The Underwater Sonar Moving Target (USMT) dataset is a forward-looking sonar (FLS) image dataset for low-pixel underwater moving-target perception and segmentation. It contains 21 temporally ordered sequences and 7,136 annotated rectangular B-scan FLS images collected during AUV experiments in a controlled lake environment. Each sonar image is accompanied by a pixel-level annotation in LabelMe JSON format and a TXT file containing the corresponding sonar acquisition metadata. The annotations were generated using X-AnyLabeling with the SAM 2 video model and subsequently reviewed and refined manually. The dataset can support research on underwater intelligent perception, sonar image segmentation, and temporal modeling of moving targets.

A project for a study on LLM-designed cooperative control strategies for connected and automated vehicles at unsignalized intersections. It includes the Python source code, SUMO simulation files, processed scenario data, experiment logs, and documentation needed to reproduce and inspect the main computational experiments.

This repository implements RADGCN, a relation-aware graph neural network for network-wide traffic state estimation (traffic kriging) when only a subset of road sensors is observed. It accompanies the manuscript Relation-diffusion augmented network-wide traffic state estimation.
Rather than learning adaptive relations from zero-filled features at unobserved nodes, RADGCN first learns functional relations among nodes with reliable observations. It then diffuses these relations across the directed road graph and uses the resulting relation kernel alongside forward and backward physical diffusion to reconstruct traffic speeds at unobserved nodes.

This repository provides the code and supporting files for reproducing the experiments reported in “Distilling Vision-Language Models for Explainable Vehicle Collision Prediction.” It includes data-processing scripts for generating collision-prone and collision-free video clips from the MM-AU and CCD datasets, dataset manifests and annotations, VLM fine-tuning and knowledge-distillation scripts, and evaluation tools. The code was developed in a Linux environment using Python 3.10 and CUDA 11.8 or later. The required dependencies are listed in `requirements.txt`.