We used a deep sparse autoencoder to extract the low-dimensional high-level representation from high-dimensional raw driving behavioral data obtained from a�...

Abstract—Driving behavioral data is too high-dimensional for people to review their driving behavior. It includes ac- celerator opening rate, steering angle�...

This work used a deep sparse autoencoder to extract the low-dimensional high-level representation from high-dimensional raw driving behavioral data obtained�...

Sep 14, 2015 � We used a deep sparse autoencoder to extract the low-dimensional high-level representation from high-dimensional raw driving behavioral data�...

In this paper, the authors propose using deep sparse autoencoder (DSAE) to extract hidden features for visualization of driving behavior. Based on the DSAE�...

It is shown the driving color map based on DSAE facilitates better visualization of driving behavior, by mapping the extracted 3-D hidden feature to the red�...

In this paper, we propose using deep sparse autoencoder (DSAE) to extract hidden features for visualization of driving behavior. Based on the DSAE, we propose a�...

Feb 2, 2017 � In this paper, we propose using deep sparse autoencoder (DSAE) to extract hidden features for visualization of driving behavior. Based on the�...

Similarly, driving behavior was encoded in a 3-channel RGB space with a deep sparse autoencoder to visualize individual driving styles [241].…” Section�...

This study proposes a defect-repairable feature extraction method based on a deep sparse autoencoder (DSAE) to extract low-dimensional time-series data. In the�...