Modeling oscillatory car following using deep reinforcement learning based car following models

John Nguyen, Raphael Stern

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this work, we use reinforcement learning (RL) to train a car following model for vehicle jerk. The learned model is specifically trained for car following in low-speed oscillatory driving conditions such as stop-and-go traffic typical in congested urban centers. This driving is of particular interest since it is difficult to model and substantially contributes to urban air pollution. The proposed model is calibrated using experimental data and the model performance is compared to a baseline calibrated intelligent driver model (IDM). The proposed RL model is able to outperform the IDM in some metrics, while the IDM has lower error in others. This indicates that the proposed RL model is able to capture the general car following behavior in low-speed oscillatory driving conditions without overfitting to the training data and represents a first step toward realistic car following models that capture the full range of driver behavior.

Original languageEnglish (US)
Title of host publication2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189956
DOIs
StatePublished - Jun 16 2021
Event7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021 - Heraklion, Greece
Duration: Jun 16 2021Jun 17 2021

Publication series

Name2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021

Conference

Conference7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021
Country/TerritoryGreece
CityHeraklion
Period6/16/216/17/21

Bibliographical note

Funding Information:
This material is based upon work supported by the Center for Transportation Studies at the University of Minnesota under the Transportation Scholars Program.

Funding Information:
This work is supported by the University of Minnesota Center for Transportation Studies through the Transportation Scholar’s Program.

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Car following
  • Reinforcement learning
  • Traffic modeling

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