# Enhanced Retrieval-Augmented Generation Using Low-Rank Adaptation
## 6 Conclusions  
This paper proposed a LoRA-enhanced RAG framework to improve the road traffic-related laws and regulations retrieval. Conventional RAG models heavily rely on vector similarity searches, which often fail to capture nuanced regulatory contexts. To address this limitation, we introduced a LoRA-enhanced BERT-based uncased retriever, a trans \begin{table}
\begin{tabular}{l l l} \hline
**Method** & **Trainable Params (\%)** & **Accuracy (\%)** \\ \hline Full fine-tuning & 100\% & 96.10 \\ Adapter & 3.60\% & 88.53 \\ Prompt tuning & 0.15\% & 85.24 \\ LoRA & 0.54\% & 95.83 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of parameter-efficient fine-tuning methods. former attention-based similarity adjustment, and a multi-stage filtering mechanism to refine retrieval accuracy. Experimental results demonstrated that the proposed method substantially improves traffic regulation information retrieval. The LoRA-enhanced model achieved the following:  
* Higher BLEU, CIDEr, and SPICE scores, indicating improved regulatory text generation quality. * Reduced processing overhead, making it more suitable for accurate decision-making conditions. * Enhanced retrieval accuracy through multi-stage filtering, ensuring more relevant road traffic-related laws and regulations are selected. By integrating LoRA into the retrieval model, we optimized both parameter efficiency and retrieval precision. The findings signify that LoRA can effectively refine the document embeddings, improving context-aware traffic regulation processing. While our approach demonstrates improvements in efficiency and reliability, it is important to acknowledge several conditions that may affect performance in practical applications. First, the system's efficiency heavily depends on the size and complexity of the used legal corpus; extremely large or highly specialized legal collections may require additional optimization. Second, the reliability of retrieved information remains contingent on the currency and comprehensiveness of the underlying legal database. Finally, while our system reduces computational overhead, deployment in resource-constrained environments may still face latency challenges that could affect time-sensitive decision making. Future research will focus on expanding the dataset to include a broader range of legal documents and optimizing LoRA hyperparameters for further performance gains. Additionally, we aim to enhance the processing capabilities, particularly addressing the identified limitations in resource-constrained environments, enabling more efficient and legally compliant decision making for autonomous driving systems. Our current implementation demonstrates the potential for improved alignment with road traffic legal standards when trained on domain-specific road traffic-related laws and regulations documents from a specific jurisdiction, but the technical approach could be adapted to other domains with appropriate domain-specific training data. Conceptualization, Y.C., S.K. and Y.S. ; methodology, Y.C., S.K. and Y.S. ; software, Y.C. and Y.C.F.B. ; validation, S.K. and Y.S. ; formal analysis, Y.C. and S.K. ; investigation, Y.C. and S.K. ; writing--original draft preparation, Y.C. and S.K. ; writing--review and editing, Y.C., S.K., Y.C.F.B. and Y.S. ; visualization, Y.C., S.K., Y.C.F.B. and Y.S. ; supervision, Y.S. ; project administration, Y.S. All authors have read and agreed to the published version of the manuscript. This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2025-RS-2023-00254592) grant funded by the Korean government (MSIT). Not applicable. The original contributions presented in the paper are included in the article, whilst further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.