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 Advent calendars naturalistic driving corpus of 46 different drivers to learn and model the behavior divergence of Japanese drivers.First, ego-driver behavior signals are used to extract unique features