# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 5 Discussion  
As our experimental results showed, optimizing the Q-Former module of BLIP-2 with a LoRA technique significantly enhanced the model's domain adaptation in autonomous driving environments. Specifically, the proposed method improved structured dataset transformation, domain-specific adaptation based on LoRA, and contextual input prompting techniques, resulting in higher scene captioning performance. First, structured dataset transformation was achieved through a template-based data preprocessing pipeline, which effectively converted structured dataset labels (e.g., BDD-100K) into natural language labels [6]. This transformation facilitated the use of structured \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Dropout** & **BLEU-4** & **CIDEr** & **SPICE** & **1 Epoch Time** \\ \hline Original BLIP-2 & N/A & 0.0117 & 0.0340 & 0.1002 & N/A \\ Enhanced BLIP-2 (r = 8, a = 16) & 0.08 & 0.1311 & 0.1832 & 0.3820 & 1.0 h \\ Enhanced BLIP-2 (r = 8, a = 16) & 0.05 & 0.1315 & 0.1985 & 0.3941 & 1.0 h \\ Enhanced BLIP-2 (r = 8, a = 16) & **0.02** & **0.1318** & **0.2025** & **0.4276** & **1.0 h** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of BLIP-2 models with different dropout rates applied to the Q-Former module during LoRA-based optimization. All enhanced configurations (\(r=8\), \(\alpha=16\)) significantly outperform the original BLIP-2 model. The model with a dropout rate of 0.02 achieves the highest BLEU-4, CIDEr, and SPICE scores, indicating that lower dropout leads to better fluency and semantic richness in driving environment captions. Figure 6: Comparison of BLEU-4, SPICE, and CIDEr scores between the original BLIP-2 model and LoRA-enhanced models with varying dropout rates (\(r=8\), \(\alpha=16\)). All LoRA configurations show substantial improvements over the original model. The model with dropout=0.02 achieved the highest scores across all metrics, demonstrating that lower dropout rates can enhance caption fluency and semantic relevance in driving environment scenes. driving datasets for training VLMs and helped overcome the textual annotation problem that has been a limiting factor in traditional autonomous driving datasets [5]. Consequently, the model learned a better and more contextually aware scene representation, which improved the quality and informativeness of the generated captions. Second, LoRA-based domain-specific adaptation significantly improved the model's capability to recognize road environment factors such as lane markings, traffic signals, and vehicle interactions. These factors are often misunderstood or misinterpreted by pre-trained VLM. Fine-tuning the Q-Former module with LoRA improved the generalization problem of pre-trained models while reducing computational costs. Experimental results showed that LoRA enhanced the retrieval and captioning performance while maintaining efficiency and feasibility for autonomous driving applications. Third, context-aware input prompting was optimized to include critical driving contexts, including weather conditions, traffic signals, and vehicle interactions, in the captioning process [8]. The effective structuring of the input prompts using our method resulted in captions that were more contextually relevant, globally coherent, and semantically rich. This method helped overcome a major limitation of traditional captioning models for driving environment scenes, which often produced object-centric descriptions without considering scene dynamics [9]. Overall, our method achieved state-of-the-art performance, as indicated by BLEU, CIDEr, and SPICE scores, and it showed significant improvements in the scene captioning accuracy and contextual relevance compared with the baseline models. These results showed the effectiveness of our method in enhancing the interpretability, contextual perception, and domain adaptation of VLMs for autonomous driving environments.