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학술대회 High-Level Data Fusion Based Probabilistic Situation Assessment for Highly Automated Driving
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저자
노삼열, 안경환, 한우용
발행일
201509
출처
International Conference on Intelligent Transportation Systems (ITSC) 2015, pp.1587-1594
DOI
https://dx.doi.org/10.1109/ITSC.2015.259
협약과제
15MC2800, ICT기반 차량/운전자 협력자율주행 시스템(Co-Pilot)의 판단/제어 기술 개발, 한우용
초록
A primary challenge of automated driving systems is the task of a situation assessment. This paper presents a high-level data fusion based probabilistic situation assessment method which is capable of assessing a current traffic situation and giving a recommendation about driving behaviors. The proposed method consists of two steps: high-level data fusion and probabilistic situation assessment. The high-level data fusion, designed to provide a better understating of observed situations, produces a local dynamic road map by integrating all dynamic entities with a high-precision static road map. The probabilistic situation assessment estimates threat levels of each lane as the probability of the lane state through the use of independent local experts based on the local dynamic road map. The recommendations for behavior decision are determined by filtering out noises resulting from object tracking even though a tracking module misses objects or detects wrong objects a lot, but immediately. The method is implemented in an open-source robot operating system to provide a reusable and hardware independent software platform, and verified and evaluated through in-vehicle tests on real highways in real-time operation.
KSP 제안 키워드
Assessment method, Automated driving systems, High-level data fusion, In-vehicle, Open source, Road map, Robot operating system(ROS), Situation Assessment, driving behavior, high-precision, highly automated driving