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.

This paper presents a novel methodology for assessing the vulnerabilities of autonomous vehicles (AVs) across diverse operational design domains (ODDs) related to road transportation infrastructure, categorized by the level of service (LOS). Unlike previous studies that primarily focused on the technical performance of AVs, this study addressed the gap in understanding the impact of dynamic ODDs on driving safety under real-world traffic conditions. To overcome these limitations, we conducted a microscopic traffic simulation experiment on the Sangam autonomous mobility testbed in Seoul. This study systematically evaluated the driving vulnerability of AVs under various traffic conditions (LOSs A–E) across multiple ODD types, including signalized intersections, unsignalized intersections, roundabouts, and pedestrian crossings. A multivariate analysis of variance (MANOVA) was employed to quantify the discriminatory power of the evaluation indicators as the traffic volume was changed by ODD. Furthermore, an autonomous driving vulnerability score (ADVS) was proposed to conduct sensitivity analyses of the vulnerability of each ODD to autonomous driving. The findings indicate that different ODDs exhibit varying levels of sensitivity to autonomous driving vulnerabilities owing to changes in traffic volume. As the LOS deteriorates, driving vulnerability significantly increases for AV–bicycle interactions and AV right turns at both signalized and unsignalized intersections. These results are expected to be valuable for developing scenarios and evaluation systems to assess the driving capabilities of AVs.

오늘날 도로의 이동수단은 자율주행자동차와 더불어 전동 킥보드, 전기 자전거 등과 같은 개인형 이동수단의 등장으로 인해 기존 도 로가 수용해야 할 범위는 더욱 광범위해졌으며, 개인형 이동장치의 시장 확대 및 공유서비스로 인한 개인형 이동장치의 교통사고는 급격하게 증가하고 있는 추세이다. 기존 자동차 중심의 설계 및 운영되고 있는 현재의 도로에서는 자동차 이외의 다른 교통수단 이용 자들은 인프라 시설과 서비스 면에서 안전하지 못하고 편리하지 못한 환경으로 인해 잦은 교통사고 발생과 대중교통 이용 기피 등의 문제로 이어지고 있다. 따라서, 현재 도로의 차량 중심 설계에 의한 한계가 드러나고 있으며 이에 대한 해결책으로 모든 도로 교통수 단 및 이용자가 고려되는 완전도로(Complete Streets)에 관한 관심이 증가함에 따라 완전도로 구축에 관한 정책이 필요한 실정이다. 이 에 본 연구에서는 완전도로 구축을 위해 미시적 교통 시뮬레이션 VISSIM을 활용하여 자율주행자동차 레벨 4 수준의 혼입에 따른 교 통흐름 변화를 분석하여 완전도로 구축을 위한 잉여차로 확보 가능성을 검증하는 분석을 진행하였다. 또한, 잉여차로를 활용하여 완전 도로를 구축하기 위해 국외 완전도로 디자인 매뉴얼을 참고하여 국내 도로의 적용이 가능한 평자지표를 안전성, 형평성, 쾌적성을 고려한 요인을 설정하였으며, 계층화 분석법(Analytic Hierarchy Process, 이하 AHP)을 통해 평가요인별 중요도 가중치를 산정하여 완전도로 구축을 위한 완전도로 서비스수준 산정방안을 제 시하였다. 완전도로 구축을 위한 모바일매핑시스템(MMS) 및 인공지능, 드론(UAV)을 활용하여 도로의 현황 모니터링을 진행하였으며, 도출된 평가지표와 가중치를 활용하여 대상 구간에 적용 및 비교를 위해 완전도로 개념과 가깝게 적용된 세종시의 한누리대로와 비교 ㆍ분석하였다. 이를 토대로 완전도로 서비스수준 적용을 통해 도출된 도로의 한계점을 보완한 완전도로 구축방안을 제시하고자 한다.

This study collected video footage of accident-risk scenarios on actual roads using automobiles and motorcycles. A total of 191,500 km was driven with three vehicles and one motorcycle, capturing 6,550 near-miss accident videos. The footage was analyzed and categorized based on the 27 parameters of the iGLAD(Initiative for the Global Harmonization of Accident Data) accident categories. Parameters difficult to classify under iGLAD were localized to fit domestic conditions, and further analysis identified areas needing optimization. The categorized data was organized into a web-based database platform, providing statistical analysis and search functions for scenario development. Future use of this data will support the creation of safety evaluation scenarios for autonomous vehicles, enhancing traffic accident investigation and analysis systems. Expanding the database to include data from secondary roads and parking areas is expected to increase its applicability and value.

PURPOSES : This study aimed to derive the factors that contribute to crash severity in mixed traffic situations and suggest policy implications for enhancing traffic safety related to these contributing factors. METHODS : California autonomous vehicle (AV) accident reports and Google Maps based on accident location were used to identify potential accident severity-contributing factors. A decision tree analysis was adopted to derive the crash severity analyses. The 24 candidate variables that affected crash severity were used as the decision tree input variables, with the output being the crash severity categorized as high, medium, and low. RESULTS : The crash severity contributing factor results showed that the number of lanes, speed limit, bus stop, AV traveling straight, AV turning left, rightmost dedicated lane, and nighttime conditions are variables that affect crash severity. In particular, the speed limit was found to be a factor that caused serious crashes, suggesting that the AV driving speed is closely related to crash severity. Therefore, a speed management strategy for mixed traffic situations is proposed to decrease crash severity and enhance traffic safety. CONCLUSIONS : This paper presents policy implications for reducing accidents caused by autonomous and manual vehicle interactions in terms of engineering, education, enforcement, and governance. The findings of this study are expected to serve as a basis for preparing preventive measures against AV-related accidents.

자율주행차가 보급되어 도로에서 사람 운전자와 함께 운영되는 미래가 다가오고 있다. 사람 중심으로 운영되는 도로 체계가 자율주 행차와 공존하는 형태로 변화하고 있으며, 도로 시스템도 사람 운전자와 자율주행차가 혼재된 혼합교통류를 대상으로 변화하고 있다. 현재 도로에서는 예상하지 못한 상황들이 다양하게 발생한다. 교통사고, 도로 낙하물 등 교통흐름에 영향을 주는 상황들이 발생하며, 대응을 위한 전략들이 각 지방자치단체에서 준비되어 있다. 미래 교통상황에는 도로상에 자율주행차가 혼재되어 있으며 이를 포함하 는 돌발 및 재난상황에 대한 제어전략은 아직 부재하다. 본 연구에서는 돌발 및 재난상황 발생 시 자율주행차 제어전략에 대한 설계 방안을 제안한다. 돌발 및 재난상황 범위에 대해 정의하며, 상황 구분을 위한 기준을 제시하여 각 상황에서 자율주행차가 안전하게 대 응할 수 있도록 제어전략을 제시한다.

PURPOSES : This study explores the preference of shared autonomous vehicle service in an underground dedicated environment. METHODS : A stated preference survey was conducted to examine the mode choice behaviors on autonomous vehicle service competing with existing modes. Multinomial logit was employed to estimate the parameters of explanatory variables from the surveyed data. The model was estimated with alternative specific parameters rather than generic parameters. The value of time was also estimated using the parameters of the mode choice model. RESULTS : The results showed that the travel cost had the highest sensitivity to public transportation and the lowest to private cars. We also found that the value of the in-vehicle travel time was highest for private cars, lowest for public transportation, and intermediate for SAVs, suggesting that SAVs could serve as a premium public transport option. Additionally, the out-vehicle time coefficient was higher for public transportation compared to that for SAVs, indicating that users are more willing to tolerate longer out-vehicle times for SAVs due to their high-speed service compared to that of public transportation. CONCLUSIONS : This study presents a direction for policy regarding the adoption of shared autonomous vehicle services by considering the attributes that are valued by users of each mode.

PURPOSES : Even when autonomous vehicles are commercialized, a situation in which autonomous vehicles and regular drivers are mixed will persist for a considerable period of time until the percentage of autonomous vehicles on the road reaches 100%. To prepare for various situations that may occur in mixed traffic, this study aimed to understand the changes in traffic flow according to the percentage of autonomous vehicles in unsignalized intersections. METHODS : We collected road information and constructed a network using the VISSIM traffic simulation program. We then configured various scenarios according to the percentage of autonomous vehicles and traffic volume to understand the changes in the traffic flow in the mixed traffic by scenario. RESULTS : The results of the analysis showed that in all scenarios, the traffic flow on major roads changed negatively with the mix of autonomous vehicles; however, the increase or decrease was small. By contrast, the traffic flow on minor roads changed positively with a mix of autonomous vehicles. CONCLUSIONS : This study is significant because it proactively examines and designs traffic flow changes in congested traffic that may occur when autonomous vehicles are introduced.

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.

PURPOSES : This study aims to understand the characteristics of accidents involving autonomous vehicles and derive the causes of accidents from road spatial information through autonomous vehicle accident reports. METHODS : For this study, autonomous vehicle accident reports collected and managed by the CA DMV were used as data sources. In addition, spatial characteristics and geometric data for accident locations were extracted by Google maps. Based on the collected data, the study conducted general statistics, text embedding, and cross-analysis to understand the overall characteristics of autonomous vehicle accidents and their relationship with road spatial features. RESULTS : The analysis results for characteristics of autonomous vehicle accidents, applying statistical analysis and text embedding techniques, reveal that the damages caused by autonomous vehicle accidents are often minor, and approximately half of the accidents are triggered by other vehicles. It is noteworthy that accidents where autonomous vehicles are at fault are not uncommon, and when the cause of the accident is within the autonomous vehicle, the accident risk can increase. The accident analysis results using spatial data showed that the severity of accidents increases when on-street parking is present, when dedicated lanes for bicycles and buses exist, and when bus stops are present. CONCLUSIONS : Through this study, geometric and spatial elements that appear to have an impact on autonomous driving systems have been identified. The findings of this study are expected to serve as foundational data for improving the safety of autonomous vehicle operations in the future.

최근 급격한 변화를 겪고 있는 자율주행 자동차 분야의 미래 기술 및 시장 전 망 예측에 대한 요구와 관심이 집중되고 있다. 자동차 산업의 특성상, 복합적 요인의 상관관 계가 미치는 영향력이 크고 요인 간의 복잡도가 높으므로, 체계적인 미래 예측 방법론 적용 을 통한 미래 전망분석 및 전략 수립이 시급하다. 본 연구에서는 자동차 분야에 적합한 미래 예측 방법론 중 필드 변칙 완화기법(Field Anomaly Relaxation)과 다중관점 개념 기법 (Multiple Perspective Concept)을 복합적으로 적용하여, 자율주행 자동차 분야의 핵심기술 및 산업 동향에 관한 미래 시나리오들을 개발하여 실증하였다. 도출된 3개의 시나리오는 전 문가 평가 체크리스트를 통하여 타당성을 검증하였다. 본 연구 결과는 자율주행 자동차 산업 과 같은 다양한 변동성이 존재하는 분야의 미래 예측 방법 중 한 가지로 적용될 수 있다는 점에 의의가 있다.

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.

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.

본 연구에서는 뇌파와 맥파를 이용한 생체신호 분석을 통해 자율주행차량 사용자에게 안정감을 유발하는 최적의 색온도를 제안하고자 하였다. 이를 위해 3000 K, 4000 K, 5000 K, 6000 K의 색온도를 갖는 조명을 자율주행 환경에 적용하여 제시하였다. 실험은 자동차 그래픽 시뮬레이터가 구비된 실험실에서 진행되었으며, 실험절차는 다음과 같다. 1) 안정기(5분), 2) U-K테스트(3분), 3) 자율주행+조명(3분). 이 과정은 색온도를 변경해가며 총 4회 반복되었다. 수집된 시계열데이터에 대해 주파수 분석을 실시하였고 파워 스펙트럼 분석을 통해 주파수 대역별 power값을 산출하였다. 뇌파는 안정의 지표인 α파와 각성의 지표인 β에 대해 분석을 실시하였으며, 맥파의 경우 교감신경계 활성도에 대해 분석을 실시하였다. 산출된 데이터는 연구대상자 개인 간 편차를 줄이기 위해 정규화하여 통계분석을 실시하였다. 1차 분석 결과, 뇌파의 경우 5000 K의 조명을 제시하였을 때 α파가 가장 높았고, 대부분의 조명 제시 상황에서 β파가 증가하였다. 맥파의 경우 주행 상황에서 SNSA가 증가하였다. β파와 SNSA에 대한 2차 분석 결과, 유의수준 5%에서 색온도 간 유의한 차이가 인정되지 않았다. 결론적으로, α파가 가장 높은 5000 K의 색온도가 안정감을 유발한다고 볼 수 있다. 이러한 결과를 자율주행차량에 적용한다면, 탑승자의 높은 만족도를 유발할 수 있을 것으로 보인다. 나아가, 이와 같은 긍정적인 효과가 자율주행차량의 수용으로 이어질 수 있다.

스스로 완벽하게 목적지까지 주행하는 완전 자율주행 자동차 시대가 가까운 미래에 도래할 것으로 예상되면서 자동차 산업은 1800년대 후반에 자동차가 탄생한 이래 큰 변화를 꾀해야 할 중대한 전환기를 맞이하고 있다. 완전 자율주행 자동차는 단순한 이동수단의 개념을 넘어 생활공간으로 정의되어지며, 앞으로 실내공간을 중심으로 큰 변화가 나타나게 될 것이라고 전망되고 있으나 완전 자율주행 자동차 실내공간 디자인에 대한 기존 연구는 미비한 실정이다. 이에 본 연구는 생활공간이라는 개념에 적합하도록 사용자의 감성적 경험을 충족시켜줄 수 있는 근본적인 디자인 요소인 실내행위에 대한 분석을 통해 실내공간 디자인 방안을 제안하고자 한다. 이를 위해 완전 자율주행 자동차의 실내행위에 대한 소비자 의식을 조사하였다. 분석 결과 국내 소비자들은 완전 자율주행 자동차 내에서 ‘수면 및 휴식’, ‘TV 및 영화 시청’, ‘음악감상’ 등 개인 중심 행위 중에서도 휴식 및 감상 활동과 관련된 실내행위에 대한 필요 성을 인지하고 있었으며, 본 연구 결과를 바탕으로 완전 자율주행 자동차 실내공간 디자인 방안을 제안하였다. 본 연구는 소비자 니즈(needs)를 정량적 방법으로 분석하여 실내공간 디자인 방안을 새로운 시각으로 제시한 선도적인 연구라는 점에서 중요한 의미를 지닌다.