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September 5, 2025SensorsOpen Access

A Driving-Preference-Aware Framework for Vehicle Lane Change Prediction

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Authors

YLYing LyuYWYulin WangHLHuan Liu

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Overview

This framework predicts lane change behavior in mixed traffic, suggesting better integration of human preferences in autonomous vehicle systems.

Key Points

  • The model significantly improves lane change prediction accuracy, enhancing the safety of autonomous vehicles.
  • Performance tests indicate a notable advantage over traditional models, including Transformer and LSTM.
  • The dual-branch model integrates driving preferences and vehicle states, allowing for a comprehensive prediction approach.
  • Using the HighD dataset, the approach effectively captures aggressive, normal, and conservative driving styles.

Cite This Study

Lyu et al. (2025) studied this question.

synapsesocial.com/papers/68bb3edf2b87ece8dc956cedhttps://doi.org/10.3390/s25175342
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Also Consider

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  1. 1Fuzzy Logic-Based Driving Style Classification for Lane-Change Prediction in Intelligent Transportation Systems2026 · 1 citations
  2. 2A Naturalistic Driving Study for Lane Change Detection and Personalization2024 · 1 citations
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  4. 4Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving2024 · 35 citations
  5. 5Modeling Discretionary Lane-Changing Decisions: A Multi-Vehicle Information Enhanced Machine Learning Approach2026