Lane Departure Warning Mechanism of Limited False Alarm Rate Using Extreme Learning Residual Network and ϵ-Greedy LSTM

Neglecting the driver behavioral model in lane-departure-warning systems has taken over as the primary reason for false warnings in human–machine interfaces. We propose a machine learning-based mechanism to identify drivers’ unintended lane-departure behaviors, and simultaneously predict the possibi...

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Detalles Bibliográficos
Autores principales: Gao, Qiaoming, Yin, Huijun, Zhang, Weiwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038345/
https://www.ncbi.nlm.nih.gov/pubmed/31979330
http://dx.doi.org/10.3390/s20030644

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038345/
https://www.ncbi.nlm.nih.gov/pubmed/31979330
http://dx.doi.org/10.3390/s20030644

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