Multi-Vehicles Decision-Making in Interactive Highway Exit: A Graph Reinforcement Learning Approach

Gao, Xin; Luan, Tian; Li, Xueyuan; Liu, Qi; Li, Zirui; Yang, Fan

doi:10.1109/iciea54703.2022.10005925

Cited by 1 publication

(1 citation statement)

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“…Graph neural networks (GNNs) excel in handling complex traffic scenarios and have been integrated into the decision-making processes of intelligent agents [ 14 ]. In a recent study by [ 15 ], GNNs combined with Double Deep Q-learning networks demonstrated effective multi-vehicle decision making in dynamic scenarios. Moreover, GNNs are applied to unmanned aerial vehicle (UAV) decision making [ 16 ], enabling UAVs to autonomously learn visual motion control strategies for precise landings in identified areas.…”

Section: Related Workmentioning

confidence: 99%

Research on Lane-Changing Decision Making and Planning of Autonomous Vehicles Based on GCN and Multi-Segment Polynomial Curve Optimization

Feng,

Wei,

Zhao

et al. 2024

Sensors

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This paper considers the interactive effects between the ego vehicle and other vehicles in a dynamic driving environment and proposes an autonomous vehicle lane-changing behavior decision-making and trajectory planning method based on graph convolutional networks (GCNs) and multi-segment polynomial curve optimization. Firstly, hierarchical modeling is applied to the dynamic driving environment, aggregating the dynamic interaction information of driving scenes in the form of graph-structured data. Graph convolutional neural networks are employed to process interaction information and generate ego vehicle’s driving behavior decision commands. Subsequently, collision-free drivable areas are constructed based on the dynamic driving scene information. An optimization-based multi-segment polynomial curve trajectory planning method is employed to solve the optimization model, obtaining collision-free motion trajectories satisfying dynamic constraints and efficiently completing the lane-changing behavior of the vehicle. Finally, simulation and on-road vehicle experiments are conducted for the proposed method. The experimental results demonstrate that the proposed method outperforms traditional decision-making and planning methods, exhibiting good robustness, real-time performance, and strong scenario generalization capabilities.

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Section: Related Workmentioning

confidence: 99%