Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts

Tian, Ran; Li, Sisi; Li, Nan; Kolmanovsky, Ilya; Girard, Anouck; Yıldız, Yıldıray

doi:10.1109/cdc.2018.8619275

Cited by 86 publications

(55 citation statements)

References 13 publications

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“…Preliminary results of this paper have been reported in the conference papers [38] and [39]. The results modeling the interactions between two vehicles at a four-way intersection are reported in [38] and those for two vehicles at a roundabout intersection are in [39]. This paper generalizes the methodology to modeling the interactions among multiple (more than two) vehicles and to an additional intersection type -T-shaped intersection.…”

Section: Introductionmentioning

confidence: 89%

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Game-Theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation

Tian

Kolmanovsky

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles. Their planning and control systems need extensive testing, including early-stage testing in simulations where the interactions among autonomous/human-driven vehicles are represented. Motivated by the need for such simulation tools, we propose a game-theoretic approach to modeling vehicle interactions, in particular, for urban traffic environments with unsignalized intersections. We develop traffic models with heterogeneous (in terms of their driving styles) and interactive vehicles based on our proposed approach, and use them for virtual testing, evaluation, and calibration of AV control systems. For illustration, we consider two AV control approaches, analyze their characteristics and performance based on the simulation results with our developed traffic models, and optimize the parameters of one of them.

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Section: Introductionmentioning

confidence: 89%

“…Constructing larger road systems based on the models of these three intersections is reported for the first time in this paper. This paper also demonstrates how the developed traffic models can be used for virtual testing, evaluation, and calibration of AV control systems, which is not provided in [38] and [39].…”

Section: Introductionmentioning

confidence: 99%

Game-Theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation

Tian

Kolmanovsky

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…There are other works in autonomous driving literature which also use game theoretic strategies, and like our work, they adopt some of these approximations. However, compared to our work, these approaches either do not allow for the stochasticity of the other agents [22], or only model intentions and do not have interactive behaviors [23].…”

Section: Davidmentioning

confidence: 96%

Interactive Decision Making for Autonomous Vehicles in Dense Traffic

Isele

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Dense urban traffic environments can produce situations where accurate prediction and dynamic models are insufficient for successful autonomous vehicle motion planning. We investigate how an autonomous agent can safely negotiate with other traffic participants, enabling the agent to handle potential deadlocks. Specifically we consider merges where the gap between cars is smaller than the size of the ego vehicle. We propose a game theoretic framework capable of generating and responding to interactive behaviors. Our main contribution is to show how game-tree decision making can be executed by an autonomous vehicle, including approximations and reasoning that make the tree-search computationally tractable. Additionally, to test our model we develop a stochastic rule-based traffic agent capable of generating interactive behaviors that can be used as a benchmark for simulating traffic participants in a crowded merge setting.

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“…On the basis of this observation, algorithms that estimate the other agents' cognitive levels according to their historical behavior have been proposed in [15], [16], [19] so that the ego agent can adapt its decision strategy to the level estimates.…”

Section: B the Cognitive Hierarchy Frameworkmentioning

confidence: 99%

Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control

Girard

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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In this paper, we describe an integrated framework for autonomous decision making in a dynamic and interactive environment. We model the interactions between the ego agent and its operating environment as a two-player dynamic game, and integrate cognitive behavioral models, Bayesian inference, and receding-horizon optimal control to define a dynamically-evolving decision strategy for the ego agent. Simulation examples representing autonomous vehicle control in three traffic scenarios where the autonomous ego vehicle interacts with a human-driven vehicle are reported.

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Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts

Cited by 86 publications

References 13 publications

Game-Theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation

Game-Theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation

Interactive Decision Making for Autonomous Vehicles in Dense Traffic

Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control

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