignment and the other one driving to the straight in a curved section. We analysed lane departure distance and time with respect to the allowance of lane detection malfunction of an AV vision sensor. With the results, we found that an AV would encounter a critical lane departure situation if a vision sensor loses lane detection over 1 second. Therefore, it is concluded that the performance standards for an AV should contain more severe lane departure situations than those of an ADAS.

• International Journal of Automotive Technology
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• v.7 no.1
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• pp.25-34
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• 2006

For decades, simulation technique has been well validated in areas such as computer and communication systems. Recently, the technique has been much used in the area of transportation and traffic forecasting. Several methods have been proposed for investigating complex traffic flows. However, the dynamics of vehicles and diversities of driver characteristics have never been considered sufficiently in these methods, although they are considered important factors in traffic flow analysis. In this paper, we propose a traffic simulation tool called Multi-Agent for Traffic Simulation with Vehicle Dynamics Model (MATDYMO). Road transport consultants, traffic engineers and urban traffic control center managers are expected to use MATDYMO to efficiently simulate traffic flow. MATDYMO has four sub systems: the road management system, the vehicle motion control system, the driver management system, and the integration control system. The road management system simulates traffic flow for various traffic environments (e.g., multi-lane roads, nodes, virtual lanes, and signals); the vehicle motion control system constructs the vehicle agent by using various vehicle dynamic models; the driver management system constructs the driver agent capable of having different driving styles; and lastly, the integrated control system regulates the MATDYMO as a whole and observes the agents running in the system. The vehicle motion control system and driver management system are described in the companion paper. An interrupted and uninterrupted flow model were simulated, and the simulation results were verified by comparing them with the results from a commercial software, TRANSYT-7F. The simulation result of the uninterrupted flow model showed that the driver agent displayed human-like behavior ranging from slow and careful driving to fast and aggressive driving. The simulation of the interrupted flow model was implemented as two cases. The first case analyzed traffic flow as the traffic signals changed at different intervals and as the turning traffic volume changed. Second case analyzed the traffic flow as the traffic signals changed at different intervals and as the road length changed. The simulation results of the interrupted flow model showed that the close relationship between traffic state change and traffic signal interval.

• Smart Media Journal
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• v.9 no.4
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• pp.9-16
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• 2020

Recently, various studies are being conducted to integrate Image Segmentation into smart factory industries and autonomous driving fields. In particular, Image Segmentation systems using deep learning algorithms have been researched and developed enough to learn from large volumes of data with higher accuracy. In order to use image segmentation in the autonomous driving sector, sufficient amount of learning is needed with large amounts of data and the streaming environment that processes drivers' data in real time is important for the accuracy of safe operation through highways and child protection zones. Therefore, we proposed a novel DFCN algorithm that enhanced existing FCN algorithms that could be applied to various road environments, demonstrated that the performance of the DFCN algorithm improved 1.3% in terms of "loss" value compared to the previous FCN algorithms. Moreover, the proposed DFCN algorithm was applied to the existing U-Net algorithm to maintain the information of frequencies in the image to produce better results, resulting in a better performance than the classical FCN algorithm in the autonomous environment.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.47-55
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• 2011

The purpose of this study was to determine the legibility distance for traffic road sign and traffic sign, fixation duration and number of fixation during the time of recognition of traffic road signs under different vision conditions. This experiment was conducted on a closed-road circuit which has realistic driving road and environment Each participant drove the real vehicle for the experiment and specially built traffic road sign for the experiment and traffic road signs on the side of closed-road circuit were used. Different vision conditions were simulated using spectacle lenses to reach visual acuity 1.0 and 0.8 and it was 1.2 without spectacles and each participant tested under 3 vision conditions.. The result of this study demonstrated that there was a significant difference on legibility distance between visual acuity of 1.2 and 0.8 and there were also significant difference on fixation duration and number of fixations with smaller traffic signs. This study demonstrated the importance of vision correction for driving at night-time, also showed there would be difference on legibility distance and efficiency of eye movement such as fixation duration and number of fixation despite of satisfied visual acuity for driver's license requirement.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.3
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• pp.287-296
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• 2017

Although emission regulations have been gradually strengthened in the past decade, the road transport section remains the most important source of NOx emission in air pollution. One reason is that there has been an increase in the proportion of diesel vehicles and in the volume of traffic. In addition, the certification procedure for standard emission limit does not sufficiently reflect real traffic condition and various driving patterns. Therefore, the European Commission(EC) has recently come up with the RDE-LDV(Real driving emissions-light duty vehicle) regulations, and the Ministry of Environment in Korea has been conducting research on evaluating RDE-LDV with PEMS(Portable Emission Measurement Systems). According to the trip requirements of the 2nd RDE package announced by the EC, the objectives of the present study include the development of Korean RDE routes to reflect domestic traffic and road conditions. Based on the results, both RDE routes are in correct compliance with RDE-LDV regulations, including trip requirements and trip dynamics. KOR-NIER Route 1, in particular, has a higher driving load in rural driving with regard to excessive gradient of elevation compared to KOR-NIER Route 2, including relatively plane rural driving.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.195-211
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• 2023

For the commercialization For the commercialization of autonomous vehicles (AV), the operational design domain (ODD) of automated driving systems (ADS) is to be clearly defined. A common language and consistent format must be prepared so that AV-related stakeholders can understand ODD at the same level. Therefore, overseas countries are presenting a standardized ODD framework and developing scenarios that can evaluate ADS-specific functions based on ODD. However, ODD includes conditions reflecting the characteristics of each country, such as road environment, weather environment, and traffic environment. Thus, it is necessary to clearly understand the meaning of the items defined overseas and to harmonize them to reflect the specific domestic conditions. Therefore, in this study, domestic optimization of the ODD classification system was performed by analyzing the domestic driving environment based on international standards. The driving environment of currently operating self-driving car test districts (Sangam, Seoul, and Gwangju) was investigated using the developed domestic ODD items. Then, based on the results obtained, the ranges of the ODDs in each test district were determined and compared.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.1
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• pp.35-45
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• 2014

The driving environment of the vehicle has characteristics that the change of position, velocity and distance between vehicles is severe. The performance test of system must be carried out in the real road environments with consideration of the driving environment of vehicles to measure the performance correctly because the performance of vehicular communication systems is affected by the driving conditions of the vehicle. In this paper, we propose the test methods of V2V/V2I performance and V2V multi-hop transmission function and present the test results measured by the vehicular communication systems already developed. In the test result, we confirmed the fact that the distance of communication devices and the driving direction of vehicle are affecting the communication performance. We also confirmed the multi-hop transmission function using the driving vehicle in the limited area as the proposed test method.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.6
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• pp.745-756
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• 2013

The purpose of this study is to evaluate representativeness of fuel-based emission factors. Twelve light-duty diesel vehicles which meet Euro-3 to 5 legislative emission limits were selected for emission tests. Second-by-second modal emission rates of vehicles were measured on a standard laboratory chassis dynamometer system. An off-cycle driving cycle was developed as a representative Korean real-world on-road driving cycle. Fuel-based emission factors were developed for short trip segments that involved in the selected driving cycle. Each segment was defined to have unit travel distance, which is 1 km, and characterized by its average speed and Relative Positive Acceleration (RPA). Fuel-based $NO_x$ emission factors demonstrate relatively good representativeness in terms of vehicle operation conditions. $NO_x$ emission factors are estimated to be within ${\pm}20%$ of area-wide emission factor under more than 40% of total driving situations. This result implies that the fuel-based $NO_x$ emission factor could be practically implemented into the on-road emission management strategies, such as a remote sensing device (RSD). High emitting vehicles as well as high emitting operating conditions heavily affect on the mean values and distributions of CO and THC emission factors. Few high emitting conditions are pulling up the mean value and biasing the distributions, which weaken representativeness of fuel-based CO and THC emission factors.

• Journal of the Korean Society of Safety
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• v.21 no.2 s.74
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• pp.128-137
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• 2006

Traffic accidents occur due to complex influences of transportation companies, drivers, and road environment. This study found that education and surroundings for transportation companies, driving habits of drivers, and road safety facilities and accessory facilities were main factors that affected strongly traffic accidents. Also, it found that driving habits of drivers could affect accidents heavily more than two other factors. Road safety and accessory facilities were analysed that they affected traffic accidents independently with transportation companies and their own drivers. Therefore, in order to achieve a traffic accident prevention as our main target, those companies need to produce atmosphere that their own drivers can have safety awareness, and related institutions for the above target should run parallel with policy assistance and strict traffic enforcement. In the end, this study suggests that transportation companies should secure manpower wholly being charged with traffic safety and financial resources investing in it.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.249-257
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• 2018

The purpose of this study is to construct a virtual reality simulation environment for searching and evaluating evaluation criteria for road safety facilities with new technologies. Virtual reality simulation requires high realism and accurate behavior data extraction. To do this, we used the Unreal Engine to create the environment by dividing it into an external environment and a vehicle environment. After that, a sample simulation for the luminescent road markers and preliminary experiments were conducted. As a result, luminescent road markers showed better 5m interval than 10m interval. It can be confirmed that it can be used in the simulation for searching the evaluation criteria for the new road safety facilities that incorporate the new technology in the future. In the future, it will be possible to simulate various environments by adding modeling and sample components for other road facilities.