In this study, we comparatively analyzed the efficiency of conventional image recognition methods and propose a digital information provisioning method for autonomous vehicle traffic safety facility recognition. We evaluated the practicality of both approaches from the perspective of autonomous vehicles' capabilities of processing regulatory information and the distribution of legal responsibility. Comprehensive field experiments were conducted at 9 major intersections in the Pangyo Techno Valley area of Hwaseong City over a 10- day period from July 12-23, 2021. Three test vehicles equipped with in-vehicle terminals and video cameras collected data through 300 driving scenarios, including 240 during peak hours and 60 during off-peak periods. The proposed digital information provision method exhibited superior performance, achieving a 100.0 % recognition success rate across all test scenarios and road conditions. In contrast, the conventional image recognition method exhibited significant variability in performance, ranging from 56.9 % in underpass conditions to 95.9 % in areas with communication interference, with an overall average of 70.8 %. The digital information provision method demonstrated superior performance compared to conventional image recognition approaches for autonomous vehicle regulatory compliance. The proposed approach delivered consistent and reliable information regardless of physical obstacles or environmental conditions. This method ensures complete comprehension of regulatory information, which is essential for establishing clear legal responsibility frameworks in autonomous driving environments.

In this study, we propose a data-driven analytical framework for systematically analyzing the driving patterns of autonomous buses and quantitatively identifying risky driving behaviors at the road-segment level using operational data from real roads. The analysis was based on Basic Safety Message (BSM) data collected over 125 days from two Panta-G autonomous buses operating in the Pangyo Autonomous Driving Testbed. Key driving indicators included speed, acceleration, yaw rate, and elevation, which were mapped onto high-definition (HD) road maps. A hybrid clustering method combining self-organizing map (SOM) and k-means++ was applied, which resulted in eight distinct driving pattern clusters. Among these, four clusters exhibited characteristics associated with risky driving such as sudden acceleration, deceleration, and abrupt steering, and were spatially visualized using digital maps. These visualizations offer practical insights for real-time monitoring and localized risk assessment in autonomous vehicle operations. The proposed framework provides empirical evidence for evaluating the operational safety and reliability of autonomous buses based on repeated behavioral patterns. Its adaptability to diverse urban environments highlights its utility for intelligent traffic control systems and future mobility policy planning.

In this study, we investigated and analyzed the impact of changes in driving speed and inter-vehicle distance on users’ perceived tension during autonomous vehicle operation. To this end, a survey experiment was conducted for both urban roads and highways. The results show that the greatest changes in perceived tension occurred in the range of 50–70 Km/h and 50–70 m following distance on urban roads, and in the range of 80–100 Km/he and 60–80 m following distance on highways. Furthermore, modeling user behavioral responses to perceived tension based on changes in speed and following distance revealed that linear models best described the relationship for speed on both urban roads and highways. For the following distance, a quadratic model was the most suitable for urban roads, whereas a logarithmic model best fit the highway data. These findings are expected to contribute to practical operational guidelines for autonomous vehicles by alleviating users’ psychological discomfort and enhancing public acceptance. Future research will extend this study using a driving simulator to examine user responses in more realistic driving environments.

In the Autonomous Mobility Living Lab, traffic situations with both autonomous vehicles (AVs) and ordinary vehicles driven by humans (HDVs) are explored. Research on countermeasures and efficient transportation management plans has emerged from this context. In this study, we analyzed the effect of AVs with different speeds on signal intersections and road networks to derive efficient traffic operation plans for roads on which various AVs and HDVs with different driving behaviors are mixed in Living Lab cities. To that end, we conducted a simulation-based analysis of the effects of AV mixing rates on continuous signal intersections and the road network in traffic situations where AVs and HDVs were mixed at peak and non-peak driving hours. The simulation scenario was designed by classifying the traffic volume levels at peak and non-peak times and defining various AV mixing rates; we also set the driver behaviors of the AVs as either conservative or aggressive. By performing a small-scale traffic simulation, the average control delay, average stopped delay, average queue length, and average travel time of the signal intersection for each scenario were derived, and the impact of the AV mixing rate on traffic operation was analyzed. The results of the analysis show that higher AV mixing rates were associated with lower measurements of the effectiveness of signal intersections, which had a positive effect on traffic operation. This resulted in a stable and efficient improvement of the traffic flow at intersections as more vehicles passed through at the time of the allocated signal, as the AVs in the simulation could be driven at short intervehicle intervals by receiving real-time traffic information. In the traffic operation on the network, we found that the higher the AV mixing rate, the lower the average travel time, resulting in a greater effect of facilitating the traffic flow of the urban network. These simulated results indicate that higher AV mixing rates were associated with positive outcomes in terms of signal intersections and network traffic operation. We expect that this simulation can be used to establish real traffic operation plans in traffic situations where AVs are mixed at each stage of autonomous driving technology in the future.

Autonomous vehicles are widely expected to be commercialized in the near future. This would naturally lead to situations in which existing vehicles and autonomous vehicles would be on the road at the same time, which would pose a notable hazard to traffic safety. From this perspective, high-risk factors relating to this deployment should be identified to prepare measures to promote traffic safety. However, at this point, deriving high-risk factors based on actual data is problematic because autonomous vehicles have not yet been widely commercialized. In this study, we derive high-risk factors that would apply if autonomous vehicles were allowed to drive alongside vehicles driven by humans using a meta-analysis. We synthesized factors related to autonomous vehicles mentioned in the relevant literature. An analysis was conducted based on a total of 58 documents according to five keywords related to autonomous vehicles (crash factors, scenarios, predictive models, laws, and regulations). We also performed a binary meta-analysis of factors related to autonomous vehicles according to these keywords and a meta-analysis of effect size according to the relative size of factors to evaluate them comprehensively. We found that many different aspects of driving such as navigating intersections, lanes, fog, rain, acceleration and deceleration, rear-end collisions, inter-vehicle spacing, and pedestrian collisions were notable as high-risk factors. This study provides basic data to identify high-risk factors to support the development of related prediction models.

This study aims to analyze the mitigation effects of phantom traffic jams on highways in a mixed traffic environment in which autonomous vehicles (AVs) and human-driven vehicles coexist. It focuses on identifying the key factors that contribute to phantom congestion and evaluating the extent to which the introduction of AVs can stabilize traffic flow and alleviate nonrecurring congestion. To achieve this goal, a theoretical analysis was conducted to examine the major causes of phantom traffic jams, including variations in the vehicle speed, road gradients, driver behaviors (for example, acceleration and deceleration), and visual adaptations in tunnel sections. Based on these factors, simulation scenarios were constructed using VISSIM to replicate real-world conditions in highway tunnel segments. The scenarios varied according to the AV penetration rate (0%, 20%, 40%, and 60%) and incorporated key traffic indicators such as the vehicle composition, speed, and headway. Traffic flow stability was evaluated using metrics including the average travel speed, headway consistency, and frequency of acceleration and deceleration events across sections. The simulation results showed that as the proportion of AVs increased, the average travel speed improved, and both the headway stability and flow continuity were enhanced. In particular, tunnel segments with higher AV ratios experienced fewer deceleration events and reduced behavioral variability, contributing to a more stable traffic flow. These findings suggested that AVs could play a critical role in mitigating phantom traffic jams by maintaining steady speeds and safe following distances, thereby reducing the instability caused by human driving behaviors. This study offers a foundational reference for future traffic congestion mitigation strategies and AV policy development, particularly in anticipation of increasingly mixed traffic environments.

이 연구는 자율운항선박의 도입이 기존 감항능력 개념의 변화를 초래함에 따라 자율운항선박에 요구되는 새로운 감항능력의 요건을 분석하고 그 법적 개 념이 어떻게 재정립되어야 하는지를 탐구한다. 그리고 이러한 변화로 인해 발 생할 수 있는 상사법적 분쟁에 대비하기 위해 다양한 상사법적 쟁점을 파악하 고, 이에 대응하기 위한 제도적 기반 마련에 선행 연구로서 기여하는 데 그 목 적이 있다. 자율운항선박은 자율운항 수준에 따라 Level 1∼4로 구분되며 각 단계별로 요구되는 감항능력이 상이하여 선원과 원격운항자에 대한 인적 측면 과 인공지능 시스템과 사이버 보안에 대한 물적 측면으로 나누어 분석하였다. 이를 바탕으로 자율운항선박의 운항에 수반되는 주요 상사법적 쟁점에 관한 주 요 법적 문제를 다루었다. 인적 감항능력에 대한 분석을 통해 자율운항 기술의 도입으로 선원의 수와 역할이 축소되는 한편 원격운항자의 역할이 강화됨에 따라 발생 가능한 상사법 적 쟁점을 자율운항 단계별로 구분하여 고찰하였다. Level 2 이하에서는 선원, Level 2 이상에서는 원격운항자에 대한 감항능력 확보 방식과 그 평가 기준 마 련이 중요한 과제로 논의되었으며 국내외 규정과 판례 분석을 바탕으로 적절한 평가 기준을 제시하였다. 특히, 원격운항자의 등장은 기존 법적 체계에 존재하 지 않던 인적요소로서 보다 심도 있는 검토가 요구됨에 따라 연구 사례 분석을 통하여 원격운항자의 정의와 역할을 명확히 규정하고, 원격운항자에 대한 자격 신설을 제시하였다. 아울러, 해상운송인의 인적 감항능력주의의무에 따른 법적 책임과 항해과실에 대한 면책 규정의 제외 가능성에 대해 검토하였다. 물적 감항능력에 대해서는 자율운항선박의 안전 운항에 있어 소프트웨어 시 스템과 사이버 보안의 감항능력이 중요한 요소로 부각됨에 따라 이와 관련된 상사법적 쟁점을 분석하였다. 특히, Level 4에서는 인공지능 시스템에 대한 감 항능력 확보와 평가 부분에서 법적 미비점이 존재하며 상사법적 분쟁의 소지가 많을 것으로 판단하여 소프트웨어 시스템 중에서도 인공지능 시스템을 중심으 로 고찰하였다. 자율운항선박의 인공지능 시스템에 대한 제조물 책임 문제를 논의하였다. 아울러, 숨은 하자에 대한 면책 규정의 적용 가능성까지 확장하여 살펴보았다. 사이버 보안에 대한 감항능력 확보와 평가에 관해서는 감항능력주 의의무의 이행 시기 및 상당한 주의 정도를 검토하였다. 아울러, 기존 해적행 위에 대한 면책 규정의 적용 가능성을 검토하였다.

This study investigates a vision-based autonomous landing algorithm using a VTOL-type UAV. VTOL (Vertical Take-Off and Landing) UAVs are hybrid systems that combine the forward flight capability of fixed-wing aircraft with the vertical take-off and landing functionality of multirotors, making them increasingly popular in drone-based industrial applications. Due to the complexity of control during the transition from multirotor mode to fixed-wing mode, many companies rely on commercial software such as ArduPilot. However, when using ArduPilot as-is, the software does not support the velocity-based GUIDED commands commonly used in multirotor systems for vision-based landing. Additionally, the GUIDED mode in VTOL software is designed primarily for fixed-wing operations, meaning its control logic must be modified to enable position-based control in multirotor mode. In this study, we modified the control software to support vision-based landing using a VTOL UAV and validated the proposed algorithm in simulation using GAZEBO. The approach was further verified through real-world experiments using actual hardware.

Autonomous vehicle (AV) technology is rapidly entering the commercialization phase driven by advancements in artificial intelligence, sensor fusion, and communication-based vehicle control systems. Real-world road testing and pilot deployments are increasingly being conducted, both domestically and internationally. However, ensuring the safe operation of AVs on public roads requires not only technological advancement of the vehicle itself but also a thorough pre-evaluation of the road environments in which AVs are expected to operate. However, most previous studies have focused primarily on improving internal algorithms or sensor performance, with relatively limited efforts to quantitatively assess how the structural and physical characteristics of road environments affect AV driving safety. To address this gap, this study quantitatively evaluated the compatibility of road environments for AV operation and defined the road conditions under which AVs can drive safely. Three evaluation scenarios were designed by combining static factors such as curve radius and longitudinal gradient with dynamic factors such as level of service (LOS). Using the MORAI SIM autonomous driving simulator, we modeled the driving behaviors of autonomous vehicles and buses in a virtual environment. For each scenario, the minimum time to collision (mTTC) from the moment the AV sensors detected a lead vehicle was calculated to assess risk levels across different road conditions.The analysis revealed that sharper curves and lower service levels resulted in significantly increased risk. Autonomous buses exhibited a higher risk on downhill segments, autonomous vehicles were more vulnerable to uphill slopes and gradient transitions. The findings of this study can be applied to establish road design standards, develop pre-assessment systems for AV road compatibility, and improve AV route planning and navigation systems, thereby providing valuable implications for policy and infrastructure development.

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.