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Automatic lane change extraction based on temporal patterns of symbolized driving behavioral data.

, , , , , and . Intelligent Vehicles Symposium, page 976-981. IEEE, (2015)

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Estimation of Driving Phase by Modeling Brake Pressure Signals., , , , , and . ICONIP (1), volume 5863 of Lecture Notes in Computer Science, page 468-475. Springer, (2009)Traffic interactions: Estimate driving behavior's influence., , and . Intelligent Vehicles Symposium, page 546-551. IEEE, (2011)A comparative study of time series modeling for driving behavior towards prediction., , , , , and . APSIPA, page 1-4. IEEE, (2013)Reducing the negative effect of defective data on driving behavior segmentation via a deep sparse autoencoder., , , , and . GCCE, page 1-5. IEEE, (2016)A Predictive Reward Function for Human-Like Driving Based on a Transition Model of Surrounding Environment., , , and . ICRA, page 7618-7624. IEEE, (2019)Visual Tracking Achieved by Adaptive Sampling from Hierarchical and Parallel Predictions., , and . ICONIP (1), volume 4984 of Lecture Notes in Computer Science, page 604-613. Springer, (2007)ReLaDec: Reliable Latency Decision Algorithm for Connected Vehicle Applications., , and . IV, page 1861-1868. IEEE, (2019)Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning., , , , and . IEEE Trans. Intell. Transp. Syst., 18 (9): 2477-2489 (2017)Defect-Repairable Latent Feature Extraction of Driving Behavior via a Deep Sparse Autoencoder., , , and . Sensors, 18 (2): 608 (2018)Latency Modeling for Mobile Edge Computing Using LTE Measurements., , and . VTC Fall, page 1-5. IEEE, (2018)