A Traffic Flow Simulation Framework for Learning Driver Heterogeneity from Naturalistic Driving Data using Autoencoders

By Ekim Yurtsever, Chiyomi Miyajima, Kazuya Takeda. 2019

9 citations recorded by the source corpus.

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Subfields: Automotive Engineering, Building and Construction, Control and Systems Engineering

How researchers see this paper

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

In Automotive Engineering

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

In Building and Construction

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

In Control and Systems Engineering

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

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “drivers, and then macro driving style centroids were found with clustering [239].”
• Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer (2019, background): “Bottom-up unsupervised learning approaches were shown to be working for extracting individual driving styles [14], but the latent learned representations were not associated with risk due to the nature of unsupervised learning.”