Leveraging Bird Eye View Video and Multimodal Large Language Models for Real-Time Intersection Control and Reasoning
Managing traffic flow through urban intersections is challenging. Conflicts involving a mix of different vehicles with blind spots makes it relatively vulnerable for crashes to happen. This paper presents a new framework based on a fine-tuned Multimodal Large Language Model (MLLM), GPT-4o, that can...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Safety |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-576X/11/2/40 |
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| Summary: | Managing traffic flow through urban intersections is challenging. Conflicts involving a mix of different vehicles with blind spots makes it relatively vulnerable for crashes to happen. This paper presents a new framework based on a fine-tuned Multimodal Large Language Model (MLLM), GPT-4o, that can control intersections using bird eye view videos taken by drones in real-time. This fine-tuned GPT-4o model is used to logically and visually reason traffic conflicts and provide instructions to the drivers, which aids in creating a safer and more efficient traffic flow. To fine-tune and evaluate the model, we labeled a dataset that includes three-month drone videos, and their corresponding trajectories recorded in Dresden, Germany, at a 4-way intersection. Preliminary results showed that the fine-tuned GPT-4o achieved an accuracy of about 77%, outperforming zero-shot baselines. However, using continuous video-frame sequences, the model performance increased to about 89% on a time serialized dataset and about 90% on an unbalanced real-world dataset, respectively. This proves the model’s robustness in different conditions. Furthermore, manual evaluation by experts includes scoring the usefulness of the predicted explanations and recommendations by the model. The model surpassed on average rating of 8.99 out of 10 for explanations, and 9.23 out of 10 for recommendations. The results demonstrate the advantages of combining MLLMs with structured prompts and temporal information for conflict detection. These results offer a flexible and robust prototype framework to improve the safety and effectiveness of uncontrolled intersections. The code and labeled dataset used in this study are publicly available (see Data Availability Statement). |
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| ISSN: | 2313-576X |