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Enhancing collaborative perception through multi-scale contextual information integration.

Mingyue Ma1, Dongyang Hong2, Hui Zhang2

  • 1Research Institute for Road Safety of the Ministry of Public Security, China.

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Summary
This summary is machine-generated.

This study introduces a multi-scale contextual information integration (MSCI) framework to improve autonomous driving perception. The method enhances detection accuracy and communication efficiency in dynamic environments.

Keywords:
Autonomous vehiclesCollaborative perceptionMulti-scale attention

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Autonomous driving perception systems struggle with dynamic environments (occlusions, lighting changes, unpredictable traffic).
  • Challenges include capturing fine local details and broad context while maintaining real-time efficiency and low communication costs.

Purpose of the Study:

  • To propose a novel framework for enhanced collaborative perception in autonomous driving.
  • To address the limitations of current perception systems in dynamic and challenging conditions.

Main Methods:

  • Developed a multi-scale contextual information integration (MSCI) framework.
  • Employed multi-scale adaptive attention augmentation for feature relevance across spatial scales.
  • Integrated a context-aware perception enhancement module combining local and global information.
  • Utilized a GRU-based dynamic booster with a motion-aware mechanism for temporal modeling.

Main Results:

  • Achieved notable improvements in detection accuracy.
  • Demonstrated significant enhancements in communication efficiency.
  • Perception robustness and stability were improved in challenging environments.

Conclusions:

  • The proposed MSCI framework effectively enhances collaborative perception in autonomous driving.
  • The method successfully balances perception accuracy with real-time operational demands.
  • Future work may involve further optimization for complex real-world scenarios.