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

• URL: http://arxiv.org/abs/2603.02528v1
• Date: Tue, 03 Mar 2026 02:26:04 GMT
• Title: LLM-MLFFN: Multi-Level Autonomous Driving Behavior Feature Fusion via Large Language Model
• Authors: Xiangyu Li, Tianyi Wang, Xi Cheng, Rakesh Chowdary Machineni, Zhaomiao Guo, Sikai Chen, Junfeng Jiao, Christian Claudel,
• Abstract summary: This paper presents LLM-MLFFN, a novel large language model (LLM)-enhanced multi-level feature fusion network.<n>The proposed LLM-MLFFN framework integrates priors from largescale pre-trained models and employs a multi-level approach to enhance classification accuracy.<n> Evaluation on the open trajectory dataset demonstrates the superior performance of the proposed LLM-MLFFN, achieving a classification accuracy of over 94%.
• Score: 8.63792214154021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate classification of autonomous vehicle (AV) driving behaviors is critical for safety validation, performance diagnosis, and traffic integration analysis. However, existing approaches primarily rely on numerical time-series modeling and often lack semantic abstraction, limiting interpretability and robustness in complex traffic environments. This paper presents LLM-MLFFN, a novel large language model (LLM)-enhanced multi-level feature fusion network designed to address the complexities of multi-dimensional driving data. The proposed LLM-MLFFN framework integrates priors from largescale pre-trained models and employs a multi-level approach to enhance classification accuracy. LLM-MLFFN comprises three core components: (1) a multi-level feature extraction module that extracts statistical, behavioral, and dynamic features to capture the quantitative aspects of driving behaviors; (2) a semantic description module that leverages LLMs to transform raw data into high-level semantic features; and (3) a dual-channel multi-level feature fusion network that combines numerical and semantic features using weighted attention mechanisms to improve robustness and prediction accuracy. Evaluation on the Waymo open trajectory dataset demonstrates the superior performance of the proposed LLM-MLFFN, achieving a classification accuracy of over 94%, surpassing existing machine learning models. Ablation studies further validate the critical contributions of multi-level fusion, feature extraction strategies, and LLM-derived semantic reasoning. These results suggest that integrating structured feature modeling with language-driven semantic abstraction provides a principled and interpretable pathway for robust autonomous driving behavior classification.

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