ETS-Data
ETS-Data is jointly established by Tsinghua University Press and School of Vehicle and Mobility, Tsinghua University, China and is a publicly accessible database, providing indispensable materials for result replications (data, codes, scripts, simulations, experimental designs, etc.). ETS-Data has been indexed by DCI (Data Citation Index) and Google Dataset Search.
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3D LiDAR and image data-level fusion for traffic vehicle detection
Asia, China Replication Code for Crash Risk Prediction Guided by Large Language Models Using Pre-Crash Trajectories
This code-only replication package accompanies the revised manuscript “Crash Risk Prediction Guided by Large Language Models Using Pre-Crash Trajectories” submitted to Communications in Transportation Research (Manuscript ID: COMMTR-2026-0063.R1).
The package contains the implementation of the lightweight temporal graph student, offline LLM-guided knowledge distillation and fine-tuning workflows, evaluation and deployment benchmarking scripts, configuration files, environment specifications, a documented data interface, and a synthetic end-to-end smoke test. It also includes a Replication Explanatory File and a SHA-256 integrity manifest.
Asia, China, Nanjing RAVE code
The basic code of RAVE (End-to-end Hierarchical Visual Localization with Rasterized and Vectorized HD Map)
Asia, China, Beijing Replication data for: Physics-Informed Platooning with Learning-Augmented Calibration and Compensation: A Real-World Study
This replication package contains all data and code necessary to reproduce the results reported in the manuscript "Physics-Informed Platooning with Learning-Augmented Calibration and Compensation: A Real-World Study" (COMMTR-2026-0163).
Hardware: Intel Core i5-9400F, 32GB RAM
Software: Python 3.8+, PyTorch 1.12+, d3rlpy 2.0+, Ubuntu 20.04/ROS Noetic
Asia, China, Wuxi, Jiangsu Underwater Sonar Moving Target (USMT) Dataset
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.
Asia, China, Cangzhou 
Communications in Transportation Research was launched in 2021, with academic support provided by Tsinghua University and China Intelligent Transportation Systems Association. The Editors-in-Chief are Professor Xiaobo Qu, a member of the Academia Europaea from Tsinghua University and Professor Shuai’an Wang from Hong Kong Polytechnic University. The journal mainly publishes high-quality, original research and review articles that are of significant importance to emerging transportation systems, aiming to become an international platform and window for showcasing and exchanging innovative achievements in transportation and related fields, to promote the exchange and development of transportation research between China and the international academic community. It has been indexed in SCIE, SSCI, ESCI, Ei Compendex, Scopus, DOAJ, TRID and other databases. On June 20, 2024, Communications in Transportation Research achieved its first Impact Factor of 12.5, ranking it top in the "TRANSPORTATION" category (1/58, Q1), and its 2023 CiteScore of 15.2 places it in the top 5% of journals in the Scopus database.
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