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Discriminative Transfer Learning for Driving Pattern Recognition in Unlabeled Scenes
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.
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A Pattern-Recognition Approach for Driving Skill Characterization
IEEE Transactions on Intelligent Transportation Systems, 2010Information about a driver's driving skill can be used to adapt vehicle control parameters to facilitate the specific driver's needs in terms of vehicle performance and safety. This paper presents an approach to driving skill characterization from a pattern-recognition perspective. The basic idea is to extract patterns that reflect the driver's driving
Yilu Zhang
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