# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 1 Introduction  
Autonomous driving technology is evolving rapidly, and it is advancing beyond the tasks of perception and decision-making to include the capability to generate descriptive captions for driving environment scenes, which would enhance both the interpretability and understanding of complex traffic scenarios. A critical aspect of this advancement is the captioning of driving environment scenes, which involves generating textual descriptions of dashcam-recorded driving environments. This capability can enhance safety, improve human interpretability of autonomous driving scenarios, and support decision-making. Recent research in this domain has primarily focused on three key areas: vision-language modeling, autonomous driving datasets, and driving environment scene captioning. Vision-language modeling techniques involve the integration of vision and language models, such as bootstrapping language-image pre-training (BLIP-2) [1], contrastive language-image pre-training (CLIP) [2], and large language and vision assistant (LLaVA) [3], to enhance the understanding of visual scenes. Autonomous driving datasets, such as the Large-scale Diverse Driving Video Database (BDD-100K) [4], Karlsruhe Institute of Technology dataset (KITTI) [5], and nuScenes dataset [6], provide extensive labeled data for training machine learning models in real-world driving environments. BDD-100K is one of the largest autonomous driving datasets, containing 100,000 video clips with annotations for object detection, lane detection, drivable areas, and weather conditions, and it is highly suitable for diverse urban and highway driving scenarios. Similarly, KITTI, a widely used dataset, offers stereo images, light detection and ranging (LiDAR) point clouds, and ground truth annotations for various perception tasks, including object detection, optical flow estimation, and scene segmentation, with a strong focus on high-resolution sensor data for 3D perception. In contrast, nuScenes is a multi-sensor dataset that integrates camera, LiDAR, radar, and global positioning system (GPS) inputs along with detailed 3D bounding box annotations to provide a comprehensive understanding of complex urban driving environments, including traffic participants, weather conditions, and scene dynamics. Furthermore, transformer-based models for driving environment scene captioning have been extensively studied to improve the textual representation of driving environments. Despite the remarkable progress of vision-language models in general-purpose image captioning, these models often underperform when applied to dashcam footage collected in real-world driving environments. Dashcam images present unique challenges, such as rapid motion, occlusion, low-light conditions, and highly structured yet dynamic scenes involving roads, vehicles, and pedestrians. Existing models, primarily trained on generic web-scale datasets, lack the ability to capture these road-specific contextual cues, resulting in inaccurate or overly generic captions. Consequently, the direct application of pre-trained vision-language models to dashcam scenes has proven to be unreliable for safety-critical autonomous driving systems. This highlights the urgent need for domain-specific optimization strategies that go beyond conventional fine-tuning involving parameter-efficient techniques, structural adaptation modules (e.g., LoRA), and objective function designs tailored to driving environments to ensure the model effectively learns from structured, context-rich road scenes under diverse environmental conditions. Despite advances in the development of vision-language models and autonomous driving datasets, there are several key challenges that hinder the accurate captioning of driving environment scenes. Vision-language models do not work well with structured label datasets, which makes it difficult to generate scene captions. This issue can be divided into three specific challenges.