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Guide for authors

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.

Latest Update

List

  • Published on: 2025-03-20

    MetaSSC: Enhancing 3D Semantic Scene Completion for Autonomous Driving through Meta-Learning and Long-sequence Modeling 

    Yansong Qu, Zixuan Xu, Zilin Huang, Zihao Sheng, Sikai Chen, Tiantian Chen

    Our approach begins with a voxel-based semantic segmentation (SS) pretraining task, designed to explore the semantics and geometry of incomplete regions while acquiring transferable meta-knowledge. Using simulated cooperative perception datasets, we supervise the training of a single vehicle’s perception using the aggregated sensor data from multiple nearby connected autonomous vehicles (CAVs), generating richer and more comprehensive labels. This meta-knowledge is then adapted to the target domain through a dual-phase training strategy—without adding extra model parameters—ensuring efficient deployment. To further enhance the model’s ability to capture long-sequence relationships in 3D voxel grids, we integrate Mamba blocks with deformable convolution and large-kernel attention into the backbone network. Extensive experiments show that MetaSSC achieves state-of-the-art performance, surpassing competing models by a significant margin, while also reducing deployment costs. 

    Connected and automated vehiclesAutonomous vehicles
    DOI: 10.26599/ETSD.2025.9190009
    CSTR: 32009.11.ETSD.2025.9190009
    North America, United States
  • Published on: 2025-03-18

    Road Network Data in Wangjing District, Beijing, China

    Xiangdong Chen, Shen Li, Meng Li

    NodeSet_2D.mat’ and LinkSet_2D.mat’ describe the node set and link set in Wangjing road network, and ‘ODset.mat’ describe the O-D pairs of demand for UAVs.

    Road transportOd
    DOI: 10.26599/ETSD.2025.9190008
    CSTR: 32009.11.ETSD.2025.9190008
    Asia, China, Beijing
  • Published on: 2025-03-18

    SAFER-Predictor

    Yutian Liu, Chengfeng Jia, Soora Rasouli, Jian Gong, Tao Feng, Melvin Wong, Tianjin Huang

    This study utilizes datasets from the original baselines, including Los-loop & SZ-taxi (https://github.com/lehaifeng/T-GCN), PEMS-BAY and METR-LA (https://github.com/liyaguang/DCRNN), and PeMSD4 and PeMSD8 (https://github.com/wanhuaiyu/ASTGCN#datasets). The code is available at https://github.com/Chengfeng-Jia/SAFER-Predictor

    Graph neural networkTraffic flow controlRoad transportUrban mobility dynamics
    DOI: 10.26599/ETSD.2025.9190007
    CSTR: 32009.11.ETSD.2025.9190007
    Global
  • Published on: 2025-03-08Updated on: 2025-03-18

    Replication package of the Paper titled a multi-agent social interaction model for autonomous vehicle testing

    Shihan Wang, Ying Ni, Chengsheng Miaob, Jian Sun, Jie Sun

    All the materials source coding and data of the Paper titled a multi-agent social interaction model for autonomous vehicle testing

    Automated vehicleDriver behaviour
    DOI: 10.26599/ETSD.2025.9190005
    CSTR: 32009.11.ETSD.2025.9190005.V2
    Asia, China
  • Published on: 2025-03-18

    Towards robust motion control in multi-source uncertain scenarios by robust policy iteration

    Jie Li

    Replication Package for "Towards robust motion control in multi-source uncertain scenarios by robust policy iteration"

    Autonomous vehiclesQ-learning
    DOI: 10.26599/ETSD.2025.9190006
    CSTR: 32009.11.ETSD.2025.9190006
    Asia, China, Beijing
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Journal
Overview

Communications in Transportation Research

Communications in Transportation Research publishes peer-reviewed high-quality research representing important advances of significance to emerging transport systems. The mission is to provide fair, fast, and expert peer review to authors and insightful theories, impactful advances, and interesting discoveries to readers. We welcome submissions of significant and general topics, of inter-disciplinary nature (transport, civil, control, artificial intelligence, social science, psychological science, medical services, etc.), of complex and inter-related system of systems, of strong evidence of data strength, of visionary analysis and forecasts towards the way forward, and of potentially implementable and utilizable policies/practices. It is indexed in Scopus and DOAJ.

Indexed by international databases