PURPOSES : Driving simulations are widely used for safety assessment because they can minimize the time and cost associated with collecting driving behavior data compared to real-world road environments. Simulator-based driving behavior data do not necessarily represent the actual driving behavior data. An evaluation must be performed to determine whether driving simulations accurately reflect road safety conditions. The main objective of this study was to establish a methodology for assessing whether simulation-based driving behavior data represent real-world safety characteristics. METHODS : A 500-m spatial window size and a 100-m moving size were used to aggregate and match the driving behavior indicators and crash data. A correlation analysis was performed to identify statistically significant indicators among the various evaluation metrics correlated with crash frequency on the road. A set of driving behavior evaluation indicators highly correlated with crash frequency was used as inputs for the negative binomial and decision tree models. Negative binomial model results revealed the indicators used to estimate the number of predicted crashes. The decision-tree model results prioritized the driving behavior indicators used to classify high-risk road segments. RESULTS : The indicators derived from the negative binomial model analysis were the standard deviation of the peak-to-peak jerk and the time-varying volatility of the yaw rate. Their importance was ranked first and fifth, respectively, using the proposed decision tree model. Each indicator has a significant importance among all indicators, suggesting that certain indicators can accurately reflect actual road safety. CONCLUSIONS : The proposed indicators are expected to enhance the reliability of driving-simulator-based road safety evaluations.

iRAP(international Road Assessment Program)에서는 최근 다양한 유형의 도로 정밀자료를 바탕으로 한 AiRAP(Accelerated intelligent Road Assessment Program)을 개발하여 여러 국가별 RAP에 확대 적용해나가고 있는 단계에 있다. 2023년부터 국내 도로 여건을 고려한 한국형 도로안전도 평가 프로그램 개발 연구가 진행 중에 있으며, 개발되는 평가항목 및 기준, 평가기술 등에 적합한 도로안전도 평가 기초데이터 역시 유형 및 범위에 변화가 필요해짐에 따라 이를 충족하는 기초데이터 수집이 가능한 도로안전도 조사장비 개발이 기획 되었다. 이에 따라 본 연구에서는 고사양의 360도 도로영상 촬영장치와 360도 128채널 LiDAR 센서, 정밀 GPS 시스템, 휴대용 운영장 치 등을 갖춘 도로안전도 조사장비를 일반 차량에 탈부착이 가능한 형태로 개발하고자 하였다. 본 개발 장비로 취득 가능한 도로영상 및 점군데이터를 활용하여 AiRAP 자동분석을 통한 도로안전도 평가가 가능한데, 이를 통해 객관적이고 신뢰성 높은 평가가 가능할 것으로 기대된다.

교통사고는 인적요인, 도로 기하구조, 교통류, 환경적 요인 등 복합적인 요인에 의해 발생하고 속도는 교통사고와 밀접한 연관성이 있다. 또한, 교통사고는 교통 혼잡도와 관련이 있으며 사고와 실시간 교통상황 간의 상관관계를 통해 사고 발생 개연성을 추정하고 도 로 안전성 분석이 필요하다. 모바일 센서와 통신 기술의 급속한 발전으로 스마트폰 보급률이 증가하였으며 내장된 센서를 기반으로 생성된 차량 주행 데이터 수집이 가능하다. 기술의 발달로 데이터 수집이 쉬워졌음에도 불구하고, 스마트폰을 기반으로 수집된 위험 운전 이벤트를 활용한 도로 위험도 평가에 대한 연구는 부족한 실정이다. 본 연구는 스마트폰 센서 기반의 위험 운전 이벤트 데이터 중 하나인 급감속 위험 운전 이벤트 데이터를 도로 위험도 평가 기법에 활용하는 것을 목적으로 한다. 급감속 위험 운전 이벤트 데이 터는 주행 차량이 3초간 속도를 40km/h 이상 감소하는 위험 이벤트가 발생할 때 시간과 위치를 기록한 자료를 의미한다. 본 연구의 범위는 대한민국 내 인구와 교통량이 많은 지역인 수도권을 대상으로 서울, 경기, 인천을 연결하는 고리 형태의 도로인 수도권제1순환 선을 대상으로 하였다. 먼저, 개별 차량 데이터는 좌표 기반의 내비게이션 데이터로 집계하여 VDS 링크 데이터와 매칭하였다. 다음으 로는 개별 차량의 위험 운전 이벤트 데이터와 차량 검지기의 교통 매개변수를 결합한 새로운 지표를 개발하였다. 또한, 시·공간적 교 통류의 특성을 반영하여 다양한 도로 위험도 평가 방법에 활용하고자 하였다. 마지막으로 위험 운전 지표와 이력 자료를 기반으로 통 계적으로 유의한 안전성능함수를 개발하였으며, 다양한 시간 단위의 집계 수준을 활용하여 도로 구간별 최적의 모형을 제안하였다. 본 연구는 스마트폰 센서를 기반으로 식별한 개별 차량의 위험과 교통류 차원의 위험을 결합하여 새로운 위험 지표를 개발하고 도로 위 험도 평가에 활용한다는 것에 의의가 있다. 결과물은 향후 스마트폰 센서 기반 개별 차량 위험 운전 이벤트 데이터와 교통 조건을 통 합하는 도로 위험도 평가의 기초자료로써 활용될 것으로 기대된다.

PURPOSES : The purpose of this study was to quantitatively evaluate the variability of LiDAR performance indicators, such as intensity and Number of Point Cloud(NPC), according to various environmental factors and material characteristics. METHODS : To consider the material characteristics of road safety facilities, various materials (Reference Material(RM), reflective sheet, matte sheet, granite, plastic, and rubber) were used in a darkroom, and the performance indicators of LiDAR were repeatedly measured in terms of changes in the measurement distance, rainfall, and angle of observation. RESULTS : In the case of standard reflective materials, the intensity measurement value decreased as the measurement distance and rainfall increased. The NPC showed a tendency to decrease as the measurement distance increased, regardless of rainfall intensity. For materials with high-intensity values, it was found that rainfall intensity and color had negligible effect on the change in intensity compared with the measurement distance. However, for materials with low-intensity values, it was found that the measurement distance, rainfall intensity, and color all had a significant effect on the change in intensity. CONCLUSIONS : For materials with high-intensity values, it was found that rainfall and color had negligible effect on change in intensity compared with the measurement distance. However, for materials with low-intensity values, the measurement distance, rainfall, and color all had a significant effect on the change in intensity value.

PURPOSES : Road surface conditions are vital to traffic safety, management, and operation. To ensure traffic operation and safety during periods of snow and ice during the winter, each local government allocates considerable resources for monitoring that rely on field-oriented manual work. Therefore, a smart monitoring and management system for autonomous snow removal that can rapidly respond to unexpected abrupt heavy snow and black ice in winter must be developed. This study addresses a smart technology for automatically monitoring and detecting road surface conditions in an experimental environment using convolutional neural networks based on a CCTV camera and infrared (IR) sensor data. METHODS : The proposed approach comprises three steps: obtaining CCTV videos and IR sensor data, processing the dataset acquired to apply deep learning based on convolutional neural networks, and training the learning model and validating it. The first step involves a large dataset comprising 12,626 images extracted from the acquired CCTV videos and the synchronized surface temperature data from the IR sensor. In the second step, image frames are extracted from the videos, and only foreground target images are extracted during preprocessing. Hence, only the area (each image measuring 500 × 500) of the asphalt road surface corresponding to the road surface is applied to construct an ideal dataset. In addition, the IR thermometer sensor data stored in the logger are used to calculate the road surface temperatures corresponding to the image acquisition time. The images are classified into three categories, i.e., normal, snow, and black-ice, to construct a training dataset. Under normal conditions, the images include dry and wet road conditions. In the final step, the learning process is conducted using the acquired dataset for deep learning and verification. The dataset contains 10,100 (80%) data points for deep learning and 2,526 (20%) points for verification. RESULTS : To evaluate the proposed approach, the loss, accuracy, and confusion matrix of the addressed model are calculated. The model loss refers to the loss caused by the estimated error of the model, where 0.0479 and 0.0401 are indicated in the learning and verification stages, respectively. Meanwhile, the accuracies are 97.82% and 98.00%, respectively. Based on various tests that involve adjusting the learning parameters, an optimized model is derived by generalizing the characteristics of the input image, and errors such as overfitting are resolved. This experiment shows that this approach can be used for snow and black-ice detections on roads. CONCLUSIONS : The approach introduced herein is feasible in road environments, such as actual tunnel entrances. It does not necessitate expensive imported equipment, as general CCTV cameras can be applied to general roads, and low-cost IR temperature sensors can be used to provide efficiency and high accuracy in road sections such as national roads and highways. It is envisaged that the developed system will be applied to in situ conditions on roads.

In this study, the Euro NCAP-based AEB system evaluation simulation was conducted by applying the calculated corrected TTC by road condition estimation of scenarios (CPFA, CPNC, CPLA) of the V2P situation scenario using PC-Crash, a program used for traffic accident analysis. The scenario was evaluated in consideration of the two road conditions. the low-speed conditions among every scenarios avoided collision, but in the medium and high-speed conditions has been collided with pedestrian. It was confirmed that the time point at recognizing pedestrian was lower than set TTC at which AEB system was operated, even though the AEB was operated immediately, a collision occurred due to insufficient braking distance. As in this study, if studies such as V2V, V2P, and V2B considering road friction are actively conducted, it is expected to be useful data for automobile accident prevention and accident analysis.

In this study, the AEB system evaluation simulation was conducted by applying a corrected TTC from 0s to 3s obtained by estimating the road-friction factor and slope applying two scenarios(CCRs and CCRm) that may occur in V2V situation using PC-CrashⓇ, a program used for traffic accident analysis. As a result of two scenarios : At 0.8 of road-friction factor and 0° slope which adapted default model of TTC had longer braking distance so that it crashed. At 0.2 of road-friction factor with corrected TTC slidingly crashed due to slope and braking condition. CCRs and CCRm scenario showed that the maximum slipped distance of the collision avoidance situation was 31.7m, 87.7m. And the collision speed was 35.7km/h at 50km/h and 66.1km/h at 80km/h.

PURPOSES : The purpose of this study is to evaluate the road design elements affecting the lateral driving safety under high-speed driving conditions with a speed limit of 140 km/h and to derive useful implications to design of safer roads. METHODS : A full-scale driving simulator was used to evaluate the various design scenarios. Different regression techniques and a random forest method were adopted to conduct comprehensive comparisons among the simulation scenarios. The relationships between the safety indicators, including the frequency of the lane departures and the standard deviation of the lateral acceleration, and the design elements were explored in terms of lateral driving safety. RESULTS : The length of the combined alignment was found to be a significant factor affecting the lateral driving safety based on the analysis of the frequency of lane departures. Regarding the standard deviation of the lateral acceleration, it was identified that the length of the horizontal curve, the length of the bridge, and the right-side superelevation must be considered significant factors associated with driving safety while designing the road under high-speed driving conditions. CONCLUSIONS : Based on the findings of this study, a set of recommendations for designing roads was proposed. For example, the proper length of the combined alignment and the horizontal curve should be determined to prevent crashes due to hazardous lateral driving events because the installation of sufficient superelevation in the bridge section would be limited under high-speed driving conditions. In addition, applying a larger horizontal curve radius with longitudinal grooving is a promising approach to tackle risky driving conditions.

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.

기계 중심의 자동차에 전기/전자 부품의 장착이 증가함에 따라 자동차 내 전기/전자(EE) 시스템에 대한 기능 안전 설계 요구가 대두되고 있다. IEC 61508 국제 표준은 모든 종류의 산업에 적용 가능한 기본적인 기능 안전 표준으로 작성되었다. 자동차 분야에서는 ISO 26262가 특화된 기능 안전 표준으 로 적용되고 있다. ISO 26262는 자동차의 기술적 복잡도 증가, 소프트웨어 내용 및 메카트로닉스 증가 추세와 함께 시스템적 고장(systematic failure)과 하드웨어의 우발 고장(random hardware failure)에 의한 리스크를 방지하기 위한 지침을 포함하고 있다. ISO 26262는 자동차 안전 수명 주기(관리, 개념, 시스템 개발, 하드웨어 개발, 소프트웨어 개발, 생산, 운용, 서비스, 폐기)를 제공하고, 각수명 주기 단계 별 필요한 안전 지침을 제공하고 있다. 최근 자율주행 차량(automatic driving) 및 전기자동차모빌 리티(e-mobility) 등에 대한 전 세계적 관심이 증가하고 이에 따른 기능 안전 설계의 중요성이 더욱 증가하는 시점에서 자동차 기능 안전에 대한 올바른 개념 이해는 자동차의 안전 설계를 넘어 자동차의 사용성 및 사용자 경험을 높이기 위한 기반을 마련하는 데 필수적이라 할 수 있다. 본 발표를 통해 자동차 안전 설계 개념을 이해하고, 안전 설계를 지원하고 보장하기 위한 아이디어를 제공하기를 기대한다.

국토교통부는 매년 고속도로에서 평균 3만∼4만 건에 달하는 화물적재규정 위반 등 운행제한차량에 대해 단속·적발하고 있지만 도로 낙하물 사고는 매년 증가하고 있는 추세이다. 또한, 지속적인 도로연장의 증가, 도로와 산림 및 녹지간 공간적 거리가 가까워짐에 따라 단절된 녹지를 통과하기 위해 도로로 뛰어드는 야생동물들로 인해 로드킬 발생 빈도도 매년 증가하고 있다. 도로 낙하물로 인한 교통사고는 연쇄 추돌사고 등 대형사고로 이어질 확률이 매우 높다. 따라서 도로 낙하물로부터 운전자뿐만 아니라 이를 처리하는 도로작업자들을 보호할 수 있는 예방책을 마련할 필요가 있다. 기존 도로 청소차의 경우 도로에 떨어진 부피가 큰 쓰레기 및 적재물이나 로드킬 등의 낙하물을 제거하지 못하는 문제점이 있다. 그러나 본 연구에서는 로드킬, 타이어 조각, 종이박스, 나무판넬 등 부피가 있는 낙하물 까지 수거가 가능하도록 설계‧개발하였다. 국내의 경우 일부 기관에서 이미 낙하물 수거 장비를 사용하고 있지만 안전성에 대한 검증이 이루어지지 않았기 때문에, 특수 도로관리 목적으로 고속도로 등 연속류에만 제한적으로 사용되고 있다. 따라서 본 연구를 통해 설계‧개발된 도로 낙하물 수거 장비는 도로 상의 로드킬을 포함한 각종 낙하물들을 신속하고 안전하게 수거할 수 있다. 국내의 경우 도로 낙하물 및 로드킬로 인한 사고는 지속적으로 증가하는 추세지만, 외국과 달리 비교적 소형 낙하물이 주류를 이루고 있기 때문에 아직까지 심각성이 대두되지 못하고 있다. 고규격 도로 및 차량 보유 대수 증가로 인해 도로 낙하물 및 로드킬로 인한 도로작업자 안전사고 및 2차 추돌사고 문제는 매우 심각해질 전망이다. 기존 청소차 및 낙하물 수거 장비의 경우 도로변의 사고잔해, 유리, 철재 등 부피가 작은 낙하물에 대해서만 수거가 가능하다. 그러나 도로 낙하물 수거 장비의 개발이 완료될 경우, 처리하기 힘든 로드킬, 타이어 조각, 종이박스, 나무판넬 등 부피가 있는 낙하물 까지 신속하게 수거가 가능하므로 도로 유지관리업무 및 교통안전 확보에 많은 도움이 될 것으로 판단된다.

Recently, the damage caused by typhoons and strong winds frequently displayed according to world climate change tends to be increasing. In the case of soundproof / windproof wall installed on the road, frequent occurrence does function for damage due to strong wind. As a result, in this study, strong wind fragility evaluation was performed to predict the degree of damage of strong winds of soundproof / windproof walls. We were conducting research focusing on the destruction mode in which the overall destruction of the sound barrier caused by the destruction of the aluminum frame occurs. Three node bending experiments were conducting for grasping the material properties of a soundproof wall aluminum frame that is currently being constructed on a road. Based on the results of this experiment, the resistance performance of the target structure was calculated, the frame breakage was selected as the limit state, and the wind load acting on the simplified soundproof wall model was measured using the Monte Carlo model model technique to measure.From now on, through the additional study, it will be necessary to proceed with a more accurate evaluation of the safety against strong windsof the soundproof wall structure using the vulnerability evaluation execution and the setting of the limit state.This study is expected to be the basic data of the study on prediction technique of wind - induced damage of soundproofing and windshield walls in the future.