PURPOSES : The percentage of vehicle overturning accidents is 16.3% of vehicle alone fatal accidents, with a fatality rate of 9.0%, accounting for a high proportion, and heavy vehicles with a high center of gravity are vulnerable to overturning accidents. In the standard guidelines of Super-Bus Rapid Transit(S-BRT), it is recommended to install physical facilities that separate buses from other traffic on dedicated bus ways, and lane separation facilities are being developed. To develop low-profile lane separation facilities that do not interfere with sight obstruction for pedestrians and drivers, it is necessary to review the height of lane separation facilities to prevent overturning crashes of heavy vehicles. METHODS : Heavy vehicle impact conditions of 8ton-55km/h-15°, 8ton-55km/h-20°, 8ton-65km/h-15°, and 8ton-65km/h-20°were applied to compare the vehicle behavior by the height of lane separation facilities using LS-DYNA, a three-dimensional nonlinear impact analysis program based on speed and angle changes. In addition, the behavior of the vehicle after the collision was analyzed to examine the impact conditions in which an overturning crash occurs when a heavy vehicle collides with a low-profile lane separation facility and the appropriate height of the facility to prevent overturning. RESULTS : In general, under the 8ton-65km/h-15°condition, which is a heavy vehicle impact condition used in the performance standard of the barrier, the vehicle’s behavior after the collision was stable as the height of the lane separation facility increased. CONCLUSIONS : Therefore, when the impact conditions were 8ton-65km/h-15°or less, it was determined that the appropriate height to prevent the condition of the lane separation facility was 400mm or more.

목적 : 본 연구는 노인운전자를 대상으로 한 운전 관련 기능 및 차량 적합성을 평가한 사례를 보고하고 노인운전자의 안전운전에 필요한 중재의 방향성을 알아보고자 하였다. 연구방법 : 경기 남부 지역의 지역사회 거주 중인 노인 7명을 대상으로 선정하였다. 자료를 수집하기 위해 노인운 전자의 신체기능(인지, 운동. 감각 기능)을 평가하고 운전행동에 대한 자기 보고, 차량조절 프로그램(Carfit)을 이 용한 운전자-차량 적합성 평가를 실시하였다. 결과 : 7명의 대상자 중 3명에서 최근 1년 내 범칙금 부과와 교통사고 경험이 보고되었다. 신체기능에서는 MMSE-DS 검사와 관절가동범위 검사 결과가 모두 정상이었다. 반면 시계그리기 검사(CDT)에서 2명(28.6%), 선 로잇기 검사(TMT-B)에서 4명(57.1%)의 대상자가 어려움을 보였다. 시야검사 결과 7명의 대상자(100%) 모두 정상 보다 저하된 시야범위를 갖고 있었으며, 차량적합성 평가(Carfit)에서도 모든 대상자(100%)에서 하나 이상의 차량 적합성 문제가 나타났다. 결론 : 운전은 지역사회 참여의 수단으로 큰 의미를 가진다. 저하된 운전 관련 신체기능, 부적절한 운전행동 및 차량 적합성은 노인의 안전운전을 위협할 수 있다. 따라서 운전행동 및 차량 조절에 관련된 운전 중재가 필요하며, 본 연구에서는 제시된 평가 사례는 노인의 안전운전 중재 계획에 필요한 자료를 확보하는 방법으로 활용될 수 있다. 향후 노인운전자와 이들의 운전에 대한 자료 수집을 위한 확대 연구 및 중재 실천에 대한 연구가 시행되어야 한다.

Combat vehicles were developed in two types depending on the purpose of operation. The body of the vehicle is relatively large welded structure, and there is a quality problem in that welding deformation occurs in a certain area. Welding deformation is a problem that is inevitably generated during the welding process, and additional corrective work is performed. However, due to the structural characteristics of the welded structure of combat vehicl, there are many limitations in the correction method, which lowered the productivity. To improve this problem, we intend to establish improvement measures through analysis of the areas where welding deformation occurs and apply corrective measures suitable for welding structures of combat vehicles. For the design improvement plans, the finite element analysis were used and the correction method were applied to simulated structure to confirm the possibility. Finally, the improvement effect was confirmed by manufacturing the actual welded structure. Based on the results of this study, it is expected that the productivity of related similar welding structures as well as structures of wheeled armored vehicles, can be improved.

This paper intends to consider the contents of the insufficient certification system of PAV and to propose the certification regulations. In other words, PAV is a combination of people and drones that should reflect commercial specifications among aviation certification specifications and civil aircraft regulations. However, the domestic PAV certification system is still lacking in clear detailed regulations, and the detailed certification system of PAV should be established as soon as possible. It is hoped that PAV certification system will be established first and applied in earnest in the future, and that the domestic PAV industry will be activated.

Manned-unmanned teaming can be a very promising air-to-air combat tactic since it can maximize the advantage of combining human insight with the robustness of the machine. The rapid advances in artificial intelligence and autonomous control technology will speed up the development of manned-unmanned teaming air-to-air combat system. In this paper, we introduce a manned-unmanned teaming air-to-air combat tactic which is composed of a manned aircraft and an UAV. In this tactic, a manned aircraft equipped with radar is functioning both as a sensor to detect the hostile aircraft and as a controller to direct the UAV to engage the hostile aircraft. The UAV equipped with missiles is functioning as an actor to engage the hostile aircraft. We also developed a combat scenario of executing this tactic where the manned-unmanned teaming is engaging a hostile aircraft. The hostile aircraft is equipped with both missiles and radar. To demonstrate the efficiency of the tactic, we run the simulation of the scenario of the tactic. Using the simulation, we found the optimal formation and maneuver for the manned-unmanned teaming where the manned-unmanned teaming can survive while the hostile aircraft is shot-downed. The result of this study can provide an insight to how manned aircraft can collaborate with UAV to carry out air-to-air combat missions.

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

PURPOSES : Accidents involving autonomous vehicle continue to occur. However, research on autonomous vehicle monitoring has been insufficient. The purpose of this study is to develop monitoring indicators from the perspective of vehicles and road infrastructure for the safe driving of autonomous vehicles. In addition, the purpose is to monitor autonomous vehicles and road environments using the monitoring indicators developed, as well as to analyze the characteristics of road sections where autonomous vehicles exhibit abnormalities. METHODS : Data from Pangyo Zero Shuttle, an autonomous vehicle, were used in this study. Infrastructure data installed in Pangyo Zero City were used. The data were collected from June 2019 to July 2019, during the normal driving period of the zero shuttle. The five monitoring indicators were developed by combining the vehicle operation information table collected from the V2X device of the zero shuttle and the road environment monitoring detail table collected from the infrastructure data with the road section table. In addition, an analysis of road characteristics with frequent errors is performed for each monitoring indicator. RESULTS : The three monitoring indicators from the perspective of the vehicle allowed monitoring of the sensor error, sensor communication error, and yaw rate error of the autonomous vehicle's timing and road section. In addition, the two monitoring indicators from the infrastructure perspective enabled the monitoring of events and road surface conditions on roads where autonomous vehicles drive. As a result of analyzing the road characteristics that frequently caused errors by monitoring indicators, sensor errors frequently occurred in the section waiting to enter the left-turn lane. Sensor communication errors are left-turn standby and have occurred frequently on road sections where U-turns are allowed. Finally, yaw rate error occurred frequently in sections of roads where there were no induction lines or where changes to lanes occurred frequently. CONCLUSIONS : The five monitoring indicators developed in this study allowed the monitoring of autonomous vehicles and roads. The results of this study are expected to help the safe driving of autonomous vehicles and contribute to the detection of autonomous driving abnormalities and the provision of real-time road condition information through further analysis.

PURPOSES : This study introduces the use of instantaneous speed collected by in-vehicle sensing devices for space mean speed and time mean speed estimation. METHODS : Using probe vehicles’ instantaneous speed data and GPS location at each designated time interval enables analysts to calculate the link-based space mean speed and time mean speed for a certain time-space domain directly. This study proves the equations mathematically and compares the results of time mean speed using Wardrop’s equation and section average speed by trips. RESULTS : This study introduces the concept of link-based average speed using both time mean and space mean speed definitions. First, an unbiased space mean speed was explained for a roadway section can be calculated from high-resolution vehicle trajectory data without necessarily continuously tracking a vehicle. Second, this study proves that the average section speed by occupancy resulted in identical values to the true space mean speed formulation by Wardrop. In addition, the average section speed by trip was nearly identical to the time mean speed. CONCLUSIONS : Each section on a signalized intersection corridor yielded various speed distributions whose parameters varied as measurements were taken closer to the intersection proper. By exploring the space mean speed with speed distribution, the intersection impact area can be defined more accurately.

PURPOSES : The evaluation of the road pavement thickness in an apartment complex through a methodology for estimating the number of construction vehicles is presented. We also show the usability of the new design criteria. METHODS : A field survey plan was established by conducting literature and preliminary surveys, and the structural performance of each section of the road pavement was compared through a structural analysis program. The load on construction vehicles is required for analyzing the ESAL factors of construction vehicles; it is calculated as a methodology for estimating construction vehicles because it is difficult to measure the traffic volume through visual inspection. Accordingly, the extent of damage predicted by road pavement sections in apartment complexes was analyzed to examine the possibility of applying the methodology for estimating the number of construction vehicles. RESULTS : If the road crack exceeds 20% as a result of the site survey of a previous study, structural damage to the road pavement occurs from construction vehicles. Compared to the actual traffic volume observation, an error of 5% took place; therefore, it is necessary to perform a design such that the damage caused by the load of the construction vehicle be 15% or less for proper road performance. Structural analysis through the KENPAVE program showed that the damage caused by the load of the construction vehicle exceeded 20% in all sections except section B (with a thickness of 46 cm). CONCLUSIONS : Owing to the limitations of the test construction section and environmental conditions, it is necessary to assess the effects of various variables by conducting an analysis. This is expected to be applied as a design basis considering more reliable construction vehicles.

자율주행 5단계(mind-off)에서는 운전에서 해방된 탑승자가 차량 내에서 대면 대화, 업무, 휴식, 영화 감상 등의 다양한 활동이 될 것으로 예상된다. 특히 자동차 실내 공간의 다양한 변화가 예상된다. 또한 미국자동차협회(American Automobile Association)가 시행한 조사에서 73%가 자율주행 자동차에 탑승하는 것이 두렵다고 응답하였고, 자율주행 5단계에서는 안전의 주체가 자율주행자동차로 이양이 예상되므로 사용자 경험 관점에서 연구가 이루어져야 한다. 최근 완전자율주행자동차의 안전성 확보에 관한 다양한 연구가 이뤄지고 있으나 실제 탑승자의 심리적 안전성 확보 관점에서의 연구는 부족한 실정이다. 이에 본 연구는 AHP 분석 기법에 기반하여 설문조사를 진행하였다. 그 결과 각 실내 행위 유형에 따라 탑승자의 심리적 안전성 확보를 위한 자동차 안전장치의 우선순위를 도출하였고 도출된 결과를 기반으로 탑승자의 심리적 안전성을 확보를 위한 실내 공간을 제시하였다. 본 연구는 탑승자의 심리적 안전성을 충족하는 실내공간 설계를 위한 방향성을 제시한 것에 의의가 있으며, 이를 바탕으로 사용자의 심리적 안전성 확보를 위한 완전 자율주행 자동차 실내 환경 조성이 이루어질 것으로 기대한다.

The emission of particulate matter and volatile organic compounds (VOCs) from a motor vehicle painting booth was quantitatively evaluated. Most particulate matter was emitted during the spraying process, in which the PM10 concentration was 16.5 times higher than that of the drying process. When the paint was being sprayed, the particles with a diameter of 1.0~2.5 μm accounted for 39.4% and particles greater than 2.5 μm in diameter accounted for 30.6% of total particles. On the other hand, small particles less than 0.5 μm in diameter accounted for 52.4% of total particles during the drying process. In contrast to the particulate matter, high concentrations of VOCs were emitted during both spraying and drying processes. Butyl acetate, xylene, toluene, and m-ethyltoluene were the most abundant VOCs emitted from the motor vehicle painting booth. Additionally, xylene, butyl acetate, toluene, and 1,2,3-trimethylbenzene were the dominant ozone precursors. Especially, xylene exhibited the highest ozone production contribution (32.5~44.4%) among 34 species of the ozone precursors. The information obtained in this study can be used to establish a suitable management strategy for air pollutants from motor vehicle painting booths.

PURPOSES : This study uses deep learning image classification models and vehicle-mounted cameras to detect types of pavement distress — such as potholes, spalling, punch-outs, and patching damage — which require urgent maintenance. METHODS : For the automatic detection of pavement distress, the optimal mount location on a vehicle for a regular action camera was first determined. Using the orthogonal projection of obliquely captured surface images, morphological operations, and multi-blob image processing, candidate distressed pavement images were extracted from road surface images of a 16,036 km in-lane distance. Next, the distressed pavement images classified by experts were trained and tested for evaluation by three deep learning convolutional neural network (CNN) models: GoogLeNet, AlexNet, and VGGNet. The CNN models were image classification tools used to identify and extract the combined features of the target images via deep layers. Here, a data augmentation technique was applied to produce big distress data for training. Third, the dimensions of the detected distressed pavement patches were computed to estimate the quantity of repair materials needed. RESULTS : It was found that installing cameras 1.8 m above the ground on the exterior rear of the vehicle could provide clear pavement surface images with a resolution of 1 cm per pixel. The sensitivity analysis results of the trained GoogLeNet, AlexNet, and VGGNet models were 93 %, 86 %, and 72 %, respectively, compared to 62.7 % for the dimensional computation. Following readjustment of the image categories in the GoogLeNet model, distress detection sensitivity increased to 94.6 %. CONCLUSIONS : These findings support urgent maintenance by sending the detected distressed pavement images with the dimensions of the distressed patches and GPS coordinates to local maintenance offices in real-time.