Year: 2025
Type: article
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.
Source: Applied Sciences
DOI: 10.3390/app15084425
Institution: Dongguk University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G2094304639, G3626246151, G3812690899
OpenAlex ID: W4409526155