Visualization of driving behavior using deep sparse autoencoder

@article{Liu2014VisualizationOD,
  title={Visualization of driving behavior using deep sparse autoencoder},
  author={Hailong Liu and Tadahiro Taniguchi and Toshiaki Takano and Yusuke Tanaka and Kazuhito Takenaka and Takashi Bando},
  journal={2014 IEEE Intelligent Vehicles Symposium Proceedings},
  year={2014},
  pages={1427-1434}
}
Driving behavioral data is too high-dimensional for people to review their driving behavior. It includes accelerator opening rate, steering angle, brake Master-Cylinder pressure and other various information. The high-dimensional data is not very intuitive for drivers to understand their driving behavior when they take a look back on their recorded driving behavior. We used a deep sparse autoencoder to extract the low-dimensional high-level representation from high-dimensional raw driving… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 12 references

Modeling and Recognition of Driving Behavior Based on Stochastic Switched ARX Model

IEEE Transactions on Intelligent Transportation Systems • 2007
View 3 Excerpts
Highly Influenced

Sparse autoencoder

A. Ng
CS294A Lecture notes, p. 72, 2011. • 2011
View 1 Excerpt

Learning deep architectures for ai

Y. Bengio
Foundations and trends R ⃝ in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009. • 2009
View 2 Excerpts

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