Recently, changes in the electric vehicle transition policy have necessitated improved user acceptance by securing the competitiveness of electric vehicles over internal combustion engine vehicles. In particular, the importance of reliable condition diagnosis technology to prevent safety accidents such as battery pack fires has been receiving significant attention. However, lithium-ion battery packs, primarily used in domestic electric vehicles, require the development of battery pack health diagnosis technology that considers real-world driving characteristics, such as high energy density and irregular and incomplete charge/discharge patterns. This study utilized OBD-II data from 100 real-world electric vehicles to extract health indicators for assessing battery pack aging over time using IC curves. Using IC curves during charging, the most stable environment during real-world driving, key factors associated with battery pack aging were identified. The IC curves confirmed that aging increased with mileage from 30,000 km to 260,000 km, demonstrating the potential for developing integrated aging maps for the same vehicle model. Furthermore, this study is considered a practical tool for immediate condition assessment of electric vehicles without the need for additional equipment.

Vehicle indoor air quality is determined by the complex interaction between interior material emissions (such as VOCs and aldehydes) and road-sourced pollutants. Despite growing public concern, existing frameworks often focus on single pollutants and lack a comprehensive health-impact-based evaluation. This study proposes the Vehicle Indoor Air Quality Index (VIAQI), which integrates acute, chronic, and odor-related exposures from internal sources with the infiltration of ambient air pollutants. The VIAQI adopts a safety-oriented priority (HQacute → SF → OA → HQchronic), reflecting the driver’s cognitive safety. It consists of 10 levels, ranging from Grade 1 (Excellent) to Grade 10 (Hazardous). Under three operating modes (AM, PM-6 hr, and DM), the analysis includes 21 chemical substances, as well as PM2.5 and NO2. Acute risks are assessed using OEHHA’s RELs, chronic risks via US EPA’s RfC, odor effects are quantified using a smell sensitivity index (SF), and outdoor air infiltration is evaluated through a weighted hazard index (OA). After evaluating actual new vehicles, Vehicles A, C, and D are categorized as Grade 3 (Good), while Vehicle B is categorized as Grade 9 (Very Unhealthy) and Vehicle E is categorized as Grade 10 (Hazardous). Notably, Vehicle B is rated Grade 9 due to acute toxicity risks identified through RELs-based assessment, even though it meets all current national regulatory standards. This highlights the existence of health hazards that conventional concentration-based regulations may overlook. As Korea’s first multi-dimensional evaluation system for vehicle air quality, the VIAQI offers a practical tool for manufacturers to implement quality control, set policy, and communicate consumer information, providing a proactive assessment based on real-world driving environments.

도로 운송 부문은 전 세계 에너지 관련 온실가스 배출의 약 23%를 차지하며, 전 세계적인 물류 수요 증가와 도시화로 인 해 향후에도 지속적인 증가 추이를 보일 것으로 예측된다. 이러한 기후 위기 상황에서 차량의 연비를 개선하여 탄소 배출량 을 저감하기 위한 기술적 노력이 다각도로 진행되고 있다. 이 중 도로 포장과 차량 간의 복잡한 물리적 상호작용인 Pavement-Vehicle Interaction(PVI)은 포장체의 처짐으로 인해 발생하는 추가 연료 소모량(Excess Fuel Consumption, EFC)을 결정하는 핵심적인 요소로 주목받고 있다. 본 연구는 도로 포장과 차량의 상호작용으로 인해 발생하는 추가 연료 소모량을 산정함에 있어, 포장 재료의 에너지 소산 특성을 정의하는 감쇠 모델과 하중 하단에서의 포장 처짐 기울기를 계산하는 경사도 산정 방법이 미치는 영향을 정량적으로 비교 분석하였다. 구체적으로, 점성 감쇠 모델과 이력 감쇠 모델을 각각 적용하여 포장의 동적 응답을 도출하였다. 또한, 기 존의 선형 및 상수 기반 경사 산정 방식의 한계를 극복하기 위해, 본 과업에서 제시하는 하중 재하 지점의 순간 기울기를 산정하는 수치적 방법론을 검토하였다. 해석 결과, 선택된 감쇠 모델의 종류와 경사도 산정 방식의 조합에 따라 최종적으로 도출되는 연료 소모량 산정 결과에 유의미한 수치적 차이가 발생함을 확인하였다. 특히 이력 감쇠 모델과 순간 기울기 방식 의 결합은 기존 모델들이 간과했던 포장체의 비대칭적 처짐 거동을 더욱 정밀하게 포착함으로써, 보다 현실적인 탄소 배출 량 예측 근거를 제시하였다

This study analyzes the usage behavior and travel characteristics of shared micro-electric vehicle (micro-EV) mobility services using large-scale empirical data collected over three years (2023-2025) from three pilot regions in Korea: Daejeon, Mokpo, and Jeju Island. By integrating the location-based driving records, user information, trip surveys, and weather data, 66,843 valid trips were extracted, including 49,264 trips matched with user attributes. Usage behavior was first examined through statistical analyses of user demographics and surveybased information on trip purposes, multimodal connections, and user satisfaction. The results indicate that micro-EV services support diverse usage behaviors, ranging from commuting and campus travel to leisure, with distinct regional differences in user composition and service roles. A travel pattern analysis further revealed clear temporal and environmental characteristics, including pronounced seasonal peaks between July and October, higher weekday utilization associated with commuting demand, high concentration of trips during daytime hours, and a nonlinear response to weather conditions, in which usage increased under moderate temperatures but declined during extreme heat, cold, or precipitation. A distance-based analysis showed that micro-EV trips were generally short-distance in nature, while substantial regional variations existed. Daejeon was dominated by ultra-short campus-oriented trips, Mokpo exhibited a broader distribution of short urban trips linked to daily activities and tourism, and Jeju Island demonstrated a higher share of mid-distance trips reflecting industrial park commuting and inter-destination travel. A spatial origin-destination (OD) analysis further highlighted region-specific network structures, with Daejeon forming a compact hub-based network, Mokpo displaying a dispersed pattern centered on major transit facilities, and Jeju Island exhibiting a multipurpose network shaped by commuting and tourism mobility. These findings provide empirical evidence that shared micro-EV services function as flexible region-dependent mobility solutions, underscoring the need for differentiated operational strategies, station-based service planning, and integration with local transport systems to support sustainable urban mobility.

Performance of the hydrogen fuel cell system in a compact special vehicle is mainly influenced by the thermal characteristics of heat release through air flow with electrochemical mechanisms. In this study, numerical analysis has been carried out to investigate air flow and heat transfer characteristics near the fuel cell system for various operating conditions. The cooling characteristics around the radiator system depend on air flow generated by vehicle movement, and the effects of vehicle-induced air flow on the velocity and temperature distributions within the heat release system were examined. These results showed that there are quite complicated air flow around the radiator and fan near the fuel cell system in the vehicle cargo area, and its efficient flow field resulted in cooling performance improvement with driving speed. Hence overall heat release characteristics of the hydrogen fuel cell system are strongly associated with various air flow behavior formed around the compact special vehicle including cargo area.

This study proposes a data-driven framework for analyzing freeway driving behavior using multiple real-world trajectory datasets, and applies it consistently to mainline and ramp sections. The four large-scale datasets—namely highD, exiD, NGSIM I-80, and NGSIM US- 101—were processed through a unified preprocessing pipeline that converted all variables to International System Units(SI), resampled trajectories to 10 Hz, applied Savitzky-Golay smoothing to speed, and removed physically implausible and statistical outliers based on joint physical-statistical criteria. For each vehicle, 24 summary features were constructed from six longitudinal indicators–speed, acceleration, deceleration, time headway (THW), distance headway (DHW), and time-to-collision (TTC)–using their minimum, maximum, mean, and standard deviation. Indicator distributions by road type were compared using relative frequency histograms with common binning; then, principal component analysis (PCA) and K-means clustering were applied independently to each dataset. The leading principal components revealed interpretable axes related to longitudinal driving intensity (speed and acceleration level), safety margin (THW/DHW/TTC), and onramp sections; responsiveness was characterized by acceleration-deceleration variability, as observed within the analyzed datasets. Cluster interpretation yielded four relative driving behavior categories–aggressive, responsive, stable, and defensive–defined within each dataset based on indicator levels and variability rather than absolute thresholds.

Recent tactical vehicle development has focused on balancing protection and mobility through reinforced hulls, modular armor, and advanced mission systems. These upgrades have inevitably increased the gross vehicle weight (GVW), demanding higher engine performance to maintain mobility. This study analyzes the relationship between GVW, engine power, and power-to-weight ratio (P/W) across various international tactical and MRAP vehicles to assess how technological progress has compensated for weight growth. The GVW– power correlation illustrates generational trends in powertrain scaling, while the GVW–P/W analysis reveals changes in mobility efficiency as protection and payload increased. Results indicate that although engine outputs have risen in proportion to GVW, the overall P/W ratio has gradually declined, implying a design shift toward protection-oriented configurations. From these findings, a reference range of 20–25 hp/t is suggested as an appropriate target for future 4×4 and medium-class tactical vehicles. The results provide a quantitative basis for achieving an optimal balance between protection, payload capacity, and mobility in next-generation military vehicle design.

This study presents a standalone diagnostic device for HEV high-voltage battery packs that communicates directly with the BMS outside the vehicle and enables quantitative verification of BMS SOC and SOH outputs. The prototype, developed for a Renault CMA HEV pack, activates the BMS via the low-voltage harness, reads key variables such as SOC, SOH, cell voltage and temperature, and pack voltage and current over CAN, and safely controls the pack’s high-voltage relay. Using a pack reported as 100% SOH by the BMS, constantcurrent discharge at about a 0.1 C-rate was performed in the SOC range from 30% to 45%, and for 5, 10 and 15-minute segments the usable energy estimated from the BMS SOC and the rated capacity showed mean values around 1.54kWh with a coefficient of variation of approximately 2-3%. The proposed BMS-linked evaluation equipment estimates the usable capacity within a tolerance consistent with the manufacturer’s nominal specification and can serve as a practical basis and tool for second-life evaluation of used high voltage battery packs.

Modern warfare demands a high level of coordination and interoperability among multiple combat vehicles and crew members operating in dynamic and complex environments. Traditional training methods are often limited in scalability, flexibility, and cost-efficiency, making it challenging to effectively prepare forces for future battlefield scenarios. To address these limitations, this study presents the development of a Multiple Combat Vehicle Integrated Training Device, a next-generation simulation-based training system. The MCITD integrates advanced technologies such as Extended Reality, Digital Twin modeling, and Artificial Intelligence to deliver immersive, interactive, and highly realistic training experiences. The system allows for simultaneous training of multiple crew members in a networked environment that replicates real-world combat conditions, including terrain, weather, and adversary behavior. Key system components, including the simulation module, network communication framework, battlefield environment generator, and performance analysis engine, are discussed in detail. Potential application scenarios such as large-scale land operations, urban warfare, and multinational joint training exercises are also explored. The MCITD aims to enhance combat readiness, mission success, and training efficiency by providing a cost-effective, scalable, and adaptable solution for future-oriented military training programs.

This paper presents the design and experimental validation of an intelligent tire alignment and lifting control system for an under-vehicle autonomous parking robot. The proposed system enables the robot to autonomously enter beneath a vehicle, recognize tire positions using a LiDAR-based sensing module, and perform precise lifting through a fork-type mechanism. A YOLOv8 instance segmentation algorithm is employed to detect tire regions from LiDAR point cloud data and estimate their geometric centers. The detected tire positions are then matched with a vehicle database to determine the correct alignment for lifting. Experiments were conducted on three different vehicle types under various surface conditions. The results show that the proposed system achieved a tire recognition accuracy exceeding 95%, a lifting success rate of 100%, and an average lifting operation time of 12.3 seconds. These results demonstrate the reliability and practicality of the proposed method for real-world autonomous parking applications.

This study aims to evaluate the high-precision positioning capability and lane-level localization accuracy of low-cost RTK-GNSS(Real- Time Kinematic Global Navigation Satellite System) technology. This study compares the positioning accuracy and lane-level localization performance of a low-cost RTK-GNSS module with those of a commercial high-precision receiver under identical conditions. Specifically, the root-mean-square, lateral offsets from HD-map(High-Definition Map) lane centerlines, and lane-change detection rates were evaluated to examine the applicability of the module to advanced mobility systems. Based on experiments conducted using a two-axis linear motion device and actual-vehicle tests on expressways, the low-cost RTK-GNSS module demonstrated precision positioning and lane-level localization comparable to that of a commercial high-precision receiver under the same test conditions. In the precision-positioning evaluation, the maximum positioning error of the low-cost module is approximately 2 cm, which is larger than that of a commercial receiver. Nevertheless, its average error generally remained within the typical range of 1–2 cm, which is the expected range for fixed RTK solutions in opensky environments. Furthermore, the difference in the lane-level localization accuracy between the low-cost and high-precision modules remained at approximately 1 cm. Although the low-cost RTK-GNSS module employs fewer receiver channels compared with commercial high-precision units, the integration of the RTK-OMEGA solution, which supports robust integer ambiguity resolution and is a key element of RTK correction, enables it to perform comparably to a commercial module under identical test conditions. The performance-evaluation indicators and methodologies presented herein are expected to provide a meaningful foundation for future studies aimed at ensuring the reliability and safety of cost-effective RTK-GNSS technologies.

This study proposes a lightweight algorithm for real-time front-vehicle detection using low-resolution camera footage under various driving conditions. The proposed method first extracts driving lanes using Canny edge detection and the Hough transform, thus enabling efficient lane detection. A forward region of interest (ROI) is delineated based on the extracted lane geometry. Subsequently, YOLOv11 is employed to detect vehicles within each frame, where only those located inside the defined ROI are classified as preceding vehicles. To evaluate the applicability of the proposed method in diverse environments, its performance was assessed across six driving scenarios: normal driving, traffic congestion, complex structural environments, nighttime, tunnel sections, and sharp curves. Experimental results show that the proposed approach maintains a stable detection accuracy across different conditions while offering a low computational cost and a high processing speed. Compared with segmentation-based deep-learning lane-detection models, the proposed method demonstrates superior real-time capability and can operate using only a built-in monocular camera without relying on expensive sensors such as LiDAR, radar, or artificial markers. This study serves as a foundation for vision-based ADASs, front-vehicle-following control, and road-hazard detection systems.

This study aims to quantitatively and qualitatively evaluate the operational effects of an emergency-vehicle preemption (EVP) system implemented in Anyang City and to derive improvement directions based on both empirical performance outcomes and user-experienced insights. Specifically, this study integrates three complementary methodologies: (1) controlled field tests comparing pre- and post-EVP travel performance under consistent traffic and signal conditions, (2) a one-year operational evaluation using 204 actual dispatch cases collected from six 119 Safety Centers, and (3) a structured survey of frontline firefighters who directly utilized the EVP system during actual emergency responses. The field test results indicated that the average travel time reduced by approximately 44% while the average travel speed increased by approximately 79%, with paired t-test verification confirming that the observed improvements were statistically significant and attributable to the EVP system instead of to random variations. Similarly, the operational evaluation indicated that the actual travel time reduced by an average of 49% compared with navigation-estimated values, whereas the golden-time (5 min) arrival rates for both fire/rescue and medical dispatches exceeded the regional average, with consistent performance demonstrated even under varying travel distances and road complexities. The firefighter survey further reinforced these findings, with respondents reporting clear improvements in golden-time achievement, reduced anxiety toward potential safety risks, and enhanced perceived safety during emergency trials, as well as identified several practical limitations such as route mismatches, occasional system malfunctions, and difficulty in perceiving preemption activation—factors that suggest necessary technical and operational refinements. In general, the EVP system was evaluated as an effective and highly practical tool that improves emergency-vehicle mobility, arrival-time stability, and operational reliability across diverse dispatch conditions. The combined quantitative and qualitative verification in this study underscores its value as a field-proven technology. Future studies should expand to multiregional longitudinal datasets, controlled analyses considering external variables such as traffic volume and weather, and quantitative evaluation of safety-related impacts such as reductions in intersection collisions or on-route risk exposure to assess the system’s broader policy and operational benefits more comprehensively.

This study aims to provide a basis for selecting the appropriate traffic-flow evaluation indicators by quantitatively analyzing the relative importance of such indicators in mixed traffic environments in which automated vehicles (AVs) and conventional vehicles coexist. As AV technology progresses and its adoption increases, establishing reliable evaluation criteria that accurately reflect the characteristics and performance of traffic systems under transitional conditions is crucial. Thus, approximately 40 domestic and international studies were reviewed in this study, from which 45 evaluation indicators were identified. These indicators were classified into three major categories: mobility, safety, and environment. Five frequently used and representative indicators were selected from each category based on the appearance frequency and relevance. An analytic hierarchy process survey was conducted with a group of transportation experts to derive the relative importance (weights) of both the major categories and individual indicators. The analysis revealed that safety (0.53676) was the most important category, followed by mobility (0.34795) and environment (0.11528). After combining the weights of the categories and sub-indicators, the top three indicators, i.e., time to collision (TTC), time exposed to TTC, and deceleration rate to avoid crashes, appeared to be safety related and associated directly with the collision risk. These findings suggest that, in the early stages of AV deployment, traffic evaluations should prioritize safety considerations over mobility or environmental factors to ensure the successful integration of AVs into existing traffic systems.

운전 시뮬레이션을 이용하여 3-수준 자율주행 상황을 구현한 후 청년 및 고령운전자가 주간/야간 운전 조건과 비 운전과제(nonm-driving task: NDT) 수행 여부에 따라 보이는 제어권 인수시간(takeover time: TOT), 차량제어 (vehicle control :VC) 및 주관적 작업부하(subjective workload: SW) 수준에서의 차이를 비교하였다. 실험참가자들에 게는 자율주행 중 NDT를 수행하도록 하였고 NDT 수행 도중 제어권 인수가 요청되면 제어권을 빠르고 정확하게 인수받아 수동운전으로 전방의 장애물을 회피하도록 하였다. 본 연구 결과를 요약하면 다음과 같다. 첫째, 야간 운전 조건과 NDT 수행 조건에서 실험참가자들의 TOT는 증가하였고, 차량에 대한 종적 및 횡적수행 모두 저하되었으며, SW 수준은 더 높았다. 둘째, 청년운전자 집단에 비해 고령운전자들의 VC 수행이 상대적으로 더 저조하였다. 셋째, 고령운전자들은 야간 운전과 NDT 수행 요구가 결합되면 모든 종속측정치에서 청년운전자들에 비해 상대적으로 더 저하된 수행을 보였다. 이러한 결과는 야간 자율주행에서 고령운전자의 주의가 분산될 경우 자율주행 차량과의 상호 작용 및 긴급한 상황에서의 장애물 회피에서 어려움이 증가할 수 있다는 것을 시사한다.

This study presents the results of compression, drop impact, and vibration durability analyses conducted to evaluate the mechanical reliability of Battery Pack Cases (BPCs) in electric vehicle (EV) systems. BPCs are essential structural components that must endure compressive loads, impact forces, and vibrational fatigue. Finite Element Analysis (FEA) was applied to a representative BPC model to assess deformation, impact resistance, and vibration endurance. The results indicate that the BPC maintained integrity within yield strength limits under compressive loading and effectively absorbed energy under drop impact. Furthermore, Power Spectral Density (PSD) analysis identified stress concentration regions, providing insights for structural optimization. Overall, the findings support the development of lightweight and reliable BPC designs for advanced EV applications.

To improve vibration reduction in the railway vehicle, the semi-active suspension system using MR damper was developed and the vibration performance of the passive suspension system and a semi-active MR suspension system was compared. For the experiment, the MR damper and suspension system were designed and manufactured. Tensile and compression tests were performed on the MR damper while varying the input current. The damping force of the MR damper was measured and analyzed using the Bingham model. The railway vehicle was modeled with 9 degrees of freedom, and the sky hook control algorithm was simulated using the MR damper, using the Bingham model. This verified the effectiveness of the sky hook controller. Furthermore, to compare the vibration performance of the railway vehicle, the driving test was conducted with the MR damper and the passive damper. The lateral acceleration vibration reduction performance of the suspension system with MR dampers and passive dampers was verified, and it was confirmed that the vibration reduction performance of the vehicle with the semi-active suspension system using MR damper was approximately 50% better than that of the vehicle with the passive damper.