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학술대회 For Safer Navigation: Pedestrian-View Intersection Classification
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마셀라, 이진하, 무함마드, 이재영, 이승익
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.7-10
20HS2600, 불확실한 지도 기반 실내ㆍ외 환경에서 최종 목적지까지 이동로봇을 가이드할 수 있는 AI 기술 개발, 이재영
Intersection classification is one of the key components of autonomous navigation. Several related research works have been conducted as a prior task to solve problems such as autonomous driving, aircraft hovering, and navigating in mines. However, to the best of our knowledge, none of these studies support pedestrian-view navigation to guide the small and slow robots for which it is too dangerous to be operated on normal roads along with normal vehicles. To address this problem, we propose: 1) a pedestrian-view-level intersection classification image dataset, 2) ResNet-based architecture fine-tuned on the proposed dataset, and 3) thorough experimentation to explore the capabilities of our proposed architecture. The detailed analysis reported in this paper enabled us to find the network configuration that is neither underfit nor overfit to our data and achieves 80% test accuracy.
KSP 제안 키워드
Image datasets, Key Components, Network configuration, autonomous driving, autonomous navigation