This data repository is related to the journal paper entitled "Reinforcement Learning Agent Embedded in Roadside Units to Implement an Adaptive Variable Speed Limit System in a Cooperative Intelligent Transportation Systems Environment", authored by Eléonore Fauchet (first author), Pierre-Antoine Laharotte, and Nour-Eddin El Faouzi.

The study uses no field, proprietary or personal data. All data are synthetic and are generated by the microscopic traffic simulations described in the paper (SUMO). The package, therefore, contains the complete data, together with the scripts that regenerate them. The files provided are:

– SUMO scenario files (XML); 

– Simulated datasets (CSV);

– Trained models (Keras/HDF5, .h5);

– Simulation outputs (CSV/XML).

Because the trained weights and datasets are included, the evaluation results (Figs. 5–8) can be reproduced without re-running the training stages; the training stages can also be re-run from scratch with the scripts.

This replication package accompanies the manuscript entitled "Vehicle Platooning and Signal Coordination for Intelligent Connected Intersections Toward Autonomous Transportation Systems," submitted to the Journal of Intelligent and Connected Vehicles (JICV). It provides SUMO-based simulation code, experimental configurations, processed traffic-demand data, prediction assets, and analysis scripts for reproducing the reported results. The code implements a rolling coordination framework integrating vehicle platooning, short-term traffic demand prediction, and signal timing optimization at an isolated intelligent connected intersection. The package supports comparative experiments under three traffic demand levels (1.0, 1.2, and 1.5) and includes the corresponding experimental results. The README provides software requirements, environment setup instructions, experimental configurations, execution commands, and procedures for regenerating the performance metrics, tables, and figures reported in the manuscript, including system delay, queue accumulation, throughput, and traffic clearance.

Replication materials

This repository provides the VTF-Flow replication package, including source code, experimental configurations, preprocessing, training and evaluation scripts, and reference results. The study uses the publicly available RELLIS-3D dataset, which can be obtained separately from the original provider.

Replication package associated with the manuscript “Towards Lawful Autonomous Driving: Deriving Scenario-Aware Driving Requirements from Traffic Laws” (Communications in Transportation Research, Manuscript ID: COMMTR-2026-0168.R3). The package includes the law corpus, source code, and README document to reproduce the numerical experiments.

ScenePilot-4K is a large-scale first-person driving dataset and benchmark for vision-language models. It contains 3,847 hours of video and 27.7 million front-view frames spanning 63 countries and regions and 1,210 cities. ScenePilot-Bench evaluates scene understanding, spatial perception, motion planning, and GPT-based semantic alignment.

This replication package contains the public code, README.md, and an explanatory file. Annotations, VQA datasets and evaluation subsets, video-level train/validation/test splits, source-video indices, and model checkpoints are available on Hugging Face. Download locations and usage instructions are provided in README.md. Original videos and extracted frames are not redistributed.

All materials related to this study have been made available through ETS Data and an external public data repository for transparency and reproducibility purposes. The ETS Data replication package contains the main output results reported in the manuscript, the source code, and the corresponding computational environment configuration files. Due to its large size, the complete raw experimental dataset is hosted separately on Hugging Face and is publicly accessible at: https://huggingface.co/datasets/InterestingITS/smartphone_in_vehicle_multisensor

The replication package provides the materials associated with the study, including simulation input data, processed numerical data for figure reproduction, the core EPSM control implementation, plotting scripts, and the corresponding SUMO simulation files. The accompanying explanatory document describes the organization and purpose of the released data, code, and simulation materials.

Data and replication package for the article "Assessing Microscopic Traffic Simulation Confidence Level Through Expert-Weighted AHP and Belief Theory: An Application to CAV Testing"

This paper presents an end-to-end approach for deploying and evaluating pedestrian crossing intention prediction on an on-board embedded computing platform. The proposed pipeline integrates YOLO11s for pedestrian detection, DeepSORT for identity tracking, RTMPose-s for pose estimation, an identity-aware temporal buffer, and PedAST-GCN for crossing intention prediction. We optimize the pipeline using ONNX Runtime and TensorRT and deploy it on an NVIDIA Jetson AGX Orin installed in KTH’s Research Concept Vehicle E. In addition, we introduce an offline LLM-based evaluation framework that automatically annotates pedestrian crossing states from real-world tracking videos and evaluates whether the system can anticipate crossing behavior one to two seconds in advance. Experiments conducted at the Gillinge test track demonstrate reliable prediction performance and real-time inference at approximately 16.6 FPS. This replication package provides the source code and documentation required to reproduce the deployment, optimization, and evaluation procedures reported in the paper.

This project develops an offline reinforcement learning framework for optimizing speed guidance of connected autonomous vehicles (CAVs) at signalized intersections under mixed traffic conditions. The framework learns safe and efficient policies from real-world trajectory data without requiring risky online interactions. The system employs Critic Regularized Regression (CRR) combined with real-world driving trajectories and Signal Phase and Timing (SPaT) information from the UCF-SST-CitySim dataset. This approach bridges the sim-to-real gap by learning directly from naturalistic human driving behavior while incorporating safety-critical features often neglected in prior work. 

Replication package for STAR: Structure-Aware Representation Learning for Efficient and Robust UAV-based Geo-Localization

This replication package includes the source codes of the pipeline, source data and results data, demo video, and the complementary material package (mentioned in the paper).  

Model and experimental data for “Prior knowledge-assisted reinforcement learning for vehicle platoon control in cut-in scenarios.” The co-simulation model uses TruckSim and MATLAB/Simulink, and the shared data support the main experimental results presented in the paper.

The dataset is obtained from miniature-vehicle experiments at intersections. It contains the experimental results used to evaluate multi-vehicle cooperation under fully connected and mixed-traffic conditions, including vehicle motion data and the main performance indicators reported in the manuscript.

This replication package contains code necessary to reproduce the results reported in the manuscript "Infrastructure-Assisted Cooperative Decision Model With Priority Awareness at Unsignalized Intersections" (COMMTR-2026-0005).

Hardware: Intel Core i7-11700F processor and an NVIDIA GeForce RTX 3060 Ti 

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.

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.

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.