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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Replication package of the Paper titled a multi-agent social interaction model for autonomous vehicle testing
All the materials source coding and data of the Paper titled a multi-agent social interaction model for autonomous vehicle testing
Asia, China Safety Assurance Adaptive Control for Modular Autonomous Vehicles
This study proposes a Safety Assurance Adaptive Model Predictive Control (SAAMPC) framework to achieve distributed docking/undocking operations for MAVs in uncertain environments. The SAAMPC framework integrates a Model Predictive Control (MPC) controller for trajectory optimization, an adaptive module for dynamic adjustment of control parameters with disturbance, and an adaptive safety assurance module with longitudinal and lateral Control Barrier Functions to ensure safe operation during risky and uncertain conditions. The effectiveness of the proposed approach is validated through simulations in Simulink and field tests on a reduced-scale MAV platform. Experimental results validate that the SAAMPC framework successfully ensures smooth and safe vehicle following and robust execution of docking/undocking operations under uncertainties.
North America, United States, MADISON ProChunkFormer
Replication Package for "Enhanced trajectory reconstruction from sparse and noisy GPS data: A progressive chunked transformer approach"
Europe, Portugal, Porto Replication package of the Paper titled a trajectory planning and tracking method based on deep hierarchical reinforcement learning
All the materials source coding and results coding of the Paper titled a trajectory planning and tracking method based on deep hierarchical reinforcement learning
Global Replication package of Enhancing Driver Emotion Recognition Through Deep Ensemble Classification.
All the materials source coding and results coding of Enhancing Driver Emotion Recognition Through Deep Ensemble Classification.
Asia, China 
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.
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