Integrating driving behavior and traffic context through signal symbolization

By Suguru Yamazaki, Chiyomi Miyajima, Ekim Yurtsever, Kazuya Takeda, Masataka Mori, K. Hitomi, Masumi Egawa. 2016

7 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

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

In Artificial Intelligence

1. Ekim Yurtsever
2. Kazuya Takeda
3. Jacob Lambert
4. Alexander Carballo
5. John H. L. Hansen
6. Chiyomi Miyajima
7. Eijiro Takeuchi
8. Junichi Yamagishi

In Automotive Engineering

1. Ekim Yurtsever
2. Kazuya Takeda
3. Jacob Lambert
4. Alexander Carballo
5. Chiyomi Miyajima
6. Eijiro Takeuchi

In Computer Vision and Pattern Recognition

1. Ekim Yurtsever
2. Jacob Lambert
3. Alexander Carballo
4. Kazuya Takeda
5. John H. L. Hansen
6. Chiyomi Miyajima
7. Eijiro Takeuchi
8. Martin Magnusson

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “In [229]–[231] driving style was categorized into three and into two in [74], [209], [217], [218], [222].”
• Integrating Driving Behavior and Traffic Context Through Signal Symbolization for Data Reduction and Risky Lane Change Detection (2018, background): “Which is why we believe that the symbolization has to be carried independently [48].”
• Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer (2019, background): “on this dataset ignored the monocular camera footage and used ego-vehicle signals [16], [17].”
• A Traffic Flow Simulation Framework for Learning Driver Heterogeneity from Naturalistic Driving Data using Autoencoders (2019, background): “This progress led the development of machine-learning based models for traffic simulations (14)-(16) and driving behavior analysis (17)-(18) .”