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Julian Schwarzat, "Platooning of Autonomous Cars as a Multi-Armed Bandit Problem," Master's Thesis, Institute of Computer Engineering (ITI), Universität Lübeck, May 2020. (Advisors: Heiko Hamann and Julian Heinovski; Referees: Heiko Hamann and Falko Dressler)


Autonomous vehicles open up new opportunities. One of them is the idea of pla- tooning, where multiple autonomous cars drive in close proximity in order to form a road-train. Platooning promises several benefits. There is a significant increase in safety. Slipstream effects can save up to 20 % of fuel consumption [9]. Our main objective is to increase the total efficiency across all cars by allowing a dynamic reconfiguration of platoons at runtime. We use methods of machine learning that were developed to solve the problem of multi-armed bandits. It is the problem of choosing from uncertain options while maximizing expected gain. We implement a simulation based on the open source software PLEXE / SUMO [27]. We implement five algorithms, ε-greedy, UCB 1, Bayes UCB, Thompson-Sampling and the appro- ach of Heinovski and Dressler [14], and compare them in different scenarios. We focus on two highway scenarios, the decision time of each algorithm, an estimated minimal profit for platoon switches and a initially speed up for faster platooning at start. In dynamic scenarios, the algorithms do not significantly differ. Choosing a conservative scenario leads to fewer changes and significantly increases the results of ε-greedy, UCB 1, Thompson-Sampling algorithms. The success of platoon for- mation is highly dependent on the chosen parameters mentioned above. A variety of parameter researches remain open. The parameter decision threshold has a great influence how fast vehicles form platoons. Further work may focus on a dynamic threshold in dependence of the current vehicle density or on acknowledgment-based inter-vehicle communication.

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Julian Schwarzat

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    author = {Schwarzat, Julian},
    title = {{Platooning of Autonomous Cars as a Multi-Armed Bandit Problem}},
    advisor = {Hamann, Heiko and Heinovski, Julian},
    institution = {Institute of Computer Engineering (ITI)},
    location = {L{\"{u}}beck, Germany},
    month = {5},
    referee = {Hamann, Heiko and Dressler, Falko},
    school = {Universit{\"{a}}t L{\"{u}}beck},
    type = {Master's Thesis},
    year = {2020},

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