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Estimation of Driving Phase by Modeling Brake Pressure Signals.

, , , , , и . ICONIP (1), том 5863 из Lecture Notes in Computer Science, стр. 468-475. Springer, (2009)

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Takashi Seki

Takashi Iwata

Takashi Numaguchi

Takashi Tsuno

Takashi Ohtsuki

 

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