Browsing by Author "Kolmanovsky, İ."

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    Adaptive game-theoretic decision making for autonomous vehicle control at roundabouts
    (Institute of Electrical and Electronics Engineers Inc., 2019) Tian, R.; Li, S.; Li, N.; Kolmanovsky, İ.; Girard, A.; Yıldız, Yıldıray; Teel, A. R.; Egerstedt, M.
    In this paper, we propose a decision making algorithm for autonomous vehicle control at a roundabout intersection. The algorithm is based on a game-theoretic model representing the interactions between the ego vehicle and an opponent vehicle, and adapts to an online estimated driver type of the opponent vehicle. Simulation results are reported.
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    Fault tolerant control for over-actuated systems: an adaptive correction approach
    (IEEE, 2016) Tohidi, Seyed Shahabaldin; Yıldız, Yıldıray; Kolmanovsky, İ.
    This paper proposes an adaptive fault tolerant control allocation approach for over-actuated systems. The methodology does not utilize the control input matrix estimation to tolerate actuator faults and, therefore, the proposed control allocation method does not require persistence of excitation. Adaptive control approach with a closed loop reference model is used for identifying control allocation parameters, which provides improved performance without introducing undesired oscillations. Furthermore, a sliding mode controller is used to guarantee the outer loop asymptotic stability. Simulation results are provided, where the ADMIRE model is used as an over-actuated system, to demonstrate the effectiveness of the proposed method.
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    Hierarchical reasoning game theory based approach for evaluation and testing of autonomous vehicle control systems
    (IEEE, 2016) Li, N.; Oyler, D.; Zhang, M.; Yıldız, Yıldıray; Girard, A.; Kolmanovsky, İ.
    A hierarchical game theoretic decision making framework is exploited to model driver decisions and interactions in traffic. In this paper, we apply this framework to develop a simulator to evaluate various existing autonomous driving algorithms. Specifically, two algorithms, based on Stackelberg policies and decision trees, are quantitatively compared in a traffic scenario where all the human-driven vehicles are modeled using the presented game theoretic approach.