Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding

Scene understanding is essential for enhancing driver safety, generating human-centric explanations for Automated Vehicle (AV) decisions, and leveraging Artificial Intelligence (AI) for retrospective driving video analysis. This study developed a dynamic scene retrieval system using Contrastive Language-Image Pretraining (CLIP) models, which can be optimized for real-time deployment on edge devices. The proposed system outperforms state-of-the-art in-context learning methods, including the zero-shot capabilities of GPT-4o, particularly in complex scenarios. By conducting frame-level analysis on the Honda Scenes Dataset, which contains a collection of about 80 hours of annotated driving videos capturing diverse real-world road and weather conditions, our study highlights the robustness of CLIP models in learning visual concepts from natural language supervision. Results also showed that fine-tuning the CLIP models, such as ViT-L/14 and ViT-B/32, significantly improved scene classification, achieving a top F1 score of 91.1%. These results demonstrate the ability of the system to deliver rapid and precise scene recognition, which can be used to meet the critical requirements of Advanced Driver Assistance Systems (ADAS). This study shows the potential of CLIP models to provide scalable and efficient frameworks for dynamic scene understanding and classification. Furthermore, this work lays the groundwork for advanced autonomous vehicle technologies by fostering a deeper understanding of driver behavior, road conditions, and safety-critical scenarios, marking a significant step toward smarter, safer, and more context-aware autonomous driving systems.

Using Multimodal Large Language Models for Automated Detection of Traffic Safety Critical Events

Traditional approaches to safety event analysis in autonomous systems have relied on complex machine learning models and extensive datasets for high accuracy and reliability. However, the advent of Multimodal Large Language Models (MLLMs) offers a novel approach by integrating textual, visual, and audio modalities, thereby providing automated analyses of driving videos. Our framework leverages the reasoning power of MLLMs, directing their output through context-specific prompts to ensure accurate, reliable, and actionable insights for hazard detection. By incorporating models like Gemini-Pro-Vision 1.5 and Llava, our methodology aims to automate the safety critical events and mitigate common issues such as hallucinations in MLLM outputs. Preliminary results demonstrate the framework's potential in zero-shot learning and accurate scenario analysis, though further validation on larger datasets is necessary. Furthermore, more investigations are required to explore the performance enhancements of the proposed framework through few-shot learning and fine-tuned models. This research underscores the significance of MLLMs in advancing the analysis of the naturalistic driving videos by improving safety-critical event detecting and understanding the interaction with complex environments.