Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning

By Hailong Liu, Tadahiro Taniguchi, Yusuke Tanaka, Kazuhito Takenaka, Takashi Bando. 2017

125 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ekim Yurtsever
2. Alexander Carballo
3. Jacob Lambert
4. Kazuya Takeda

In Signal Processing

1. Kazuya Takeda
2. Alexander Carballo
3. Ekim Yurtsever
4. Jacob Lambert
5. Junichi Yamagishi
6. Tomoki Toda

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “Similarly, driving behavior was encoded in a 3-channel RGB space with a deep sparse autoencoder to visualize individual driving styles [241].”
• Driving Feature Extraction and Behavior Classification Using an Autoencoder to Reproduce the Velocity Styles of Experts (2018, unknown): “As an extension, the work in [20], treated driving recorded data as hidden timeseries statistically independent of each other which capture essential driving behavior.”