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.

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.