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Unsupervised Hierarchical Modeling of Driving Behavior and Prediction of Contextual Changing Points.

, , , , and . IEEE Trans. Intell. Transp. Syst., 16 (4): 1746-1760 (2015)

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Online Multibody Factorization Based on Bayesian Principal Component Analysis of Gaussian Mixture Models., , , , and . ICONIP (1), volume 5506 of Lecture Notes in Computer Science, page 679-687. Springer, (2008)Driving word2vec: Distributed semantic vector representation for symbolized naturalistic driving data., , , , , and . Intelligent Vehicles Symposium, page 1313-1320. IEEE, (2016)Visualization of driving behavior using deep sparse autoencoder., , , , , and . Intelligent Vehicles Symposium, page 1427-1434. IEEE, (2014)Unsupervised drive topic finding from driving behavioral data., , , and . Intelligent Vehicles Symposium, page 177-182. IEEE, (2013)Automatic generation of summarized driving video with music and captions., , and . IECON, page 2409-2414. IEEE, (2015)Drive video summarization based on double articulation structure of driving behavior., , , and . ACM Multimedia, page 1169-1172. ACM, (2012)Evaluating Mental State of Drivers in Automated Driving Using Heart Rate Variability towards Feasible Request-to-Intervene., , , , , , , , , and 1 other author(s). SMC, page 3454-3459. IEEE, (2020)Reducing the negative effect of defective data on driving behavior segmentation via a deep sparse autoencoder., , , , and . GCCE, page 1-5. IEEE, (2016)Latency Modeling for Mobile Edge Computing Using LTE Measurements., , and . VTC Fall, page 1-5. IEEE, (2018)Defect-Repairable Latent Feature Extraction of Driving Behavior via a Deep Sparse Autoencoder., , , and . Sensors, 18 (2): 608 (2018)