Discriminative Transfer Learning for Driving Pattern Recognition in Unlabeled Scenes
IEEE Transactions on Cybernetics(2022)
摘要
Driving pattern recognition based on features, such as GPS, gear, and speed information, is essential to develop intelligent transportation systems. However, it is usually expensive and labor intensive to collect a large amount of labeled driving data from real-world driving scenes. The lack of a labeled data problem in a driving scene substantially hinders the driving pattern recognition accuracy...
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关键词
Pattern recognition,Automotive engineering,Cybernetics,Learning systems,Data structures,Companies,Global Positioning System
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