Literature Database Entry


Max Schettler, Dominik S. Buse, Anatolij Zubow and Falko Dressler, "How to Train your ITS? Integrating Machine Learning with Vehicular Network Simulation," Proceedings of 12th IEEE Vehicular Networking Conference (VNC 2020), Virtual Conference, December 2020.


Machine Learning (ML) is becoming ever more popular in many application domains, including vehicular networking. It has been shown already that Intelligent Transportation Systems (ITS) can greatly benefit from this approach, particularly from Reinforcement Learning (RL). To implement Vehicular Ad- hoc Network (VANET) environments for RL training, researchers often start from scratch. Because up until now, there is neither an established interface to ML toolkits nor a common scenario for VANET applications. Though such established standards would be a great benefit to research: Previous results would be easier to reproduce and different solutions could be compared in equal situations and using the same metrics. We developed Veins-Gym to bridge this gap. Veins-Gym combines the popular Veins vehicular networking simulator with OpenAI Gym. Using an exemplary VANET application, we show that RL techniques can be easily applied to ITSs with this framework. This enabled us to train an agent that outperformed hand-written algorithms.

Quick access

Original Version DOI (at publishers web site)
Authors' Version PDF (PDF on this web site)
BibTeX BibTeX


Max Schettler
Dominik S. Buse
Anatolij Zubow
Falko Dressler

BibTeX reference

    author = {Schettler, Max and Buse, Dominik S. and Zubow, Anatolij and Dressler, Falko},
    doi = {10.1109/VNC51378.2020.9318324},
    title = {{How to Train your ITS? Integrating Machine Learning with Vehicular Network Simulation}},
    publisher = {IEEE},
    address = {Virtual Conference},
    booktitle = {12th IEEE Vehicular Networking Conference (VNC 2020)},
    month = {12},
    year = {2020},

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at

This page was automatically generated using BibDB and bib2web.

Last modified: 2024-07-21