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Vehicle Trajectory Prediction Using Hierarchical Graph Neural Network for Considering Interaction among Multimodal

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  • 1Global ADAS BU, Mando Corporation, Seongnam 13486, Korea.

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Summary

This study introduces a hierarchical graph neural network to predict autonomous vehicle trajectories by modeling complex interactions between multiple vehicle maneuvers. The new method improves prediction accuracy in highly interactive driving scenarios.

Keywords:
autonomous vehicledeep learning-based trajectory predictiongraph neural networkhierarchical structureinteraction-aware trajectory predictionmultimodal maneuver

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Area of Science:

  • Autonomous Systems
  • Artificial Intelligence
  • Robotics

Background:

  • Predicting surrounding vehicle trajectories is crucial for autonomous vehicle safety and functionality.
  • Existing methods struggle to account for unobservable maneuvers and numerous maneuver combinations, limiting interaction modeling.

Purpose of the Study:

  • To propose a novel hierarchical graph neural network (HGNN) for predicting vehicle trajectories.
  • To effectively model and consider interactions among multiple, potentially unobservable, vehicle maneuvers.

Main Methods:

  • Developed a hierarchical graph neural network architecture.
  • Represented relationships among vehicle maneuvers in a graph structure to capture interactions.
  • Evaluated the model using both public and real-world driving datasets.

Main Results:

  • The proposed HGNN demonstrated superior prediction performance compared to previous methods.
  • The model showed significant improvements, particularly in highly interactive driving situations.
  • Accurate prediction of multiple maneuvers and their interactions was achieved.

Conclusions:

  • The hierarchical graph neural network effectively addresses the limitations of prior trajectory prediction methods.
  • The approach enhances the ability of autonomous vehicles to navigate complex, interactive environments.
  • This work contributes to safer and more reliable autonomous driving systems.