• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.3
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• pp.65-76
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• 2000

To measure the fuel consumption rate of a vehicle, a car is tested on chassis dynamometer following given driving mode. But the fuel consumption rate measured by this method may be somewhat different from that measured in on-road driving conditions. It may be due to not considering road grade in driving modes. In this study, new driving modes which include road grade are proposed, and the simulation program to estimate the real driving fuel consumption rate of a vehicle is developed. On-road car tests to verify the simulation program are carried out and the results of the simulation are analysed and compared with those of the experiments.

• International Journal of Highway Engineering
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• v.17 no.2
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• pp.107-122
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• 2015

PURPOSES : The use of virtual driving tests to determine actual road driving behavior is increasing. However, the results indicate a gap between real and virtual driving under same road conditions road based on ergonomic factors, such as anxiety and speed. In the future, the use of virtual driving tests is expected to increase. For this reason, the purpose of this study is to analyze the gap between real and virtual driving on same road conditions and to use a calibration formula to allow for higher reliability of virtual driving tests. METHODS : An intelligent driving recorder was used to capture real driving. A driving simulator was used to record virtual driving. Additionally, a virtual driving map was made with the UC-Win/Road software. We gathered data including geometric structure information, driving information, driver information, and road operation information for real driving and virtual driving on the same road conditions. In this study we investigated a range of gaps, driving speeds, and lateral positions, and introduced a calibration formula to the virtual record to achieve the same record as the real driving situation by applying the effects of the main causes of discrepancy between the two (driving speed and lateral position) using a linear regression model. RESULTS: In the virtual driving test, driving speed and lateral position were determined to be higher and bigger than in the real Driving test, respectively. Additionally, the virtual driving test reduces the concentration, anxiety, and reality when compared to the real driving test. The formula includes four variables to produce the calibration: tangent driving speed, curve driving speed, tangent lateral position, and curve lateral position. However, the tangent lateral position was excluded because it was not statistically significant. CONCLUSIONS: The results of analyzing the formula from MPB (mean prediction bias), MAD (mean absolute deviation) is after applying the formula to the virtual driving test, similar to the real driving test so that the formula works. Because this study was conducted on a national, two-way road, the road speed limit was 80 km/h, and the lane width was 3.0-3.5 m. It works in the same condition road restrictively.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.1
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• pp.48-55
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• 2009

In order to keep safe driving conditions under road networks, there are several formations such as road structure, road surface condition, traffic occupancy and supplement of an accurate information of traffic status ahead To support safe-driving on each road formation, each formation is supplied with various information to help the driver. However, in some cases like rapid status change at local-scale geography, traffic information systems often displays insufficient information because of the lack of information correlation. In order to accurately aware the driver, all road formation must be in sync. It is important to supply accurate information to the driver because this information directly impacts the drivers on the road. This paper discusses the amber information to keep the least safety driving over road formations including tunnels and bridges. This paper also will propose the informations for safe-driving conditions, information linkage on the road and rule-base safety information, as ITS technology, being displayed for all drivers under the worst weather conditions.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.15-28
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• 2016

The purpose of this study is to prepare countermeasures for aggressive driving and road rage which have recently become a hot issue by analysing mechanism of how the driving behavior determinants(personal anger and aggression) and dangerous driving behavior factors(aggressive driving behavior and over-speeding driving behavior, drunk driving behavior, inattentive driving behavior, and inexperience driving behavior) affect aggressive driving and road rage. From the survey conducted by seven branches of the Road Traffic Authority with 351 people who were traffic offenders and drivers who caused car accidents, this study obtained three results as follows. First, seriousness of aggressive driving and road rage and requirements as types of customized educations, proper length of time for education, and contents of education can be understood. Second, specific relation and mechanism between the driving behavior determinants and dangerous driving behavior factors with respect to aggressive driving and road rage can be clearly identified, which helps to set order of priority and weighting of measures for reducing aggressive driving and road rage. Third, countermeasures can be categorized as corporate measures or customized measures through mechanism analysis model of aggressive driving and road rage.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.389-395
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• 2022

In order to ensure reliability the high-level automated driving such as Advanced Driver Assistance System (ADAS) and universal robot taxi provided by autonomous driving systems, the operation with high integrity must be generated within the defined Operation Design Domain (ODD). For this, the position and posture accuracy requirements of autonomous driving systems based on the safety driving requirements for autonomous vehicles and domestic road geometry standard are necessarily demanded. This paper presents localization requirements for safe road driving of autonomous ground vehicles based on the requirements of the positioning system installed on autonomous vehicle systems, the domestic road geometry standard and the dimensions of the vehicle to be designed. Based on this, 4 Protection Levels (PLs) such as longitudinal, lateral, vertical PLs, and attitude PL are calculated. The calculated results reveal that the PLs are more strict to urban roads than highways. The defined requirements can be used as a basis for guaranteeing the minimum reliability of the designed autonomous driving system on roads.

• International Journal of Automotive Technology
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• v.8 no.3
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• pp.375-382
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• 2007

The multi-driver off-road vehicle drive-line consists of many components, with close connections among them. In order to design and analyze the drive-line efficiently, a modular methodology should be taken. The aim of a modular approach to the modeling of complex systems is to support behavior analysis and simulation in an iterative and thus complex engineering process, by using encapsulated submodels of components and of their interfaces. Multi-driver off-road vehicles are comparatively complicated. The driving-line is an important core part to the vehicle, it has a significant contribution to the performance. Multi-driver off-road vehicles have complex driving-lines, so performance is heavily dependent on the driving-line. A typical off-road vehicle's driving-line system consists of a torque converter, transmission, transfer case and driving-axles, which transfers the power generated by the engine and distributes it effectively to the driving wheels according to the road condition. According to its main function, this paper proposes a modularized approach for design and evaluation of the vehicle's driving-line. It can be used to effectively estimate the performance of the driving-line during the concept design stage. Through an appropriate analysis and assessment method, an optimal design can be reached. This method has been applied to practical vehicle design, it can improve the design efficiency and is convenient to assess and validate the performance of a vehicle, especially of multi-driver off-road vehicles.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.87-94
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• 2015

This paper proposes a situation awareness method based on data fusion and independent objects for autonomous driving in on-road environment. The proposed method, designed to achieve an accurate analysis of driving situations in on-road environment, executes preprocessing tasks that include coordinate transformations, data filtering, and data fusion and independent object based situation assessment to evaluate the collision risks of driving situations and calculate a desired velocity. The method was implemented in an open-source robot operating system called ROS and tested on a closed road with other vehicles. It performed successfully in several scenarios similar to a real road environment.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.38-44
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• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• Therapeutic Science for Rehabilitation
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• v.5 no.2
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• pp.35-47
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• 2016

Objective: The aim of this study was to identify the driving rehabilitation for on-road driving through a systematic review. Methods: We systematically examined papers published in journals from December 2014 to January 2015, using CINAH, Embase, Pubmed, PsycINFO, and The Cochrane Library. Eventually, 15 studies were included in the analyses. Results: The evidence of 15 studies was from levels I, III, and V. The subjects included in the analyses were patients with stroke(40.0%), older driver(20.0%), traumatic brain injury(20.0%), acquired brain injury(13.3%) and spinal cord injury(6.7%). The intervention types were driving simulator training(53.3%), cognitive skills training(26.6%), off-road educational training(6.7%), adaptation of assistive device(6.7%), and behind-the-wheel training(6.7%). The effects of driving rehabilitation were different depending on the types of intervention. However, driving simulator training showed significant improvement of on-road assessments in all studies included this study. Conclusions: Driving rehabilitation for on-road driving has been used in various types. Specially, the effect of the driving simulator training has been proved by many studies. Future studies are to be required with client from a range of diagnostic groups to establish evidence-based interventions and determine their effectiveness in improving on-road driving.

• Safety and Health at Work
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• v.7 no.1
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• pp.38-42
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• 2016

Background: Truck driving is known as one of the occupations with the highest accident rate. This study investigates the characteristics of traffic collisions according to road types (expressway and rural road). Methods: Classifying 267 accidents into expressway and rural road, we analyzed them based on driver characteristics (age, working experience, size of employment), time characteristics (day of accident, time, weather), and accident characteristics (accident causes, accident locations, accident types, driving conditions). Results: When we compared the accidents by road conditions, no differences were found between the driver characteristics. However, from the accident characteristics, the injured person distributions were different by the road conditions. In particular, driving while drowsy is shown to be highly related with the accident characteristics. Conclusion: This study can be used as a guideline and a base line to develop a plan of action to prevent traffic accidents. It can also help to prepare formal regulations about a truck driver's vehicle maintenance and driving attitude for a precaution on road accidents.