# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 6 Conclusions  
We propose an optimized method for generating accurate captions of driving scenes in autonomous driving environments, emphasizing structured dataset transformation, efficient BLIP-2 adaptation through LoRA, and refined input prompting techniques. Our approach significantly improves domain adaptation, contextual relevance, and interpretability, addressing critical performance limitations inherent in traditional vision-language models (VLMs) for autonomous driving. Our experimental results demonstrate that strategically optimizing only the Q-Former module using LoRA substantially enhances the model's ability to accurately interpret critical road-specific features such as lane structures, traffic signals, and dynamic vehicle interactions, all while maintaining exceptional computational efficiency. This targeted optimization not only conserves computational resources but also ensures the feasibility of deploying our model in resource-constrained autonomous driving systems. Moreover, our structured dataset transformation approach effectively integrates structured autonomous driving datasets into vision-language learning frameworks, optimizing dataset utilization and overcoming limitations associated with insufficient textual annotations commonly encountered in standard datasets. Additionally, our carefully optimized, context-aware prompting strategy explicitly incorporates environmental variables, such as weather conditions and traffic dynamics, thereby generating more coherent, informative, and contextually relevant captions. Overall, our method achieves superior captioning accuracy, enhanced contextual perception, and significantly improved computational efficiency, contributing directly to safer, more transparent, and legally compliant autonomous driving technologies. These optimizations lay a robust foundation for further innovations in vision-language modeling that can effectively bridge structured driving data with sophisticated scene understanding. However, several optimization challenges remain, presenting valuable directions for future research. The dependency on structured datasets highlights the importance of exploring optimization strategies involving unsupervised or self-supervised learning to broaden model generalizability. While LoRA provides considerable computational efficiency improvements, achieving true real-time capabilities necessitates additional optimization efforts, such as advanced model compression techniques and strategic edge-device deployments. Future optimization could also include integrating temporal context through video-based models, significantly improving scene continuity and long-term situational awareness. Moreover, optimizing legal compliance and decision-making reliability through integration with rule-based traffic law knowledge graphs presents another promising research avenue. Finally, human-in-the-loop optimization strategies may further enhance model interpretability, adaptability, and continuous improvement in realistic driving conditions. Beyond the current scope, our model's performance and efficiency can be further optimized for video-based and multimodal data integration by employing advanced parameter-efficient tuning techniques like LoRA, ensuring broader applicability and enhanced real-world performance. Conceptualization, M.C., S.K., D.C. and Y.S. ; methodology, M.C., S.K., D.C. and Y.S. ; software, M.C. ; validation, M.C., S.K. and Y.S. ; formal analysis, M.C., S.K. and Y.S. ; investigation, M.C. and S.K. ; writing--original draft preparation, M.C. and S.K. ; writing--review and editing, M.C., S.K., D.C. and Y.S. ; visualization, M.C., D.C. and S.K. ; supervision, Y.S. ; project administration, M.C. and S.K. 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 Korea government(MSIT).