# Enhanced Retrieval-Augmented Generation Using Low-Rank Adaptation
###### Abstract  
Recent advancements in retrieval-augmented generation (RAG) have substantially enhanced the efficiency of information retrieval. However, traditional RAG-based systems still encounter challenges, such as high latency in output decision making, the inaccurate retrieval of road traffic-related laws and regulations, and considerable processing overhead in large-scale searches. This study presents an innovative application of RAG technology for processing road traffic-related laws and regulations, particularly in the context of unmanned systems like autonomous driving. Our approach integrates embedding generation using a LoRA-enhanced BERT-based uncased model and an optimized retrieval strategy that combines maximal marginal similarity score thresholding with contextual compression retrieval. The proposed system enhances and achieves improved retrieval accuracy while reducing processing overhead. Leveraging road traffic-related regulatory datasets, the LoRA-enhanced model demonstrated remarkable performance gains over traditional RAG methods. Specifically, our model reduced the number of trainable parameters by 13.6% and lowered computational costs by 18.7%. Performance evaluations using BLEU, CIDEr, and SPICE scores revealed a 4.36% increase in BLEU-4, a 6.83% improvement in CIDEr, and a 5.46% improved in SPICE, confirming greater structural accuracy in regulatory text generation. Additionally, our method achieved an 8.5% improvement in retrieval accuracy across key metrics, outperforming baseline RAG systems. These contributions pave the way for more efficient and reliable traffic regulation processing, enabling better decision making in autonomous systems. First, latency in decision making can be a significant issue. RAG involves both information retrieval and text generation, the latency introduced can hinder timely responses to rapidly changing road conditions, such as sudden pedestrian crossings or unexpected obstacles. Traditional RAG-based systems operate through a sequential process where document retrieval and embedding computation must fully complete before text generation can begin. This two-stage architecture creates significant computational bottlenecks, as the system must process and rank large volumes of road traffic-related laws and regulations before generating responses for decision making. In autonomous driving scenarios where milliseconds matter, these processing delays may compromise the safety and reliability of decision-making systems, particularly when navigating complex traffic situations requiring immediate action. Another challenge is the potential inaccuracy in retrieving road traffic-related laws and regulation information. RAG system may sometimes retrieve outdated or irrelevant traffic regulations that do not align with current road conditions. This inaccuracy stems from the complexity of road traffic-related laws and their specialized terminology, establishing a direct causal link to non-compliance. When retrieval systems extract inappropriate road traffic-related standards, autonomous systems make decisions based on incorrect regulatory frameworks. This misalignment propagates through the decision-making process, causing the system to implement actions that violate current local traffic laws. Thus, retrieval accuracy directly determines whether an autonomous vehicle operates within road traffic boundaries, creating a clear causal relationship between information retrieval quality and regulatory compliance. Finally, computational overhead and scalability constraints pose additional challenges. simultaneously addressing complex road scenarios requires extensive retrieval and text generation computations. This high computational burden not only increases operational costs but also creates bottlenecks that limit scalability and reduce accuracy in decision making, particularly in resource-constrained autonomous driving environments.