Capturing Car-Following Behaviors by Deep Learning

By Xiao Wang, R. Jiang, Li Li, Yilun Lin, Xinhu Zheng, Fei-yue Wang. IEEE transactions on intelligent transportation systems (Print), 2018

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How researchers see this paper

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Survey on Deep Neural Networks in Speech and Vision Systems (2019, methodology): “However, recent advancements in IS are permeating almost every aspect of our life with the introduction of intelligent transportation [1-10], intelligent diagnosis and health monitoring for precision medicine [11-14], robotics and automation in home appliances [15], virtual online assistance [16], e”
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) .”
Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving (2019, methodology): “tructed NGSIM I-80 data [27] were utilized. Fig. 1. The layout of the road segment studied in dataset. Car-following events were extracted by applying a carfollowing ﬁlter as described in Wang et al. [28]. A carfollowing event was deﬁned as: The leading and following vehicle pairs stay in the same ”