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LLM-MLFFN: Multi-Level Autonomous Driving Behavior Feature Fusion via Large Language Model

arXiv – CS AI|Xiangyu Li, Tianyi Wang, Xi Cheng, Rakesh Chowdary Machineni, Zhaomiao Guo, Sikai Chen, Junfeng Jiao, Christian Claudel|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed LLM-MLFFN, a new framework combining large language models with multi-level feature fusion to classify autonomous vehicle driving behaviors. The system achieves over 94% accuracy on the Waymo dataset by integrating numerical driving data with semantic features extracted through LLMs.

Key Takeaways

→LLM-MLFFN combines large language models with traditional numerical analysis for autonomous vehicle behavior classification.
→The framework achieved over 94% classification accuracy on the Waymo open trajectory dataset, outperforming existing machine learning models.
→The system uses three core components: multi-level feature extraction, semantic description via LLMs, and dual-channel feature fusion.
→Integration of structured numerical data with language-driven semantic abstraction improves interpretability and robustness.
→The research demonstrates practical applications for AV safety validation and traffic integration analysis.

#llm #autonomous-vehicles #machine-learning #waymo #feature-fusion #classification #ai-research #semantic-analysis

Read Original →via arXiv – CS AI

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