Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing

Understand

This study aims to comprehensively review and empirically evaluate the application of multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object detection for transportation systems.

• In the first fold, we provide a background about the potential benefits of MLLMs in transportation applications and conduct a comprehensive review of current MLLM technologies in previous studies.
• We highlight their effectiveness and limitations in object detection within various transportation scenarios.
• The second fold involves providing an overview of the taxonomy of end-to-end object detection in transportation applications and future directions.