VLMPed-CoT: A Large Vision-Language Model with Chain-of-Thought Mechanism for Pedestrian Crossing Intention Prediction

This paper proposes a lightweight vision and language large model based approach for pedestrian crossing intention prediction.We introduce a two-stage tuning strategy designed to enhance the model’s explicit and implicit reasoning capabilities. In our experiments, we first use Gemini to generate chain-of-thought annotations from the raw data. We then fine-tune a lightweight Qwen 2.5 model on the resulting CoT dataset using the proposed two-stage procedure. Experiments are conducted on two public datasets, PIE and JAAD. This replication package includes all materials required for readers to understand and reproduce the analyses reported in the paper.