[2603.02528] LLM-MLFFN: Multi-Level Autonomous Driving Behavior Feature Fusion via Large Language Model

Computer Science > Artificial Intelligence arXiv:2603.02528 (cs) [Submitted on 3 Mar 2026] 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 View a PDF of the paper titled LLM-MLFFN: Multi-Level Autonomous Driving Behavior Feature Fusion via Large Language Model, by Xiangyu Li and 7 other authors View PDF HTML (experimental) 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 ...