Accurate estimation of vehicle exhaust emissions at urban intersections is essential to assess environmental impacts and support sustainable traffic management. Traditional emission models often rely on aggregated traffic volumes or measures of average speed that fail to capture the dynamic behaviors of vehicles such as acceleration, deceleration, and idling. This study presents a methodology that leverages video data from smart intersections to estimate vehicle emissions at microscale and in real time. Using a CenterNet-based object detection and tracking framework, vehicle trajectories, speeds, and classifications were extracted with high precision. A structured preprocessing pipeline was applied to correct noise, missing frames, and classification inconsistencies to ensure reliable time-series inputs. Subsequently, a lightweight emission model integrating vehicle-specific coefficients was employed to estimate major pollutants including CO and NOx at a framelevel resolution. The proposed algorithm was validated using real-world video data from a smart intersection in Hwaseong, Korea, and the results indicated significant improvements in accuracy compared to conventional approaches based on average speed. In particular, the model reflected variations in emissions effectively under congested conditions and thus captured the elevated impact of frequent stopand- go patterns. Beyond technical performance, these results demonstrate that traffic video data, which have traditionally been limited to flow monitoring and safety analysis, can be extended to practical environmental evaluation. The proposed algorithm offers a scalable and cost-effective tool for urban air quality management, which enables policymakers and practitioners to link traffic operations with emission outcomes in a quantifiable manner.

This study presents a truck classification method using panoramic side-view images to meet the Ministry of Land, Infrastructure and Transport’s 12-category standard (types 4–12). The system captures a vehicle’s full side profile via a panoramic imaging device, ensuring complete wheel visibility. A YOLOv12-based deep learning model detects wheels, and image processing extracts their center coordinates. Pixel distances between adjacent wheels are calculated and normalized to determine axle spacing patterns, which, together with wheel count, are applied to a rule-based classifier. Tests on 1,200 real-world panoramic truck images (1,000 for training, 200 for testing) achieved a mean average precision of 96.1% for wheel detection and 90.5% overall classification accuracy. The method offers explainable classification through measurable structural features, supporting applications in smart tolling, road usage billing, overloading enforcement, and autonomous vehicle perception.

The center bearing of the propeller shaft in the KM-Sam mounted vehicle is a component that requires high durability due to high-speed rotation and it must exhibit strong resistance to vibration and strees. However some Republic of Korea Air Force (ROKAF) units have experienced failures where the center bearing bracket breaks, leading th the detachment of the propeller shaft assembly. This issue has only occurred with domestically developed center bearings, A root cause analysis confirmed excessive second-order components and stress, Therefore improved results were derived through comparative testing with imported parts, and the effectivenss was verified by applying them to actual vehicles.

Current portable reference equipment used to evaluate the performance of vehicle detectors can collect traffic volume and speed only for the outermost lanes in each direction. Passing vehicles on the other lanes are manually counted by reviewing the recorded videos. Consequently, only traffic volume—without vehicle speed—is used as a reference value. This method is time-consuming for comparing the performance data from the equipment with the reference data and can compromise the performance evaluation. This study aims to enhance the efficiency of vehicle detection system (VDS) performance evaluations by developing multilane portable reference equipment that can accurately collect traffic information for lanes beyond the outermost lane or for more than two lanes. This study introduced the core technologies of multilane portable reference equipment and compared and analyzed the measurement accuracy of the developed equipment against data from fixed reference equipment operated by the Intelligent Transportation System (ITS) Certification and Performance Evaluation Center, following ITS performance evaluation criteria. The data from the fixed reference equipment were considered the true values, providing a basis for evaluating the accuracy of the measurements by the developed equipment. First, the accuracy of the vehicle length was determined by driving four test vehicles, each measuring 7,085 mm in length, 24–29 times in each lane. The accuracy was calculated by comparing the vehicle length data obtained from the fixed reference equipment with the actual vehicle length. A confidence interval was established for this accuracy. To assess the accuracy of the speed and occupancy time in relation to the accuracy of the analyzed vehicle length, we evaluated the error range of the vehicle length according to variations in speed and occupancy time. This analysis was based on the following relationship equation: “vehicle length = speed × occupied time – sensor spacing.” The analysis used data from approximately 16,000 vehicles, including the speed, occupancy time, and vehicle length, collected between 8:00 am and 12:00 pm on August 8, 2024. The principle behind measuring traffic volume and vehicle speed using multilane portable reference equipment involves detecting a vehicle by analyzing the time difference between the driver and passenger tires. The vehicle speed was calculated using the installation angle of the tire detection sensor and trigonometric functions. An analysis of the measurement accuracy revealed that the traffic volume accuracy of the outermost lane (the fourth lane) was 100% during both day and night. The speed accuracy was 98.8% during the day and 97.7% at night, representing the highest performance in these metrics. Additionally, the traffic volume accuracy for the innermost lane (the first lane), as measured by the detection sensor from the third lane, was more than 99.3% at all times, with a speed accuracy exceeding 96% during the day and night, that also demonstrated excellent results. The analysis results indicated that the multilane portable reference equipment developed in this study was suitable for evaluating the VDS performance. This equipment allowed the collection of traffic volume and speed data from all lanes, rather than only the outermost lanes. This capability enabled consistent analysis for each lane and enhanced efficiency by reducing the analysis time. Additionally, this is expected to improve the reliability of the performance evaluations.

In this study, the design of shock tower mounting, a type of shock absorber mounting for four-wheel drive vehicles, was addressed through structural analysis. In the case of existing shock tower mounting components, cracks occurred in the shock tower frame side weld joints, so the maximum stress should be reduced to extend the life of the designed components. Based on this, various design changes were performed on the shock tower mounting components, and the maximum stress generated through structural analysis of each design change model was compared. For the structural analysis, a load of 40,000 N was applied in the axial direction of the shock absorber, and the results were relatively analyzed and compared. As a result of the analysis of the shock tower mounting components through the design change, Case 3, a model that alleviated the stress concentration applied to the body mounting, increased the strength compared to the existing model, and the stress in the shock tower frame side weld joints was reduced by 16.3%.

In this study, the effects of a hypothetical autonomous vehicle (AV)-exclusive roadway were estimated through a step-by-step approach using both microscopic and macroscopic simulations. First, the AV-exclusive roadway was classified into four types—entry lanes, mainlines, merging lanes, and intersections—and the C, α, and β values of the Bureau of Public Roads (BPR) function were estimated for each type through a microscopic simulation. These estimated values were then applied to a 3×3 (20 km) network, and a macroscopic simulation was conducted to compare the effectiveness of AVs and conventional vehicles (CVs) in terms of traffic volume and travel time.The analysis showed that for the same travel time, the traffic volume increased by more than 12% with AVs compared to that with CVs. Conversely, for the same traffic volume, the total travel time decreased by 11% for AVs. The estimated capacity of the AV-exclusive roadway, similar to the U-Smartway with a size of 3×3 (20 km), was approximately 400,000 vehicles, which was more than 140% higher than that of CVs. Assuming that each AV carries five passengers, up to two million people can be transported per day, indicating a significant potential benefit. However, these results were based on theoretical analyses using hypothetical networks under various assumptions. Future studies should incorporate more realistic conditions to further refine these estimations.

The rapid expansion of bridge and tunnel infrastructure has resulted in a growing incidence of wind-induced traffic accidents occurring at bridge approaches and tunnel portals. These accidents not only inflict direct damage on vehicles but also lead to substantial social and economic losses, stemming from roadway infrastructure repair and maintenance costs, as well as elevated logistics expenses due to traffic delays and congestion. In this study, a theoretical expression for the lateral displacement of vehicles as a function of wind speed was derived. Subsequently, lateral displacement and lateral wind force were analyzed and compared across vehicle types, considering both straight and curved roadway sections. An analysis of prevailing wind directions at each site revealed that, for passenger cars, the maximum lateral force and displacement on straight sections occurred at a wind incidence angle of 45°, whereas on curved sections with a pier curvature of 90°, the critical wind direction ranged from 0° to 120°. These results demonstrate that vehicle stability can be significantly compromised during high-speed travel under crosswind conditions. Based on departure trajectories of vehicles under varying wind speeds, a risk-assessment scale for wind-induced accidents was developed. In addition, design guidelines were proposed for the strategic placement of windbreak barriers to enhance driving safety under strong wind conditions.

본 연구는 보행자용 방호울타리의 구조 재료를 FRP로 대체하는 경우의 구조적 타당성을 동적 조건에서 검토하였다. ABAQUS/Explicit 기반의 비선형 충돌 해석을 통해 FRP 복합재 방호울타리의 충돌 거동을 분석하였으며, 각 재료의 비선형성을 반영 한 적절한 재료 모델을 적용하였다. 해석 결과, 기존 강재 방호울타리는 최대 약 167.6 mm의 변위가 발생하였으며, 방호구조물 및 기둥부의 소성 파괴가 관촬되었다. 추가적으로 콘크리트 연석의 고정부에서는 광범위한 파괴 양상이 확인되었으며, 이는 차량 충돌 시 구조체가 보행자 측으로 비산될 수 있는 위험성을 내포한다. 한편, CFRP 및 GFRP 방호울타리는 강재 대비 최대 변위가 약 7.1∼ 9.6%까지 증가하였으며, Hashin 파손 기준에 따른 파손 지수가 최대 1,548.428로 나타나 초기 단계에서 파손이 시작된 것으로 분석 되었다. 이러한 결과는 단순한 재료 치환만으로는 충분한 구조 안전성을 확보하기 어려움을 보여주며, FRP 복합재에 적합한 구조 설계 및 변수 최적화에 대한 추가 연구가 필요함을 시사한다. 아울러 수치해석 결과의 신뢰성 확보를 위해서는 향후 실험적 검증이 필수적이다.

The blocked force from the electric vehicle compressor is transmitted through the mount to the body side, serving as a primary source of body vibration during air conditioner operation at idle. Accordingly, a method is required during the compressor development stage to quantitatively evaluate the blocked force and analyze its influence for each transmission path. In this study, the blocked force at the outlet of an electric compressor was measured, and a test model was constructed to predict the response of the vehicle body using the Frequency-Based Substructuring(FBS) method. The 6-DOF dynamic stiffness of the bushing up to 500 Hz, not measurable with the elastomer, was successfully obtained using the inverse substructuring(IS) method. Finally, the proposed method was validated by the close match between predicted and measured body vibrations for both conventional and low dynamic stiffness bushings.

In four-wheel-drive vehicle, improving traction with the road surface enhances the vehicle's ability to respond to various driving conditions, increasing its overall versatility. Consequently, various studies have been conducted on four-wheel-drive vehicles that support torque distribution through electronic control. The driving unit that operates the transfer case assists in smooth torque distribution by providing high torque. Therefore, this study developed a reduction mechanism by vertically arranging a planetary gear set in the driving unit to increase the reduction ratio. To achieve this, a common ring gear with 52 teeth was used, and the design included a first-stage planetary gear with a sun gear having 18 teeth, a planet gear with 17 teeth, a second-stage sun gear with 12 teeth, and a planet gear with 20 teeth. The corresponding tooth profiles and structures were also designed. Based on this, a transfer case drive reduction module was developed, which improved torque performance: the first-stage planetary gear system provides 4.23 kgf·m of torque, and the second-stage planetary gear system achieves a final torque of 5.98 kgf·m

This study aims to enhance the efficiency of the after-sales service (A/S) process for commercial trucks by implementing a data-driven approach. Traditional A/S methods result in long repair wait times, especially for intermittent faults requiring symptom reproduction. To address this, a system that records Diagnostic Trouble Code (DTC) and Vehicle Running Mode (VRM) data at failure moments is proposed. By storing data from 10 seconds before and after an event, fault diagnosis can be performed without symptom reproduction. Additionally, for exported vehicles, stored data enables remote analysis, overcoming real-time data limitations due to varying environmental factors. This approach improves maintenance reliability, optimizes repair accuracy, and supports proactive quality improvements for newly developed vehicles.

목적 : 본 연구는 장애인의 자가운전을 지원하고 사회참여를 확대하기 위한 기반으로, 차량 관련 보조공학기기의 품질을 확보할 수 있는 체계적인 품질관리 기준을 마련하는 것을 목적으로 하였다. 특히 현재 국내에 부재한 명확한 품질 기준을 보완하고, 국제 기준에 부합하는 실효성 있는 방안을 제시하고자 하였다. 연구방법 : 연구는 총 세 단계로 진행되었다. 1차 및 2차 전문가협의체 회의를 통해 차량 관련 보조공학기기 품질관리의 방향성과 구체적인 시험항목을 도출하였으며, 이후 델파이 조사는 전문가 패널 20명을 대상으로 항목별 중요도 및 적합 도를 평가하고, 최종 품질관리 기준 항목을 확정하였다. 결과 : 1차 전문가협의체를 통해 시험인증, 체크리스트, 수입제품 인증 확인이라는 세 가지 품질관리 방안이 도출되었고, 이 중 시험인증 방안이 핵심 방안으로 채택되었다. 2차 전문가협의체에서는 SAE 기준을 참고하여 22개의 시험 및 평가 항목이 도출되었으며, 델파이 조사 결과 내용타당도비율을 기준을 만족하지 못한 항목을 제외하고 총 21개 항목이 최종 품질관리 기준으로 확정되었다. 이 기준은 SAE 국제 표준을 기반으로 하되, 국내 실정과 산업계 의견을 반영하여 조정되 었다. 결론 : 본 연구는 차량 관련 보조공학기기의 품질과 안전성을 확보하기 위한 실행 가능한 품질관리 기준 방안을 제시하였 으며, 이는 향후 관련 제도 마련 및 기술 가이드라인 개발에 있어 기초자료로 활용될 수 있다. 아울러 품질관리는 단순한 제품 성능의 확보를 넘어 장애인의 안전한 이동권 보장과 자립적인 사회참여에 기여하는 핵심 요소임을 강조하며, 실제 사용자 참여와 산업계 협력을 바탕으로 한 단계적 도입 방안이 필요함을 제언하였다.