Until all vehicles are equipped with autonomous driving technology, there will inevitably be mixed traffic conditions that consist of autonomous vehicles (AVs) and manual vehicles (MVs). Interactions between AVs and MVs have a negative impact on traffic flow. Cloverleaf interchanges (ICs) have a high potential to cause traffic accidents owing to merging and diverging. Analyzing the driving safety of cloverleaf ICs in mixed traffic flows is an essential element of proactive traffic management to prevent accidents. This study proposes a comprehensive simulation approach that integrates driving simulation (DS) and traffic simulation (TS) to effectively analyze vehicle interactions between AVs and MVs. The purpose of this study is to identify hazardous road spots for a freeway cloverleaf IC by integrating DS and TS in mixed traffic flow. The driving behavior data of MVs collected through a DS were used to implement vehicle maneuvering based on an intelligent driver model in the TS. The driving behavior of the AVs was implemented using the VISSIM parameters of the AVs presented in the CoEXist project. Additionally, the market penetration rate of AVs, ranging from 10% to 90% in 10% increments, was considered in the analysis. Deceleration rate to avoid crashes was adopted as the evaluation indicator, and pinpointing hazardous spot technique was used to derive hazardous road spots for the cloverleaf IC. The most hazardous road spot was identified in the deceleration lane where greater speed changes were observed. Hazardous road spots moved downstream within the deceleration lane as traffic volumes increased based on level of service. The number of AVs decelerating stably increased as traffic increased, thereby improving the safety of the deceleration lane. These results can be used to determine the critical point of warning information provision for preventing accidents when introducing AVs.

Motorcycles are becoming a major means of transportation in the delivery industry because of their mobility and economic feasibility, and their use is increasing with the spread of non-face-to-face culture. However, owing to the absence of a systematic maintenance and inspection system, illegal modifications, and a lack of safety education, the possibility of accidents is increasing, and social problems are intensifying. To address this issue, we aim to find ways to improve motorcycle safety. Problems were identified by registering motorcycles, driver crashes, and surveys of the current status of laws and systems. Subsequently, a questionnaire was administered to assess the actual conditions and perceptions regarding motorcycles. Finally, to analyze the driving characteristics of delivery motorcycles, traffic safety education was conducted for new delivery riders, and the driving characteristics were analyzed by collecting driving record data. In this study, a plan to enhance the license system, education, insurance, and educational programs is proposed to strengthen motorcycle safety. The licensing system needs to be elevated by age and classified by displacement, and delivery riders can improve their driving skills through mandatory traffic safety education. The insurance sector should introduce a system that discounts insurance premiums upon completion of training. Additionally, it is essential to prepare a systematic education program, including obstacle avoidance and simulation-based learning, by reflecting on the analysis results of road environments and driving data. In this study, insensitivity to safety, insufficient management systems, and lack of education and publicity were identified as causes of motorcycle driver crashes. It was confirmed that most types of dangerous driving were improved through traffic safety education. However, some limitations were observed, such as an increase in the right-hand rotation over time during sudden turns. Future research is needed to enhance laws, systems, and driver safety by analyzing driving characteristics in a broader context based on actual driving records and images.

Along with the increase in the number of vehicles in circulation, the indoor air quality in automobiles is attracting attention as another possible health concern. However compared to data regarding indoor air quality in other spaces, there are insufficient data on indoor air quality in automobiles. In addition, there is no standard for the evaluation method. In this study, the change in the concentration of particulate matter in the vehicle while driving under real road conditions was analyzed in order to use it as basic data for a method to evaluate vehicle indoor air quality. Through the selection of measurement target materials and test vehicles and the preparation of test methodologies, evaluation was performed on vehicle, route, and HVAC modes. The concentration of particulate matter in the vehicle was the lowest in the RC (In-vehicle recirculation) condition, and it was confirmed that it decreased with time. The highest average concentration was confirmed in the OA (Outside air ventilation) condition, and the concentration change according to the changing HVAC mode was observed in the Auto condition. The concentration of pollutants inside the vehicle showed a significant correlation with factors such as season, external concentration, and HVAC conditions, along with a weak correlation to powertrain type. The results of this study can be used as basic data for developing methods for evaluating vehicle interior air quality in future work.

본 연구는 자율주행상황에서 주관적인 운전 준비도를 객관적으로 측정할 수 있는 심리⋅생리적 지표를 확인하는 것을 목적으로 한다. 51명의 연구대상자가 참여하였고, 설문을 통해 운전 경험, 태도, 운전부하, 상황인식 등을 평가 하였다. 자율주행 중 차량 제어권을 인계받아야 하는 시나리오 동안 심전도를 측정하여 심박변이도 지표를 추출하였 고, 주행 종료 후 연구대상자는 자신의 상태를 평가하였다. 분석 결과, 운전 준비도는 정신적 부하와 부적 상관, 상황 인식과 상황 이해도와는 정적 상관을 보였다. 또한, 심박변이도 지표인 제곱 평균 근간 심박 간격 차이(Root Mean Square of Successive Differences, RMSSD)와 50ms 이상의 연속적인 RR 간격의 차이 비율(proportion derived NN50 by the total number of NN interval, pNN50)과의 유의한 정적 상관관계가 확인되었다. 운전 준비도 수준에 따라 상⋅중⋅하로 나누어 분석한 결과, 높은 운전 준비도 집단은 정신적 부하가 낮고 상황인식 및 상황에 대한 이해 도가 유의하게 높았으며, 자율주행 구간에서 pNN50이 높은 경향이 있었다. 마지막으로 상황인식과 RMSSD가 운전 준비도의 주요 예측 지표로 확인되었다. 이는 운전 준비도가 낮은 운전자는 자율신경 각성이 높고, 높은 운전자는 부교감신경계의 활성화로 인해 심리적, 생리적으로 안정된 상태임을 의미한다. 본 연구는 운전자의 주관적인 운전 준비도를 예측하기 위한 운전자의 심리 및 생리 지표를 확인하였고, 이는 운전자의 운전 준비 상태를 모니터링하는 기술에 적용되어 사고 예방에 기여할 수 있을 것이다.

This study aimed to develop a comprehensive validation methodology for an Infra-guidance system, which is an infrastructure-based service aimed at enhancing the safety of autonomous driving. The proposed method includes quantitative techniques for validating both the Infra-guidance algorithm module and the guidance message module using each optimal indicator. In addition, a promising method is suggested to validate the entire system by applying a multicriteria decision methodology. The relative weight for the algorithm module was higher than relative weight for the message module. Moreover, the relative weight of the latency for the message module was slightly higher than weight of the packet error rate. The proposed methodology is applicable for validating the performance of infrastructure-based services for enhancing connected autonomous driving based on the comprehensive quantification of various factors and indicators.

The purpose of this study was to identify and evaluate hazardous road sections based on roadside friction. Using GIS mapping and clustering techniques, this study analyzed traffic accidents and roadside friction data based on latitude and longitude coordinates. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was applied, with parameters of MinPts = 5 and eps = 0.0001, determined through a K-nearest neighbor analysis. The data were separated based on traffic flow direction (uphill/ downhill), and clustering was performed separately in each direction to identify specific hazard zones. The DBSCAN clustering results revealed 18 clusters in traffic accident data and 44 clusters in roadside friction data. Traffic accident clusters include various types of accidents (e.g., vehicle-to-vehicle and vehicle-to-pedestrian accidents), identifying locations as high-accident zones. The clustering results from the roadside friction data highlighted areas with crosswalks, absence of curbs, and roadside parking zones as major risk sections. Future research should analyze the operational design domain (ODD) of autonomous vehicles on hazardous road sections and explore the integration of multiple data sources to establish a comprehensive safety management system for accident prevention in autonomous driving environments. Additionally, road hazard sections are categorized into stages (e.g., hazardous, cautious, and safe) to enhance the precision in assessing road conditions. This categorization, combined with a detailed analysis of ODD, serves as a foundation for future research aimed at improving the safety of autonomous driving environments.

This study proposes a method to evaluate the publicity of real-time, demand-responsive, autonomous public-transportation systems. By analyzing real-time data collected based on publicity evaluation indicators suggested in previous research studies, this study seeks to establish a system that objectively assesses the publicity of public transportation. Thus, the introduction of autonomous public transportation systems is expected to contribute to solving problems in underserved transportation areas and enable more sophisticated public transportation operations. We reviewed evaluation indicators proposed in previous studies. Based on this review, publicity evaluation indicators were derived and specific criteria were selected to assess systematically the publicity of autonomous public transportation. An AHP analysis was conducted to assess the relative importance of each indicator by analyzing the importance of the selected indicators. Additionally, to score the indicators, minimum and maximum target values were established, and a method for assigning scores to each indicator was examined. The most important factor in the publicity evaluation of autonomous demand-responsive transport (DRT) was the “success rate of allocation to weak public transportation service areas,” with a significance level p of 0.204. This was analyzed as a key evaluation criterion because of the importance of service provision in areas with low-public-transportation accessibility. Subsequently, “Accessing distance to a virtual station” (p = 0.145) was evaluated as an important factor representing the convenience of the service. “Waiting time after allocation” (p = 0.134) also appeared as an important evaluation factor, as reducing waiting time considerably affected service quality. Conversely, “compliance rate of velocity” yielded the lowest significance (p = 0.017), as speed compliance was typically guaranteed owing to autonomous driving technology. This study proposed a specific evaluation method based on publicity indicators to provide a strategic direction for improving services and enhancing the publicity of autonomous DRT systems. These results can serve as a foundational resource for improving transportation services in underserved areas and for enhancing the overall quality of public transportation services. However, the study’s limitation was its inability to use real-time autonomous public transportation data, relying instead on I-MoD data from Incheon. This limitation constrained the ability to establish universal benchmarks because data from various municipalities were not included. Future research should collect and analyze data from diverse regions to establish more reliable evaluation indicators.

Onboard truck scales can accurately measure payload under static conditions. However, their performance is limited in accounting for dynamic environments encountered during driving, leading to inaccuracies in load estimation under real-world conditions. This study employs TruckCaliber, a dynamic state measurement system, to estimate real-time vehicle loads. Fusion sensor modules were installed on leaf spring suspensions and vehicle frames to collect tilt and IMU data. The system was implemented on a commercial truck, and driving tests were conducted with varying payloads. The analysis focused on curved sections under different dynamic conditions.

Driving Resistance is calculated for emission test defines total vehicle resistance forces. Resistance factors of running vehicle are sum of rolling resistance, transmission loss and aerodynamic drag force. To measure this resistance, Coastdown test is conventional method and it needs a long level driving road. In this study coastdown test is executed on short driving road. And also each resistance factors are calculated. This test is based on S(Distance)-Time Method. From the result, it is shown that this method is reliable and can be used for initial vehicle test.

PURPOSES : In this study, the importance of various goals, accomplishment, composition, and operation factors of autonomous driving living labs was identified, and implications for establishing strategies to expand the performance of autonomous driving living labs are presented based on their analyzed activation factors. METHODS : We set the factors for accomplishing autonomous living labs to promote technology development and commercialization, create an autonomous living ecosystem, secure the sustainability of living labs, resolve social issues related to urban transportation, and perform factor analyses. To identify the determining factors affecting performance, we performed a multiple regression analysis based on the scores of the composition and operation factors of autonomous living lab environments. RESULTS : Among the accomplishments of autonomous driving living labs, it was found that performance activation and physical environmental factors are important for the promotion of technology development and commercialization; performance activation and promotion and communication factors are important for sustainability related to ecosystem creation; and performance activation and physical environmental factors are important for sustainability related to operational experience acquisition. Additionally, operational factors related to the developer are important for the direct resolution of urban transportation problems, and promotion and communication and performance activation factors are important for the indirect resolution of urban transportation problems. CONCLUSIONS : The findings of this study clarify that activation factors differ depending on accomplishments or goals, providing basic data for establishing accomplishment-based strategies.

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.

PURPOSES : For autonomous vehicles, abnormal situations, such as sudden changes in driving speed and sudden stops, may occur when they leave the operational design domain. This may adversely affect the overall traffic flow by affecting not only autonomous vehicles but also the driving environment of manual vehicles. Therefore, to minimize the traffic problems and adverse effects that may occur in mixed traffic situations involving manual and autonomous vehicles, an autonomous vehicle driving support system based on traffic operation optimization is required. The main purpose of this study was to build a big-data-classification system by specifying data classification to support the self-driving of Lv.4 autonomous vehicles and matching it with spatio-temporal data. METHODS : The research methodology is explained through a review of related literature, and a traffic management index and big-dataclassification system were built. After collecting and mapping the ITS history traffic information data of an actual Living Lab city, the data were classified using the traffic management indexing method. An AI-based model was used to automatically classify traffic management indices for real-time driving support of Lv.4 autonomous vehicles. RESULTS : By evaluating the AI-based model performance using the test data from the Living Lab city, it was confirmed that the data indexing accuracy was more than 98% for the KNN, Random Forest, LightGBM, and CatBoost algorithms, but not for Logistics Regression. The data were severely unbalanced, and it was necessary to classify very low probability nonconformities; therefore, precision is also important. All four algorithms showed similarly good performances in terms of accuracy. CONCLUSIONS : This paper presents a method for efficient data classification by developing a traffic management index to easily fuse and analyze traffic data collected from various institutions and big data collected from autonomous vehicles. Additionally, EdgeRSU is presented to support the driving of Lv.4 autonomous vehicles in mixed autonomous and manual vehicles traffic situations. Finally, a database was established by classifying data automatically indexed through AI-based models to quickly collect and use data in real-time in large quantities.