UJ-FLAC: Unsupervised Joint Feature Learning and Clustering for Dynamic Driving Cycles Construction

Driving cycles construction, which aims to generate various vehicle driving profiles corresponding to typical traffic conditions, plays an important role in the evaluation of vehicle emissions, economy and mileage. Existing methods usually represent the speed-time distributions of driving data in th...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems 2022-08, Vol.23 (8), p.10970-10982
Hauptverfasser:	Pei, Lihong, Cao, Yang, Kang, Yu, Xu, Zhenyi, Zhao, Zhenyi
Format:	Artikel
Sprache:	eng
Schlagworte:	Brain modeling Clustering Driving Driving conditions Dynamic characteristics Dynamic driving cycles construction emission estimation Europe Feature extraction Iterative methods Light duty vehicles Machine learning Markov processes Regularization Roads Robustness Traffic unsupervised joint feature learning and clustering Vehicle dynamics Vehicle emissions visualization
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