The automotive industry is rapidly shifting from hardware-focused design to Software Defined Vehicles (SDVs), where functions are flexibly updated through software. Embedded systems are central to this transition, ensuring real-time data processing and control across sensors, actuators, and controllers. Yet, most autonomous driving education and competitions have been designed for senior students, creating high entry barriers for early undergraduates. This study proposes an embedded practice-based education model for lower-year students, implemented through an autonomous driving competition. Arduino was adopted as an accessible embedded platform, enabling rapid prototyping and intuitive learning of sensor–controller–actuator integration. The curriculum was structured to advance from interrupt-based programming to Real-Time Operating System (RTOS)-based task scheduling, providing stepwise exposure to core SDV concepts. The model was validated through a mission-oriented competition that included line following, obstacle avoidance, and stop-line detection tasks. Dual assessment—combining technical performance indicators with rubric-based educational outcomes— demonstrated both algorithmic feasibility and pedagogical effectiveness. This work highlights that early undergraduates can gain meaningful SDV-oriented embedded control experience through lightweight competitions. The proposed framework offers an effective pathway for cultivating the next-generation mobility workforce, bridging the gap between theoretical education and practical implementation in the SDV era.

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.

본 연구는 레벨 3 자율주행의 운전이양권(TOR) 안전성 향상을 위해, 기존 행동 기반 감지 방식의 한계를 극복하 는 운전자 모니터링 시스템(DMS)을 개발했다. 차량의 미러 내장형 RGBW 카메라를 이용한 비접촉 원격 광용적맥 파(rPPG) 기술로 운전자의 심박수를 실시간 측정하고, 심박변이도(HRV) 분석을 통해 졸음, 스트레스 등 운전자의 각성 수준을 판단한다. 딥러닝 기반 얼굴 인식, 신호 처리, 패턴 인식 알고리즘을 통합하여 시스템을 구현했다. 총 28명을 대상으로 105시간 이상의 실제 도로 환경에서 검증한 결과, 심전도(ECG) 대비 85.14%의 심박수 측정 정확 도와 90.81%의 상태 판단 정확도를 달성했다. 본 연구는 생체신호 기반의 운전자 상태 평가가 TOR 판단의 신뢰성 을 높이는 핵심 기술이 될 수 있음을 실증했다.

이동로봇은 인공지능, 센서 기술 등과 융합함으로서 다양한 산업 및 서비스 분야에서 광범위하게 사용되고 있으며, 조선 및 해 양 분야에서도 이동로봇을 활용한 물품 운반, 현장 모니터링, 위험한 업무 등에 대한 연구가 수행됨으로서 생산성 향상 및 안정성 강화를 향상시키고자 하고 있다. 본 연구에서는 선박기관실처럼 내연기관, 선반, 드릴머신, 공구대, 용접실습대 등 다양한 기기 및 장비의 간격이 좁고 구조가 복잡한 환경의 기관실습실 내에서 이동로봇의 자율주행을 구현함으로서 선박기관실에 적용 가능여부를 확인하고자 하였다. ROS2기반의 이동로봇으로 SLAM 라이브러리 중 하나인 Cartographer를 사용하여 지도를 작성한 후 여러 위치에서 자율주행 시험과 지도에 없는 장애물을 놓은 경우 자율주행 시험결과 복잡한 환경에서도 높은 자율주행 성능을 확인하였다. 선박기관실은 실험한 장소와 여러환 경의 차이는 있으나 구조의 변화가 거의 없어 자율주행이 가능할 것으로 사료된다.

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.

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.

Korea has many test beds where various mobility services are provided by automated vehicles. The test beds are operated in their operational design domain (ODD). However, disengagement frequently occurs, even in the ODDs of automated vehicles. In particular, human drivers have to take control of the automated vehicles at SAE Level 3 whenever the vehicles cannot drive by themselves because of an emergency or unknown factors. This study analyzed the driving safety of right turning at signalized intersections where automated vehicles face selfdriving issues because of potential conflicts with other vehicles, crossing pedestrians, and geometric factors. To conduct this analysis, we categorized right-turning intersections into two types with right-turning lanes and channelization islands and divided them into three sections, with a total of six sections. Subsequently, the six sections were compared with each other by disengagements of the automated vehicles as the key index to investigate their self-driving safety. Their significant differences indicate that ODD-related variables must be considered when designing and updating target test beds for automated vehicles.