Machine Learning for Cooperative Driving in a Multi-Lane Highway Environment

Aashik Chandramohan, Mannes Poel, Bernd Meijerink, Geert Heijenk

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    8 Citations (Scopus)
    2 Downloads (Pure)


    Most of the research in automated driving currently involves using the on-board sensors on the vehicle to collect information regarding surrounding vehicles to maneuver around them. In this paper we discuss how information communicated through vehicular networking can be used for controlling an autonomous vehicle in a multi-lane highway environment. A driving algorithm is designed using deep Q learning, a type of reinforcement learning. In order to train and test driving algorithms, we deploy a simulated traffic system, using SUMO (Simulation of Urban Mobility). The performance of the driving algorithm is tested for perfect knowledge regarding surrounding vehicles. Furthermore, the impact of limited communication range and random packet loss is investigated. Currently the performance of the driving algorithm is far from ideal with the collision ratios being quite high. We propose directions for additional research to improve the performance of the algorithm.

    Original languageEnglish
    Title of host publication2019 Wireless Days, WD 2019
    ISBN (Electronic)9781728101170
    Publication statusPublished - 13 Jun 2019
    Event11th Wireless Days Conference, WD 2019 - Manchester Metropolitan University Business School, Manchester, United Kingdom
    Duration: 24 Apr 201926 Apr 2019
    Conference number: 11


    Conference11th Wireless Days Conference, WD 2019
    Abbreviated titleWD 2019
    Country/TerritoryUnited Kingdom
    Internet address


    • Autonomous driving
    • Cooperative driving
    • Highway environment
    • Q learning
    • Reinforcement Learning
    • SUMO


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