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Area of Science:

  • Intelligent Transportation Systems
  • Artificial Intelligence in Automotive Engineering
  • Machine Learning for Autonomous Driving

Background:

  • Mixed traffic environments with autonomous and human-driven vehicles present significant safety challenges.
  • Accurate prediction of human-driven vehicle lane change behavior is essential for autonomous vehicle navigation.
  • Driver preferences introduce uncertainty, complicating lane change prediction.

Purpose of the Study:

  • To develop a robust method for mining driving preferences and predicting lane change behavior.
  • To enhance the safety and human-like decision-making of autonomous vehicles.
  • To integrate multi-source information, including vehicle dynamics and driver preferences, for improved prediction.

Main Methods:

  • Constructed a driving operation representation using vehicle dynamics and statistical features.
  • Designed a SimCLR-based contrastive learning framework for unsupervised extraction of high-dimensional driving preference features.
  • Proposed a dual-branch lane change prediction model fusing temporal vehicle state features with implicit driving preference features.

Main Results:

  • The proposed preference feature extractor effectively distinguishes between aggressive, normal, and conservative driving styles.
  • The dual-branch prediction model achieved significantly higher lane change prediction accuracy compared to Transformer and LSTM models.
  • Experimental validation was conducted using the HighD dataset.

Conclusions:

  • The developed approach effectively captures and utilizes driving preferences for accurate lane change prediction.
  • This research provides a technical foundation for enhancing the safety and human-likeness of autonomous driving systems.
  • The findings contribute to more reliable decision-making in mixed autonomous and human-driven traffic scenarios.