Extracting Traffic Primitives Directly From Naturalistically Logged Data for Self-Driving Applications

Developing an automated vehicle, that can handle complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and understand driving environment, oftentimes, based on analyzing massive amounts of naturalistic driving data. An important pa...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 3; H. 2; S. 1223 - 1229
Hauptverfasser:	Wang, Wenshuo, Zhao, Ding
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.04.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Automation Bayes methods Bayesian analysis big data in robotics and automation Data mining Dirichlet problem Hidden Markov models Intelligent transportation systems Learning systems Machine learning Markov chains Mechanical engineering Roads Teaching methods Traffic flow Traffic information Vehicles
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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