A Traffic Flow Simulation Framework for Learning Driver Heterogeneity from Naturalistic Driving Data using Autoencoders
This paper proposes a novel data-centric framework for microscopic traffic flow simulation with intra and inter driver heterogeneity. We utilized a naturalistic driving corpus of 46 different drivers to learn and model the behavior divergence of Japanese drivers. First, ego-driver behavior signals a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Society of Automotive Engineers of Japan, Inc.
2019-01-01
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| Series: | International Journal of Automotive Engineering |
| Online Access: | https://www.jstage.jst.go.jp/article/jsaeijae/10/1/10_20194087/_article/-char/ja |