Game-Theoretic Cooperative Lane Changing Using Data-Driven Models

Ding, Guohui; Aghli, Sina; Heckman, Christoffer; Chen, Lijun

doi:10.1109/iros.2018.8593725

Cited by 21 publications

(14 citation statements)

References 22 publications

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“…In autonomous driving, game theory has been applied to the problem of lane changing and merging [18]. Deep reinforcement learning was used to solve two player merging games in [17], which relied on defining drivers as proactive or passive. Roles for two players were also used in [32], which modelled lane changing as a Stackleberg game that has implicit leaders and followers.…”

Section: B Game Theory For Roboticsmentioning

confidence: 99%

“…Recently, game theory has attracted increasing attention in coordinating robots without communication [16]. Game theoretic methods have been developed to identify actions in the context of their effect on teammates and adversaries for two-player applications such as driving [17], [18], or racing [19], as well as larger games in known environments for surveillance [20]. However, they have not been previously utilized to address the problem of navigating a variable size multi-robot system through a complex environment to a goal position.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized and Communication-Free Multi-Robot Navigation through Distributed Games

Reily,

Mott,

Zhang

2020

Preprint

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Effective multi-robot teams require the ability to move to goals in complex environments in order to address real-world applications such as search and rescue. Multi-robot teams should be able to operate in a completely decentralized manner, with individual robot team members being capable of acting without explicit communication between neighbors. In this paper, we propose a novel game theoretic model that enables decentralized and communication-free navigation to a goal position. Robots estimate the behavior of their local teammates in order to identify behaviors that move them in the direction of the goal, while also avoiding obstacles and maintaining team cohesion without collisions. We prove theoretically that generated actions approach a Nash equilibrium, which also corresponds to an optimal strategy identified for each robot. We show through simulations that our approach enables decentralized and communication-free navigation by a multi-robot system to a goal position, avoiding obstacles and collisions, while also maintaining connectivity.

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Section: B Game Theory For Roboticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decentralized and Communication-Free Multi-Robot Navigation through Distributed Games

Reily,

Mott,

Zhang

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…However, there is a promising line of research with game-theoretic RL: policy updates can be based on stochastic game equilibria, with the goal of improving state-value function estimates and reducing learning instability. This has been demonstrated as an effective solution that incorporates agent-agent interaction in multi-agent RL [18]- [20], and these approaches are seeing increasing interest across different communities [21]- [23].…”

Section: Related Workmentioning

confidence: 99%

Cooperative Control of Mobile Robots with Stackelberg Learning

Koh,

Ding,

Heckman

et al. 2020

Preprint

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Multi-robot cooperation requires agents to make decisions that are consistent with the shared goal without disregarding action-specific preferences that might arise from asymmetry in capabilities and individual objectives. To accomplish this goal, we propose a method named SLiCC: Stackelberg Learning in Cooperative Control. SLiCC models the problem as a partially observable stochastic game composed of Stackelberg bimatrix games, and uses deep reinforcement learning to obtain the payoff matrices associated with these games. Appropriate cooperative actions are then selected with the derived Stackelberg equilibria. Using a bi-robot cooperative object transportation problem, we validate the performance of SLiCC against centralized multi-agent Q-learning and demonstrate that SLiCC achieves better combined utility.

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“…Aside from the above examples, the existing literature in game-theoretic motion planning and modeling for multiple competing agents typically are either for discrete state or action spaces [6]- [8], or are specifically for pursuit-evasion style games. In [9], a hierarchical reasoning game theory approach is used for interactive driver modeling; [10] predicts the motion of vehicles in a model-based, intention-aware framework using an extensive-form game formulation; [11], [12] considers Nash and Stackelberg equilibria in a two-car racing game where the action space of both players are finite and the game is formulated in bimatrix form.…”

Section: Related Workmentioning

confidence: 99%

Game Theoretic Planning for Self-Driving Cars in Competitive Scenarios

Wang

Gerdes

et al. 2019

Robotics: Science and Systems XV

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We propose a nonlinear receding horizon gametheoretic planner for autonomous cars in competitive scenarios with other cars. The online planner is specifically formulated for a two car autonomous racing game in which each car tries to advance along a given track as far as possible with respect to the other car. The algorithm extends previous work on gametheoretic planning for single integrator agents to be suitable for autonomous cars in the following ways: (i) by representing the trajectory as a piecewise-polynomial, (ii) incorporating bicycle kinematics into the trajectory, (iii) enforcing constraints on path curvature and acceleration. The game theoretic planner iteratively plans a trajectory for the ego vehicle, then the other vehicle until convergence. Crucially, the trajectory optimization includes a sensitivity term that allows the ego vehicle to reason about how much the other vehicle will yield to the ego vehicle to avoid collisions. The resulting trajectories for the ego vehicle exhibit rich game strategies such as blocking, faking, and opportunistic overtaking. The game-theoretic planner is shown to significantly out-perform a baseline planner using Model Predictive Control which does not take interaction into account. The performance is validated in high-fidelity numerical simulations, in experiments with two scale autonomous cars, and in experiments with a fullscale autonomous car racing against a simulated vehicle.

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Game-Theoretic Cooperative Lane Changing Using Data-Driven Models

Cited by 21 publications

References 22 publications

Decentralized and Communication-Free Multi-Robot Navigation through Distributed Games

Decentralized and Communication-Free Multi-Robot Navigation through Distributed Games

Cooperative Control of Mobile Robots with Stackelberg Learning

Game Theoretic Planning for Self-Driving Cars in Competitive Scenarios

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