Visual Reasoning at Urban Intersections: FineTuning GPT-4o for Traffic Conflict Detection
On the map: LLM reasoning → Chain-of-thought reasoning for driving
Abstract (arXiv)
Traffic control in unsignalized urban intersections presents significant challenges due to the complexity, frequent conflicts, and blind spots. This study explores the capability of leveraging Multimodal Large Language Models (MLLMs), such as GPT-4o, to provide logical and visual reasoning by directly using birds-eye-view videos of four-legged intersections. In this proposed method, GPT-4o acts as intelligent system to detect conflicts and provide explanations and recommendations for the drivers. The fine-tuned model achieved an accuracy of 77.14%, while the manual evaluation of the true predicted values of the fine-tuned GPT-4o showed significant achievements of 89.9% accuracy for model-generated explanations and 92.3% for the recommended next actions. These results highlight the feasibility of using MLLMs for real-time traffic management using videos as inputs, offering scalable and actionable insights into intersections traffic management and operation. Code used in this study is available at https://github.com/sarimasri3/Traffic-Intersection-Conflict-Detection-using-images.git.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning. Builds on 13 of its 30 references that are in the map. First posted on arXiv 27 Feb 2025.
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13 of 30 references.
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- 2023Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles12 Oct
Other references · 12
- 2023DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Modelcited by 455
- 2023Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Drivingcited by 231
- 2023LLMLight: Large Language Models as Traffic Signal Control Agentscited by 73
- 2023MTD-GPT: A Multi-Task Decision-Making GPT Model for Autonomous Driving at Unsignalized Intersectionscited by 38
- 2024LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environmentscited by 31
- 2024TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Modelscited by 23
- 2024iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvementcited by 23
- 2024RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Modelscited by 20
- 2024Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigmcited by 12
- 2024Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testingcited by 10
- 2022Evaluating a Signalized Intersection Performance Using Unmanned Aerial Datacited by 3
- 2021Impact of risk factors on work zone crashes using logistic models and Random Forestcited by 2
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- 2024GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events
- 2025Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding
- 2025Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends
- 2025CoT-VLM4Tar: Chain-of-Thought Guided Vision-Language Models for Traffic Anomaly Resolution
- 2026OmniTraffic: A Controllable Generation Pipeline and Benchmark for Spatio-Temporal Traffic Reasoning
- 2025RoadBench: Benchmarking MLLMs on Fine-Grained Spatial Understanding and Reasoning under Urban Road Scenarios
- 2023GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction
- 2026Reasoning to Regulate: Chain-of-Thought for Traffic Rule Understanding