# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 4 Experiment
To evaluate the scene captioning performance in diverse driving environments, we employed a large-scale dataset consisting of 69,864 image-caption pairs, each paired with detailed human-annotated captions. The dataset spans a wide range of real-world driving conditions, supporting effective domain adaptation and comprehensive assessment of model performance. As illustrated in Table 3, the dataset encompasses various scenes, weather conditions, times of day, and detected objects, providing a rich context for evaluating vision-language models in complex driving scenarios. \begin{table}
\begin{tabular}{l l} \hline \hline
**Scene** & **Count** \\ \hline City streets & 43,516 \\ Highways & 17,379 \\ Residential areas & 8074 \\ Tunnels & 129 \\ Parking lots & 377 \\ Gas stations & 27 \\ \hline \hline
**Time of Day** & **Count** \\ \hline Daytime & 36,728 \\ Nighttime & 27,971 \\ Dawn/Dusk & 5027 \\ \hline
**Weather Conditions** & **Count** \\ \hline Clear & 37,344 \\ Overcast & 8770 \\ Rainy & 5070 \\ Snowy & 5549 \\ Partly cloudy & 4881 \\ Foggy & 130 \\ \hline
**Detected Object** & **Count** \\ \hline Person & 10,540 \\ Car & 5043 \\ Bus & 12,352 \\ Truck & 5147 \\ Bike & 3108 \\ Motorbike & 2671 \\ Rider & 6011 \\ Train & 6822 \\ Traffic light & 9874 \\ Traffic sign & 8296 \\ \hline
**Traffic Light States** & **Count** \\ \hline Green & 76,284 \\ Red & 6845 \\ Yellow & 8216 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of dataset statistics extracted from the BDD-100K dataset. The table provides the frequency distribution of various attributes, including scene types, times of day, weather conditions, detected object categories, and traffic light states. These statistics reflect the diversity and coverage of the dataset, which is essential for developing robust vision–language models in autonomous driving scenarios. This diverse and richly annotated dataset enables the robust evaluation of caption generation across various lighting conditions, weather scenarios, and traffic densities, reflecting the complexity and contextual richness of real-world driving environment scenes. Although this study primarily reports aggregate performance across the entire dataset, the dataset itself includes a wide range of challenging environmental conditions, such as night-time, rainy, snowy, and foggy scenes, which are known to be critical for the reliability of autonomous systems. This diversity enables future work to conduct a stratified evaluation to analyze model robustness under specific safety-critical scenarios. Incorporating such fine-grained analysis remains an important direction for improving the interpretability and reliability of vision-language models in real-world driving applications. To quantitatively assess the captioning quality of each model, we employed three widely used evaluation metrics: CIDEr [22], BLEU [23], and SPICE [24]. These metrics capture different aspects of the generated captions, including lexical accuracy, fluency, informativeness, and semantic relevance. CIDEr measures the consensus between a generated caption and a set of reference captions using TF-IDF (term frequency-inverse document frequency)-weighted n-gram similarity. It emphasizes how similar the generated caption is to what a human would write for the same image. \[\text{CIDEr}_{n}(c_{i},S_{i})=\frac{1}{|S_{i}|}\sum_{s\in S_{i}}\text{Sim}_{n} (c_{i},s), \tag{7}\]  
where \(c_{i}\) is the candidate caption, \(S_{i}\) is the set of reference captions, and \(\text{Sim}_{n}\) is the cosine similarity between TF-IDF vectors of n-grams. BLEU is a precision-based metric that measures the overlap of n-grams between the candidate and reference captions. BLEU-n (e.g., BLEU-1 to BLEU-4) reflects precision at different n-gram levels. \[\text{BLEU}=\text{BP}\cdot\exp\left(\sum_{n=1}^{N}w_{n}\log p_{n}\right), \tag{8}\]  
where \(p_{n}\) is the precision of n-grams, \(w_{n}\) is the weight (typically uniform), and BP is a brevity penalty to penalize short candidates.