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Lane Position Detection Based on Long Short-Term Memory (LSTM).

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Summary

This study enhances lane line detection for safer driving by combining YOLO v3 (You Only Look Once) with LSTM and RcNN models. The integrated approach significantly improves accuracy in challenging conditions like rain, snow, and occlusions.

Keywords:
lane line detectionlane line predictionlong short-term memoryrecurrent neural network

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Driving Systems

Background:

  • Accurate lane line detection is crucial for vehicle safety.
  • Existing methods like YOLO v3 (S × 2S) lack spatial information, leading to poor performance in non-ideal conditions (occlusion, poor lighting, etc.).

Purpose of the Study:

  • To improve lane line detection accuracy and robustness in challenging environmental conditions.
  • To integrate spatial and temporal information for more reliable lane line prediction.

Main Methods:

  • Developed a lane line prediction model using Long Short-Term Memory (LSTM) and Recursive Neural Network (RcNN) to leverage historical lane line data.
  • Combined predicted lane line information with YOLO v3 (S × 2S) detection results using Dempster-Shafer (D-S) evidence theory.

Main Results:

  • The integrated approach significantly enhanced lane line detection accuracy.
  • Improved performance was observed in adverse weather (rainy, snowy) and obstacle scenarios.
  • Reduced system uncertainty in lane line detection.

Conclusions:

  • The proposed method effectively improves lane line detection accuracy and reliability.
  • The combination of deep learning prediction and evidence theory offers a robust solution for autonomous driving systems.
  • This approach addresses key limitations of traditional lane detection algorithms.