논문 상세보기
pp.131-139
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.
1. 서론
2. 문헌 고찰
2.1. 국내·외 V2I 기술 동향
2.2. 자율주행차 교통안전시설 정보제공 연구
2.3. 본 연구의 차별성
3. 디지털 규제정보 제공방식 제안
3.1. IoT 기반 디지털 규제정보 제공
3.2. 행정 기준 개별 규제정보
3.3. 현실 적용 복합 규제정보
3.4. 현장 적용 디지털 복합 규제정보
4. 현장 검증
4.1. 평가 지표
4.2. 실험 장소
4.3. 실험 시나리오 설계
5. 실험 결과
5.1. 교차로 단위 결과 해석
5.2. 진행 상황 단위 결과 해석
5.3. 소결
6. 결론
REFERENCES
