LLM-MLFFN: Multi-Level Autonomous Driving Behavior Feature Fusion via Large Language Model
arXiv:2603.02528v1 Announce Type: new 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
arXiv:2603.02528v1 Announce Type: new 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.
Executive Summary
This article proposes a novel approach to autonomous vehicle driving behavior classification, leveraging the power of large language models (LLMs) to enhance the accuracy and robustness of classification results. The proposed LLM-MLFFN framework integrates priors from largescale pre-trained models and employs a multi-level approach to extract statistical, behavioral, and dynamic features from driving data. The framework demonstrates superior performance on the Waymo open trajectory dataset, achieving a classification accuracy of over 94%. Ablation studies validate the critical contributions of multi-level fusion, feature extraction strategies, and LLM-derived semantic reasoning. The results suggest that integrating structured feature modeling with language-driven semantic abstraction provides a principled and interpretable pathway for robust autonomous driving behavior classification.
Key Points
- ▸ The proposed LLM-MLFFN framework integrates priors from largescale pre-trained models and employs a multi-level approach to enhance classification accuracy.
- ▸ The framework demonstrates superior performance on the Waymo open trajectory dataset, achieving a classification accuracy of over 94%.
- ▸ Ablation studies validate the critical contributions of multi-level fusion, feature extraction strategies, and LLM-derived semantic reasoning.
Merits
Strength
The proposed framework demonstrates superior performance on a challenging dataset, showcasing the potential of LLMs in autonomous driving applications.
Methodological innovation
The framework's multi-level approach to feature extraction and fusion provides a principled and interpretable pathway for robust autonomous driving behavior classification.
Interpretability and explainability
The use of LLMs enables the transformation of raw data into high-level semantic features, providing insights into the driving behavior classification process.
Demerits
Limitation
The framework's reliance on largescale pre-trained models may limit its applicability to smaller-scale datasets or domains with limited training data.
Computational complexity
The multi-level feature fusion network may introduce significant computational complexity, requiring specialized hardware or optimization techniques to achieve real-time performance.
Data quality and availability
The framework's performance may be sensitive to the quality and availability of training data, which may not always be feasible or reliable in real-world applications.
Expert Commentary
The proposed LLM-MLFFN framework represents a significant contribution to the field of autonomous driving, leveraging the power of large language models to enhance the accuracy and robustness of driving behavior classification results. The framework's multi-level approach to feature extraction and fusion provides a principled and interpretable pathway for robust autonomous driving behavior classification. While the framework's reliance on largescale pre-trained models may limit its applicability to smaller-scale datasets or domains with limited training data, the results suggest that integrating structured feature modeling with language-driven semantic abstraction provides a promising approach to robust autonomous driving behavior classification. Further research is needed to explore the potential of this framework in real-world applications and to address the computational complexity and data quality concerns.
Recommendations
- ✓ Future research should focus on developing smaller-scale pre-trained models that can be adapted to smaller-scale datasets or domains with limited training data.
- ✓ Specialized hardware or optimization techniques should be developed to achieve real-time performance of the multi-level feature fusion network.
- ✓ Further research is needed to explore the potential of the proposed framework in real-world applications and to address the data quality and availability concerns.