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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Vehicle-Dynamics-Aware motion planning for pothole-hazard mitigation
Replication code associated with the manuscript “Vehicle-Dynamics-Aware Motion Planning for Pothole-Hazard Mitigation” (Journal of Intelligent and Connected Vehicles, Manuscript ID: JICV-2026-0051.R2). The package includes the vehicle-dynamics simulation environment, reinforcement-learning training, comparison baselines, paired evaluation, and statistical reporting scripts required to reproduce the numerical experiments.
Asia, China PACE-V2X: Planning-aware and communication-efficient semantic interaction for V2X cooperative end-to-end autonomous driving
tools/run_bev_downlink_fused_formal675.sh to evaluate 675 frames for L2 error, collision rate, and communication payload.run_bev_downlink_label_and_train.sh for gate training, then calibrate via calibrate_bev_downlink_gates.py. Asia, China, Beijing ROSE: Roadside Oversight-Guided Scenario Enhancement with Self-Supervised Coupling for multi-modal Perception
ROSE is a PyTorch/MMDetection3D-based framework for robust roadside Camera–LiDAR 3D object detection under adverse weather. The code implements physics-guided cross-modal augmentation (RISA), teacher–student self-supervised coupling, adaptive training analysis, and tools for training, evaluation, and visualization on DAIR-V2X-style datasets.
Asia, China Associated codes for Paper ‘A multi-agent asynchronous cooperative on-ramp merging strategy with decision-making priorities in mixed traffic’
The replication package for the paper “A multi-agent asynchronous cooperative on-ramp merging strategy with decision-making priorities in mixed traffic” is available. It contains source code, configurations, pretrained checkpoints, simulation outputs, and scripts for the proposed attention-order framework. The package reproduces the proposed model simulations, learning-curve analysis, ordering agreement, safety evaluation, and finite-stage-game Stackelberg Equilibrium.
Asia, China 3D LiDAR and image data-level fusion for traffic vehicle detection
Asia, China 
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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