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Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization.

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This study introduces an advanced Long Short-Term Memory model with an attention mechanism for accurate autonomous vehicle trajectory prediction. The model enhances safety in complex traffic by considering vehicle interactions and road geometry.

Keywords:
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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Computer Science

Background:

  • Autonomous vehicles require sophisticated trajectory prediction for safe navigation.
  • Predicting vehicle behavior is complex due to driver variations and inter-vehicle interactions.

Purpose of the Study:

  • To develop an accurate trajectory prediction model for autonomous vehicles.
  • To improve the safety and planning capabilities of level 3+ autonomous driving systems.

Main Methods:

  • Utilized a Long Short-Term Memory (LSTM) encoder-decoder model with an attention mechanism.
  • Trained the model on the large-scale Highway Drone (HighD) dataset.
  • Developed a road geometry linearization method for broader applicability.

Main Results:

  • The proposed model demonstrated effective future trajectory prediction.
  • The attention mechanism successfully managed the importance of surrounding vehicle flow and ego-vehicle dynamics.
  • Road geometry linearization improved model performance across diverse road environments.

Conclusions:

  • The LSTM model with attention offers a robust solution for vehicle trajectory prediction.
  • The integration of road geometry linearization enhances the model's adaptability and performance.
  • This research contributes to safer and more efficient autonomous driving systems.