A Game-Theoretic Approach on Conflict Resolution of Autonomous Vehicles at Unsignalized Intersections

Lu, Xinghao; Hai-yan, Zhao; Li, Cheng; Gao, Bingzhao; Chen, Hong

doi:10.1109/tits.2023.3285597

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…Additionally, they also [32] proposed a trajectory planning model based on fuzzy coalitional game theory to address the social and individual benefits between vehicles. Similar research endeavors can be found in the literature [33][34][35].…”

Section: Introductionmentioning

confidence: 66%

Game Scheduling for Vehicle Platoons at Autonomous Intersection Accommodating Priority Passage Requests

Hu,

Peng

2024

IEEE Access

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Addressing the human-centric needs in vehicle scheduling at intersections poses a significant challenge. In this paper, we propose a game scheduling method to address the problem of platoon scheduling while considering priority passage requests. Firstly, we construct a coalition game-based platoon scheduling model. Within this model, a kernel function searches for the optimal coalition group within the kernel set to ensure that participants, such as platoons, gain higher payoffs by joining the coalition than they would individually. The coalition payoff function determines the optimal passage order. Secondly, we design a baseline strategy where the payoff and action obtained by the priority passage platoon in the coalition game serve as the baseline values. Finally, based on this strategy, we develop a bargaining game model where the priority passage platoon acts as the bargainer, while other platoons act as the other party, with baseline payoff and action serving as constraints. The Nash equilibrium identifies the optimal passage order for the priority passage platoon. We conduct 3 experiments: experiment 1 evaluates the effectiveness of the coalition game model; experiment 2 tests the effectiveness of the bargaining game model; and experiment 3 assesses the effectiveness of the game scheduling method in reducing vehicle travel costs and compares it with existing methods. The results demonstrate that our proposed game scheduling method not only reduces the travel costs of the priority passage platoon but also minimizes its impact on other vehicles to the greatest extent possible.INDEX TERMS Autonomous intersection, baseline payoff, game scheduling, priority passage request, vehicle platoon.

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Section: Introductionmentioning

confidence: 66%

Game Scheduling for Vehicle Platoons at Autonomous Intersection Accommodating Priority Passage Requests

Hu,

Peng

2024

IEEE Access

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Physics-Informed Particle-Based Reinforcement Learning for Autonomy in Signalized Intersections

Emamifar,

Ghoreishi

2024

Int. J. ITS Res.

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In this paper, we develop a framework to enhance the control of autonomous vehicles within signalized intersections by integrating system dynamics with imperfect sensor data. Although sensor data for autonomous vehicles is often partial, noisy, or missing, its alignment with underlying physics has the potential to significantly improve prediction accuracy. Our methodology involves a physics-based representation of system dynamics, accounting for stochastic elements originating from differences between real-world scenarios and model-based representations, using a Markov decision process (MDP) model that embraces system physics while accommodating uncertainties. Leveraging partial sensor data, we aim to diminish uncertainty associated with segments of the system model reflected in the data and better estimate the other physical variables that are not measurable through data. This framework develops particle-based reinforcement learning action policies, seamlessly integrating data and physics for controlling autonomous vehicles approaching signalized intersections. Numerical experiments showcase the effectiveness of these policies in ensuring safe control and decision-making in different scenarios of missing state variables and varying levels of data sparsity.

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Interactive Behavior Modeling for Vulnerable Road Users With Risk-Taking Styles in Urban Scenarios: A Heterogeneous Graph Learning Approach

Li,

Gong,

Zhang

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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A Game-Theoretic Approach on Conflict Resolution of Autonomous Vehicles at Unsignalized Intersections

Cited by 4 publications

References 35 publications

Game Scheduling for Vehicle Platoons at Autonomous Intersection Accommodating Priority Passage Requests

Game Scheduling for Vehicle Platoons at Autonomous Intersection Accommodating Priority Passage Requests

Physics-Informed Particle-Based Reinforcement Learning for Autonomy in Signalized Intersections

Interactive Behavior Modeling for Vulnerable Road Users With Risk-Taking Styles in Urban Scenarios: A Heterogeneous Graph Learning Approach

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