Paper Info
Title
Reducing the negative effect of defective data on driving behavior segmentation via a deep sparse autoencoder
Abstract
Analyzing driving behavior data is essential for developing driver assistance systems. Statistical segmentation is one of the important methods to realize the analysis. Driving behavior data actually include undesirable defects caused by external environment and sensor failures. Defects in the data cause a huge negative effect on the segmentation. In this paper, we showed that a feature extraction method based on a deep sparse autoencoder with fixed point (DSAE-FP) could reduce the negative effect of defective data in a driving behavior segmentation task. In the experiments, we used sticky hierarchical Dirichlet process hidden Markov model to segment the driving behavior. We compared the segmentation results using hidden features extracted by DSAE-FP and other comparative methods. Experimental results showed that segmentation results of non-defective dataset and defective dataset turned out most similar when DSAE-FP was used.
Year
DOI
Venue
2016
10.1109/GCCE.2016.7800355
2016 IEEE 5th Global Conference on Consumer Electronics
Keywords
Field
DocType
behavior segmentation,deep sparse autoencoder,driving behavior data,driver assistance systems,statistical segmentation,feature extraction method,deep sparse autoencoder with fixed point,DSAE-FP,driving behavior segmentation task,sticky hierarchical Dirichlet process hidden Markov model
Data mining,Hierarchical Dirichlet process,Autoencoder,Pattern recognition,Segmentation,Computer science,Advanced driver assistance systems,Feature extraction,Artificial intelligence,Hidden Markov model,Principal component analysis,Maintenance engineering
Conference
ISBN
Citations 
PageRank 
978-1-5090-2334-9
0
0.34
References 
Authors
3
5
Name
Order
Citations
PageRank
Hailong Liu1397.57
Tadahiro Taniguchi220133.56
Kazuhito Takenaka3737.41
Yusuke Tanaka400.34
Takashi Bando512314.55