지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학위논문
- 저자정보
- 지도교수
- 김형원
- 발행연도
- 2019
- 저작권
- 충북대학교 논문은 저작권에 의해 보호받습니다.
이용수23
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초록· 키워드
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There has been a lot of research on autonomous driving lately. Various applications for self-driving cars carry out object recognition through cameras or various sensors in advance. It is also important to recognize the roadway in which the host vehicle drives during this prior recognition. This is because many applications of connected cars or self-driving cars are based on recognition of driving Road lanes. Methods for determining the driving road lane are as follows. Locate the yellow lane line after detecting the lane marker based on the characteristics of the lane marker. Locate the load lane of the host vehicle depending on the location of the yellow lane found. However, we have identified that it is difficult to find a driving lane on roads above three lanes, and that intersections without lane markers are also difficult to identify. This paper presents new ways to utilize V2X and Deep Learning technologies on roads above the three-lane. A Deep Learning model was designed to identify the driving lanes of a vehicle in question using the front image characteristics received from the vehicle within the communication range from the RSU installed on the side of the road. Through this, the predicted accuracy of 90% or more was verified in the three-lane road and intersection environment.
목차
Ⅰ. Introduction 1Ⅱ. Related work 32.1 V2X Communication application 32.2 Lane Detection algorithm 4Ⅲ. Driving road lane prediction technique in 3-lane roads or below 53.1 Image resize and cropping 63.2 Image filtering 73.2.1 Color filtering 73.2.2 Histogram Equalization 103.2.3 Top Hat filtering 133.3 Region Of Interest Setting 173.4 Lane Detection 193.4.1 Contour detection 203.4.2 Hough transform 213.4.3 Best Line detection 233.5 Center line detection and driving road lane prediction 27Ⅳ. Driving road lane prediction technique in multi-lane roads and at intersections 314.1 Technical Overview 314.2 CNN classification model 324.3 Road lane Predictive model using V2X and CNN 374.4 Semantic Segmentation network 384.5 Predictive model for multi-lane roads using Segnet 424.5.1 CNN-based Classification model configuration 434.5.2 Image Alignment Correction 45Ⅴ. Simulation result 485.1 Simulation condition 485.2 Verification methods and results in road environments below three lanes 485.3 Method and Results of Verification of Prediction Model Using CNN 515.3.1 Road environment with three or more Lane 515.3.2 Two lane intersection environment without lane markers 55Ⅵ. Conclusion 58References 61
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