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악천후 환경에 강건한 혼합밀도네트워크 기반 객체 탐지 딥러닝 모델
조택형(Taekhyung Cho),최종은(Jongeun Choi) 한국자동차공학회 2023 한국자동차공학회 부문종합 학술대회 Vol.2023 No.5
With the growth of deep learning technology, there are many elaborate object detection models being developed for safe autonomous driving. However, a common problem is that the training data is often biased toward normal daytime which leads to high uncertainty in the predictions on adverse weather conditions that were not included in the training data. Therefore, in this paper, we developed a robust model for bad weather conditions by utilizing mixture density network to estimate the uncertainty of the deep learning model’s predictions. Our method showed better performance than original models in fog, rain, and nighttime environments.
가우시안 프로세스를 이용한 실내 환경에서 소형무인기에 적합한 SLAM 시스템 개발
전영산(Young-San Jeon),최종은(Jongeun Choi),이정욱(Jeong Oog Lee) 제어로봇시스템학회 2014 제어·로봇·시스템학회 논문지 Vol.20 No.11
Localization of aerial vehicles and map building of flight environments are key technologies for the autonomous flight of small UAVs. In outdoor environments, an unmanned aircraft can easily use a GPS (Global Positioning System) for its localization with acceptable accuracy. However, as the GPS is not available for use in indoor environments, the development of a SLAM (Simultaneous Localization and Mapping) system that is suitable for small UAVs is therefore needed. In this paper, we suggest a vision-based SLAM system that uses vision sensors and an AHRS (Attitude Heading Reference System) sensor. Feature points in images captured from the vision sensor are obtained by using GPU (Graphics Process Unit) based SIFT (Scale-invariant Feature Transform) algorithm. Those feature points are then combined with attitude information obtained from the AHRS to estimate the position of the small UAV. Based on the location information and color distribution, a Gaussian process model is generated, which could be a map. The experimental results show that the position of a small unmanned aircraft is estimated properly and the map of the environment is constructed by using the proposed method. Finally, the reliability of the proposed method is verified by comparing the difference between the estimated values and the actual values.
박제현(Jehyun Park),손호준(Hojoon Son),최종은(Jongeun Choi) 대한기계학회 2017 대한기계학회 춘추학술대회 Vol.2017 No.11
This paper proposes an advanced driver assistance system (ADAS) that provides drivers with useful information using a long short-term memory (LSTM) network. The ADAS predicts road events in order to improve driving performance using range finder sensors. We show the effectiveness of the proposed system through the experiment using the open racing car simulator (TORCS).