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Related Experiment Video

Updated: Aug 12, 2025

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
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Dual-flow network with attention for autonomous driving.

Lei Yang1,2, Weimin Lei1,3, Wei Zhang1

  • 1School of Computer Science and Engineering, Northeastern University, Shenyang, China.

Frontiers in Neurorobotics
|January 26, 2023
PubMed
Summary

We developed a dual-flow network for autonomous driving. This attention-based model achieved a 74% success rate in complex urban environments, particularly with multiple dynamic objects.

Keywords:
CARLA simulatorartificial intelligenceattentionautonomous drivingdeep neural networknetwork architecturevisual navigation

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Autonomous driving systems require robust perception and motion understanding.
  • Integrating visual and motion data is crucial for accurate navigation.
  • Attention mechanisms can enhance feature fusion in deep learning models.

Purpose of the Study:

  • To introduce a novel dual-flow network for autonomous driving.
  • To leverage an attention mechanism for improved waypoint prediction.
  • To evaluate the model's performance in complex urban driving scenarios.

Main Methods:

  • A dual-flow network architecture combining perception and motion streams.
  • Perception network processes Red, Green, Blue (RGB) images for feature extraction.
  • Motion network processes grayscale images for object motion feature extraction.
  • Attention mechanism fuses features from both networks at each layer for waypoint prediction.

Main Results:

  • The model achieved a 74% success rate in autonomous driving within the CARLA simulator.
  • High performance was observed even in scenarios with multiple dynamic objects.
  • The attention-based fusion effectively integrated visual and motion information.

Conclusions:

  • The proposed dual-flow network with attention mechanism is effective for autonomous driving.
  • The model demonstrates robustness in complex urban environments.
  • This approach shows promise for real-world autonomous navigation systems.