This replication package accompanies the manuscript “CF-Drive: Candidate-conditioned interaction risk estimation for retrieval-augmented autonomous driving,” submitted to the Journal of Intelligent and Connected Vehicles under manuscript ID JICV-2026-0117. The package contains the source code, experimental configurations, log-disjoint fold definitions, processed candidate-level results, evaluation and plotting scripts, and a Replication Explanatory File for reproducing the principal risk-estimation, matched-shuffle, and utility-selection results. The original NAVSIM data are not redistributed. Users should obtain the source data from the official NAVSIM repository and comply with its applicable terms of use.

This replication package accompanies the manuscript "Hierarchical Neural-Scope Detector for Silent Fault Detection in Vision-Language-Action-Based Autonomous Driving," submitted to Communications in Transportation Research.

The package contains experimental code, processed data, detector models, feature specifications, evaluation configurations, sample-level predictions, and supporting results. The materials are organized into five modules covering OpenDriveVLA detector development, UniAD2 cross-architecture validation, independent validation, component ablation and threshold-sensitivity analysis, and runtime overhead evaluation.

These materials correspond to the experiments and results reported in the main manuscript, appendices, and supplementary information. The package includes README files and a replication guide describing the data, code, and procedures for reproducing the reported results.

The materials are also available at https://github.com/xlqy-114/Hierarchical-Neural-Scope-Detector.

OVPD is a virtual-physical fusion testing dataset derived from the 2025 OnSite Autonomous Driving Challenge. It contains 20 complete clip-level test runs from 20 teams, covering nearly 3 hours of driving data and 15 atomic scenarios for emergency response, traffic efficiency, and rule compliance.

This package includes structured data synchronized at 10 Hz: ego-vehicle trajectories and motion states, control commands, background traffic trajectories and states, traffic-light states, OpenDRIVE HD maps, and evaluation results for safety, efficiency, comfort, rule compliance, and traffic coordination. It supports replay-based analysis, planning and decision-making research, failure diagnosis, and capability-oriented benchmarking.

Due to the platform’s 3 GB upload limit, image data are not included here. The complete OVPD dataset, including synchronized image data, is available at: https://huggingface.co/datasets/Yuhang253820/Onsite_OVPD

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.