20.500.12556/DKUM-87412 CNN-Based Vessel Meeting Knowledge Discovery From AIS Vessel Trajectories How to extract a collection of trajectories for different vessels from the raw AIS data to discover vessel meeting knowledge is a heavily studied focus. Here, the AIS database is created based on the raw AIS data after parsing, noise reduction and dynamic Ramer-Douglas-Peucker compression. Potential encountering trajectory pairs will be recorded based on the candidate meeting vessel searching algorithm. To ensure consistent features extracted from the trajectories in the same time period, time alignment is also adopted. With statistical analysis of vessel trajectories, sailing segment labels will be added to the input feature. All motion features and sailing segment labels are combined as input to one trajectory similarity matching method based on convolutional neural network to recognize crossing, overtaking or head-on situations for each potential encountering vessel pair, which may lead to collision if false actions are adopted. Experiments on AIS data show that our method is effective in classifying vessel encounter situations to provide decision support for collision avoidance. AIS Data CNN Dynamic Rammer-Douglas-Peucker knowledge discovery maneuvering pattern traffic pattern trajectory podatki odkrivanje znanja krivulje vzorec prometa manevrski vzorec true false true Idea Group Pub. Angleški jezik Slovenski jezik https://www.igi-global.com/gateway/journal/1072 Članek v reviji 2024-03-18 15:30:43 2024-03-19 10:18:58 2024-06-12 09:54:36 0000-00-00 00:00:00 2023 0 0 Str. 1-38 Št. 3 Letn. 34 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2023-01-01 004.9 151792899 10.4018/JDM.321636 1063-8016 Chen-2023-CNN-Based_Vessel_Meeting_Knowledge_D.pdf Chen-2023-CNN-Based_Vessel_Meeting_Knowledge_D.pdf 1 CFA7307A8D1298ADC97D397AC4C9F5C9 13dc12a4df3aec9c3287b984f3c758e26e5d3ecae25b4aa7e19ba79267cb735c 40091cb2-e537-11ee-9a93-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=176955 https://www.igi-global.com/gateway/article/321636 0 6a7ef848-e537-11ee-9a93-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=176956 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0