자율주행차량의 첨단 운전자 보조 시스템(ADAS)의 발전은 자율주행차량의 상용화를 가속화하고 있지만, 그 안전성을 입증하기 위한 충분한 테스트와 검증이 필요하다. 실제 차량을 이용한 대규모 테스트는 비용과 시간뿐만 아니라 다양한 시나리오를 구현하고 평가하 는 데 어려움이 있어, 다수의 연구자들은 시뮬레이션을 활용하고 있다. 이러한 문제를 해결하기 위해, 본 연구에서는 차량 시뮬레이션 소프트웨어인 CarMaker와 교통 흐름 시뮬레이션인 VISSIM을 결합하여 공동으로 시뮬레이션을 진행한다. 또한 두 시뮬레이션의 장점 을 결합하여 자율주행차량의 데이터를 보다 포괄적으로 분석할 수 있는 프레임워크를 제안한다. 시뮬레이션 결과, 각각의 시뮬레이션 에서 얻은 Ego Vehicle의 속도 값은 미세한 차이를 보였으며, 이는 실시간 시뮬레이션의 통신 과정에서 발생하는 오류로 해석된다. 또 한, 특정 시나리오에서는 차량이 급정지 후 출발하는 형태를 보였으며, 이는 자율주행차량이 주변 차량의 주행을 인식하여 주행 패턴 에 변화를 주는 것으로 해석된다. 향후에는 도심 도로에서의 자율주행 평가를 통해 복잡한 교통 상황과 불확실한 요소들로 인해 어려 운 문제를 겪는 상황을 분석할 수 있을 것으로 기대된다.

우리나라는 교통사고 사망자수 감축을 위해 다양한 교통안전 개선사업을 지속적으로 추진하고 있다. 특히, 보행자 안전 강화를 위한 보행환경개선사업은 중앙정부 및 지방정부를 중심으로 시설, 제도, 교육ž홍보 등 다양한 방법으로 수행되고 있다. 최근 횡단보도 보행자 안전 강화를 위한 시설 개선 사례로 활주로형 횡단보도가 설치ž운영되고 있다. 활주로형 횡단보도는 야간에 운전자 시인성을 증진하고 보행자의 안전성을 확보하는 시설물이다. 그러나 신규로 설치되는 활주로형 횡단보도의 정량적인 보행안전 개선 효과에 대한 연구는 부 족하다. 본 연구에서는 보행환경개선사업을 통해 읍․면지역에 신규 설치되는 활주로형 횡단보도의 효과를 시설물 접근로 진입 차량 평 균 주행 속도 조사를 통해 분석하였다. 총 8개 지점의 활주로형 횡단보도를 대상으로 접근로 진입 차량 평균 주행 속도를 조사하였으 며, 평균 속도 감소율은 15.3%이고, 속도 최대 감소율은 27.4%로 나타나는 것으로 분석되었다. 이와 같이 무단횡단이 빈번이 관측되는 읍․면지역의 경우 활주로형 횡단보도의 설치함으로써 접근하는 차량의 속도를 저감시키는 것으로 확인하였다. 향후 보행자 교통사고 저감이 필요한 읍․면지역에 설치 시 긍정적인 효과를 기대할 수 있을 것으로 사료된다

전 세계적으로 실도로에서의 자율주행차 안전성능을 검증하고 자율주행 시스템 기술의 개발을 위해 다양한 실증을 수행하고 있다. 미국의 경우 캘리포니아, 오하이오, 애리조나 등 다양한 주에서 자율주행차의 실도로 테스트를 진행하고 있으며, 독일의 경우 페가수 스 및 이매진 프로젝트 등을 통해 자율주행 성능 및 협력 운행 테스트를 수행하였다. 그러나, 자율주행차의 주행 성능 측면의 평가에 국한되어 실증이 진행되고 있다는 한계가 존재한다. 실도로 환경에서 자율주행차는 비자율주행차, 보행자 및 자전거 등과 상호작용하 며, 다양한 도로 기하구조에서 주행안전성 저하 문제가 발생할 수 있다. 따라서, 본 연구에서는 혼재교통상황에서 자율주행차의 주행 안전성을 저하시키는 도로 기하구조를 도출하였다. 또한, 캘리포니아 Department of Motor Vehicles (DMV)에서 제시한 자율주행차 관련 사고자료 검토를 통해 유사한 도로 기하구조에서 발생할 수 있는 사고 유형을 검토함으로써 선제적인 대안을 마련하고자 한다. 시뮬 레이션 분석을 위한 자율주행차 거동구현의 경우 real-world automated vehicle data (AVD) 기반 주행행태 분석을 통해 VISSIM 파라미 터를 조정하였다. 위험구간 도출을 위해 평가지표를 선정하고 주행안전성 분석을 수행하였으며, 위험 구간의 도로 기하구조의 특성을 도출하였다. 마지막으로 위험구간의 도로 기하구조와 유사한 구간에서 발생한 실제 자율주행차 관련 사고 보고서를 검토함으로써 본 연구에서 도출된 위험구간의 도로 기하구조에서 발생할 수 있는 잠재적 사고 원인을 제시하였다. 본 연구의 결과를 통해 향후 자율주 행차의 실도로 도입을 위해 선제적인 대책을 마려하는데 기초자료로 활용될 수 있으며, 나아가 자율주행차 안전성 향상을 위한 경고 정보 서비스 개발, 정보 제공 인프라 설치 우선순위, 도로 기하구조 개선 사업에 활용할 수 있을 것으로 기대된다.

PURPOSES : This study develops a model that can estimate travel speed of each movement flow using deep-learning-based probe vehicles at urban intersections. METHODS : Current technologies cannot determine average travel speeds for all vehicles passing through a specific real-world area under obseravation. A virtual simulation environment was established to collect information on all vehicles. A model estimate turning speeds was developed by deep learning using probe vehicles sampled during information processing time. The speed estimation model was divided into straight and left-turn models, developed as fully-offset, non-offset, and integrated models. RESULTS : For fully-offset models, speed estimation for both straight and left-turn models achieved MAPE within 10%. For non-offset models, straight models using data drawn from four or more probe vehicles achieved a MAPE of less than 15%. The MAPE for left turns was approximately 20%. CONCLUSIONS : Using probe-vehicle data(PVD), a deep learning model was developed to estimate speeds each movement flow. This, confirmed the viability of real-time signal control information processing using a small number of probe vehicles.

In contemporary society, traffic concerns and road safety are of paramount importance. Overloading and improper loading significantly jeopardize road safety. Leveraging AI Hub's CCTV traffic videos, overloading hazard data, and Yeosu Sunsin Bridge HS-WIM data from Jeollanam-do, we've crafted detection models. These models facilitate the development of a vehicle detection system calibrated for specific scenarios, making the detection of overloading and loading discrepancies more accessible. Such a system promises to streamline detection operations while substituting manual efforts with AI, thereby economizing on both time and resources.

본 연구에서는 운전 시뮬레이션을 사용하여 자율주행 환경을 구현한 후 3-수준 자율주행 조건에서 자율주행 차량 (automated vehicle: AV)으로부터 운전자에게 전달되는 제어권 인수 요구(takeover request: TOR) 정보의 양상(시각, 청각 및 시각+청각) 및 도로 형태(직선도로와 곡선도로)에 따라 운전자의 제어권 인수 시간(takeover time: TOT) 및 정신적 작업부하(제어권 인수 이후에 운전자들이 경험한 주관적 작업부하와 심장박동수에서의 변화)가 어떻게 차별 화되는지 분석하였다. 본 연구의 결과를 요약하면 다음과 같다. 먼저, AV로부터 TOR이 제시된 이후 실험참가자들 이 보인 TOT에 대한 분석 결과, TOR 정보양상의 측면에서는 시각 정보가 가장 빠른 TOT를 이끌어 낸 반면 청각 정보 조건에서 가장 느렸고, 도로 형태 측면에서는 직선도로 조건에 비해 곡선도로 조건에서의 TOT가 유의하게 더 느렸으며, 특히 청각 정보 조건에서 도로 형태에 따른 TOT에서의 차이가 가장 컸다. 둘째, 정신적 작업부하에 대한 분석 결과, TOR 정보가 시각 혹은 시각+청각적으로 제시된 조건에 비해 청각적으로 제시된 조건에서 주관적 작업부 하 측정치와 심장박동수 변화 크기 모두 전반적으로 더 낮았고 특히, 심장박동수 변화의 경우 이러한 경향은 곡선도 로 조건에서만 관찰되었다. 이러한 결과는 TOR 정보의 양상과 도로 형태에 따라 운전자의 TOT와 정신적 작업부하 수준이 달라질 수 있고, 특히 TOT가 빠를수록 정신적 작업부하 수준은 상대적으로 더 높아질 수 있음을 시사한다.

PURPOSES : Basic research to calculate the appropriate gap acceptance for autonomous vehicles at merging section. Research on whether users prefer short or long gap acceptance. METHODS : Using a driving simulator, experience autonomous driving with different gap acceptance in different weather condition, and analyze which gap acceptance is preferred using survey and biometric data. RESULTS : Regardless of the weather condition, long gap acceptance was preferred, and difference was especially clear in rainy or foggy situation. CONCLUSIONS : It was analyzed that users prefer long gap acceptance over short gap acceptance, and that they feel less frustrated due to long gap acceptance when weather condition is poor.

PURPOSES : Evaluation of the effectiveness of changing the form of yellow carpet installation as a way to reduce child pedestrian traffic accidents. METHODS : Through expert opinion, two improvement plans for yellow carpet installation (oblique type, extended type) were derived. The improvement paln was built in virtual reality, and a virtual driving experiment was performed using a driving simulator and eye-trakcing device. The improvement effects of the two alternatives were evaluated by analyzing eye-tracking data and driving behavior. RESULTS : In the case of the oblique type, it was analyzed that it was effective in improving the total gaze time and first gaze position compared to the normal type. In the case of the extended type, it was analyzed that the workload during operation can be reduced. However, neither of them had a significant effect on driving behavior. CONCLUSIONS : Although the change in the yellow carpet installation type did not affect the driver's driving behavior, it had advantages in terms of visual behavior and workload while driving, so it can be considered as an alternative among measures to improve traffic accidents involving children and pedestrians.

PURPOSES : Demonstrated performance degradation of LiDAR for vehicle and pedestrian dummy in rainy and foggy road conditions. METHODS : In real-scale rain and fog road conditions, adjust the distance between LiDAR and the measurement target from 10m to 70m (in 10m interval), measure LiDAR NPC (number of point cloud) and intensity, and compare the resulting numerical values. RESULTS : LiDAR's NPC and Intensity showed statistically significant differences by overall weather condition (normal, rain, fog), and the values were found to be larger in the order of normal>rainfall>fog. In the case of vehicles, sunder rain conditions, NPC and intensity are recognized even at 70m as in normal conditions, but under fog conditions, NPC and intensity are measured only up to 30m. In the case of pedestrians, the reflective area size is smaller than that of vehicles, so they are recognized only up to 30m in rainy conditions, and NPC and intensity are measured only up to 20m in fog conditions. CONCLUSIONS : It was confirmed that LiDAR performance deteriorates in rain and fog compared to normal.

PURPOSES : This study aims to develop a congestion mitigation strategy at lane drop bottleneck with low Connected and Automated Vehicle (CAV) penetration. METHODS : The proposed strategy is designed to assign a role of a moving bottleneck to CAVs to reduce low-speed lane changes at bottleneck locations, which are the main cause of bottleneck capacity drop. Through this, it aims to induce proactive upstream lane changes for Human-Driven Vehicles (HDVs,). Therefore, this study includes the control algorithm for CAVs, and the evaluation of the strategy assumes penetration rates of 5% and 10% in a Microsimulation VISSIM environment. The assessment is conducted by comparing the capacity drop and total travel time. Additionally, a sensitivity test for the parameter of the CAV control algorithm, reduced speed, is performed to find the optimal parameter. RESULTS : In this study, three scenarios, a) Base, b) CAV with no control, and c) CAV with control, are designed to evaluate the effects of the CAV control strategy. Analysis of segment density and lane change distribution reveals that the control strategy effectively prevented vehicle congestion due to the bottleneck effect. Additionally, the analysis of capacity changes before and after the bottleneck and total travel time shows the effectiveness of the control strategy. The sensitivity test on CAV control speed emphasized the importance of selecting an appropriate speed for maintaining efficient traffic flow. Lastly, as the CAV penetration rate increased, the control strategy exhibited greater effectiveness in mitigating capacity drop. CONCLUSIONS : The proposed strategy is intended for use at low CAV penetration rates and is expected to provide assistance in mitigating congestion at bottlenecks during the early stages of CAV commercialization. Furthermore, since the role of CAV in the strategy can be performed by CVs or even HDVs, it can be applied not only immediately but also in the near future.

PURPOSES : This study aims to perform a quantitative analysis of Forward Collision Warning and crash frequency using heavy vehicle driving data collected in expressway driving environments, and to classify the driving environments where Forward Collision Warnings of heavy vehicles occur for accident-prone areas and analyze their occurrence characteristics. METHODS : A bivariate Gaussian mixture model based on inter-vehicle distance gap and speed-acceleration parameters is used to classify the environment in which Forward Collision Warning occurs for heavy vehicles driving on expressways. For this analysis, Probe Vehicle Data of 80 large trucks collected by C-ITS devices of Korea Expressway Corporation from May to June 2022. Combined with accident information from the past five years, a detailed analysis of the classified driving environments is conducted. RESULTS : The results of the clustering analysis categorizes Forward Collision Warning environments into three groups: Group I (highdensity, high-speed), Group II (high-density, low-speed), and Group III (low-density, high-speed). It reveals a positive correlation between Forward Collision Warning frequency and accident rates at these points, with Group I prevailing. Road characteristics at sites with different accident incidences showed that on-ramps and toll gates had high occurrences of both accidents and warnings. Furthermore, acceleration deviation at high-accident sites was significant across all groups, with variable speed deviations noted for each warning group. CONCLUSIONS : The Forward Collision Warning of heavy vehicles on expressways is classified into three types depending on the driving environment, and the results of these environmental classifications can be used as a basis for building a road environment that reduces the risk of crashes for heavy vehicles.

PURPOSES : This study evaluates the effectiveness of traffic flow optimization when giving safety strategy guidance to a connected autonomous vehicle (CAV) based on information received through infrastructure cooperation in a V2X environment for non-signal intersection. METHODS : To evaluate the effectiveness of safety strategy guidance based on developed traffic flow control algorithm at a non-signalized intersection, it was implemented on simulation. A scenario based on the Level of Service (LOS) and the market penetration rate(MPR) of autonomous vehicles was established. The simulation results were divided into safety, operation, and environment to evaluate the effect, and the effect of optimizing traffic flow was finally derived through the integrated evaluation score. RESULTS : As a result, when safety strategy guidance was provided, the number of conflicts and CO emissions decreased by about 29% and about 15%, improving safety and environmental performance. In the case of operation, the mean of delay time was increased overall by 1%, but in the case of MPR 50 and above, the delay time was reduced by about 38%, thereby increasing operation. Finally, the aspect of traffic flow optimization, effectiveness of safety strategy guidance was derived through the integrated evaluation score, and the average integrated evaluation score improved from MPR 20 or higher. CONCLUSIONS : Providing guidance had the effect of optimizing traffic flow at a non-signal intersection. In the future, V2X communications will provide CAV with algorithm-based guidance developed in this study to control driving behavior. it will support safe and efficient driving at non-signal intersections.

The automotive industry continuously strives to enhance safety for both drivers and passengers through technological advancements. Car side impacts have the potential to significant risks to passengers, So the automotive industry has proposed various technological solutions. As part of these efforts, the development of side impact beams, which are affixed to the inner frame of vehicle side doors to absorb and dissipate collision energy, has been a safety enhancement. Conventional side impact beams are manufactured using hot-rolled steel sheets and have a pipe-like configuration. However, these impact beams are fixed to the vehicle's chassis, which directly transfers the energy generated during a collision to the chassis frame. This paper aims to address this issue by proposing the development and optimization of vehicle door impact beams using a dual-beam structure and fastening method, utilizing shear bolts. Moreover, the focus is on optimizing the cross-sectional shape of the dual-beam impact structure. The evaluation criterion for optimization is based on the second moment of area of the cross-section. To validate these improvements, Static experiments were conducted, comparing the proposed dual-beam structure with the traditional impact beam. This research is expected to serve as a guideline for enhancing vehicle safety through design directions and validation methods.

In this study, the abnormal noise phenomenon generated intermittently in the shock absorber of the combat armored vehicle was approached from the side of the squeak joint to solve the phenomenon. In order to identify the cause of the noise, the factors causing friction in the shock absorber were checked from the viewpoint of problem product analysis, process, and design. As an improvement plan, by improving the non-lubricated section in the nitrogen chamber, the operating noise was reduced to a level lower than that of a general automobile engine along with the elimination of noise. Considering that this is a combat vehicle, it is judged that it has been improved so as not to affect the survivability and health of the crew during equipment operation. Through the improvement of this study, it is expected to prevent power loss and improve the emotional quality of our soldier.

Recently, in the case of the root industry, although it is a basic industry that forms the basis of manufacturing competitiveness, there continues to be a shortage of manpower due to reasons such as dangerous working environments, industrial economic difficulties, and low wage systems. In addition, the demand for automation of production lines using robots is increasing due to a shrinking labor market due to a decrease in the working population due to aging, higher wages, shorter working hours, and limitations of foreign workers. In this study, a system was developed to automate the injection molding process for producing ball valves for automobiles by applying robot system. The applied process flow consists of alignment and insertion of insert parts, and removal, transfer, and loading of the product after injection molding, which is currently performed manually. Through the application of the developed robot automation system, the cycle time was improved by more than 30% and the defect rate was reduced by more than 70%.

Combat-armored vehicles were equipped with an automatic-fire-extinguishing system to ensure the safety of the crew and vehicle from fires on the vehicle. When a fire was occurred, the automatic-fire-extinguishing system automatically detects the fire through sensors and detection lines, sprays a fire extinguisher, and notifies the crew visually and audibly. Recently, there had been cases of automatic-fire-extinguishing systems malfunction on combat-armored vehicles. In this study, in order to resolve the automatic-fire-extinguisher's malfunction phenomenon, ground noise and inter-circuit noise generated from the fire detection line were identified, and the resistance connected on the circuit was revised to remove noise. As a result of resistance revision, the noises was eliminated and the electromotive force difference between input circuits was made constant, thereby improving the malfunction of the automatic-fire-extinguishing system. By applying the result, it was confirmed that the control device sensed a temperature similar to the actual temperature on actual vehicles, and it was confirmed that the automatic-fire-extinguishing system's malfunction phenomenon was not founded in the field vehicles after then.