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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Ego vehicle trajectory prediction based on driver-vehicle coupling characteristics in diverse urban scenarios
This software package supports the reproduction of key numerical results reported in the study, including scripts that use driver-vehicle coupling characteristics as input to predict the ego-vehicle’s trajectory in diverse urban scenarios, as well as script for evaluating the performance of the predicted trajectories.
The package includes processed data & normalization parameters, ego vehicle trajectory prediction deep learning training models (DriVETP, DriVETP-Ablation1, DriVETP-Ablation2, AM-LSTM, Transformer), visulaization results from multi-scenario testing, performance metrics (RMSE, ADE, FDE) radar plots, and model generalization validation.
Test scenarios cover typical urban driving conditions: left lane changing, right lane changing, roundabout navigation, left turning, right turning, and straight driving. Detailed instructions for reproducing the main results are provided in the README file.
Asia, China, Changchun Towards the Intelligence Evaluation of High-level Autonomous Vehicles: A Subjective-Objective Mapping Method via LLM
This repository provides a lightweight reproduction of an autonomous-driving intelligence evaluation workflow. It evaluates vehicle interactions from five dimensions, combines ten normalized objective metrics with LLM-based subjective scores, and trains an attention-based neural network to approximate the final intelligence score.
The repository includes 705 indexed Waymo interaction samples and a lightweight Waymo/trajdata cache. It can therefore reproduce the main workflow without downloading the complete Waymo or InterHub dataset.
Africa, China North America, United States MapKD: Unlocking Prior Knowledge with Multi-Level Cross - Modal Distillation for Map-aware Driving Perception
Replication package access: https://github.com/2004yan/MapKD_new.
Data description: We adopt the publicly available nuScenes as our dataset.
Code description: torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1
Simulation software description: Configuring Python 3.8 on Ubuntu 20.04
Experiment design description: In the first stage, the teacher and coach model is jointly pretrained.In the second stage, the student model is trained from scratch under the proposed Teacher-Coach-Student pipeline. The teacher and the coach are kept frozen, while providing intermediate guidance at the feature and output level. The student is supervised with both hard labels and soft logits. We train our model on the nuScenes dataset , using a BEV range of 60 m × 30 m. The voxel resolution is set to 0.15 m. Input images are resized to 128 × 352. We train for 30 epochs with a batch size of 8, using 4 GPUs and 20 workers.We use the Adam optimizer with a learning rate of , weight decay of , and gradient clipping at 5.0.
North America, American Samoa Autonomous vehicle decision-making and safety evaluation based on physics-aware graphs and hybrid policy optimization
This replication package is associated with the manuscript “Autonomous vehicle decision-making and safety evaluation based on physics-aware graphs and hybrid policy optimization” submitted to the Journal of Intelligent and Connected Vehicles under manuscript ID JICV-2026-0020. The package includes source code, configuration files, simulation scripts, processed sample data, training logs, evaluation results, generated tables, figure-generation scripts, and a Replication Explanatory File for Journal-Associated Data. These materials support reproduction of the main experiments, including online reinforcement learning in highway-v0, comparisons among PPO-MLP, Ladm-PPO, and Ladm-HPO, offline safety evaluation using LadmCritic, TTC correlation analysis, and risk-energy field visualization. External datasets are not redistributed if restricted by their original data-use policies; instead, source information and preprocessing instructions are provided.
Asia, China, Changchun MILD: Mediator Agentic System with Bidirectional Perception and Multi-Layered Alignment for Human-Vehicle Collaboration
The code and data for MILD. To access the original video of AIDE, DMD, and DriveLM, please refer to their source papers.
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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