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.

ABSTRACT
1. 서론
    1.1. 연구 배경 및 목적
    1.2. 연구 범위
    1.3. 연구 방법
2. 선행연구 고찰
    2.1. 데이터 분류 관련 연구
    2.2. 기존 연구와의 차별성
3. AI 기반의 빅데이터 분류체계 구축
    3.1. 자율주행 지원을 위한 빅데이터 수집
    3.2. 시공간 데이터 맵 매칭
    3.3. 교통관리 인덱스 개발
    3.4. 교통관리 인덱스 AI 모델링
4. 교통관리 인덱싱 테스트
    4.1. 데이터 수집
    4.2. AI 기반의 교통관리 인덱싱
    4.3. 교통관리 인덱싱 결과
5. 결론
감사의 글
REFERENCES

• 하혜종(명지대학교 재난대응교통관리센터 연구교수) | Ha HyeJong
• 손영태(명지대학교 교통공학과 교수) | Son Youngtae Corresponding author
• 정현숙(명지대학교 교통공학과 석박사통합과정) | Jeong Hyeonsuk