This replication package supports the manuscript "Driving risk emerges from the required two-dimensional joint evasive acceleration", submitted to Communications in Transportation Research. It provides processed risk-measure data, analysis and plotting code, reference results, validation records and a Replication Explanatory File. Source cohorts comprise 658 reconstructed crashes from CIMSS-TA and 44,180 noncrash interactions from highD, exiD, inD, SinD and Argo2-LCR. Primary warning analyses use the [-3.0, -0.1] s window with zero terminal tolerance; full-history analyses with 0.1 s tolerance are a sensitivity check. The package covers the principal discrimination, warning and information statistics in Figures 3-6 and Tables 1-4. Raw source trajectories and crash recordings are not redistributed. Original dataset access and reuse conditions remain applicable. The README describes the data, software requirements and reproduction steps.

This replication package accompanies the manuscript “Compatibility-Invariance Conflict: a CLF-CBF surrogate safety measure for vehicle trajectories,” submitted to the Communications in Transportation Research under manuscript ID COMMTR-2026-0264. The code accompanying this manuscript is provided with the scripts, environment specification and instructions required to reproduce the reported analyses after the relevant source data are obtained under their original access conditions. SHRP2 reconstructed trajectories are publicly available from the source record at `https://doi.org/10.15787/VTT1/T7UUC1`. The reconstructed 100-Car files and source-data information are available from `https://github.com/Yiru-Jiao/Reconstruct100CarNDSData` and `https://doi.org/10.15787/VTT1/CEU6RB`. CIDAS source data cannot be publicly redistributed; authorized users must obtain the three documented cases through the applicable provider or institutional process.  The data/examples/ directory contains small public-derived fragments for loader validation only and does not​ satisfy the full input contract.

This code-only replication package corresponds to the revised manuscript “Vision Language Model-Guided Semantic Distillation Network for Pedestrian Crossing Intention Prediction” submitted to Communications in Transportation Research (Manuscript ID: COMMTR-2026-0077).

The package contains the implementation of the proposed VLM-GSDN framework, including scripts for generating pedestrian-level and scene-level semantic descriptions using Qwen, InternVL, MiniCPM, and Phi models, as well as training and evaluation workflows for the main model and different VLM-GSDN variants on the JAAD and PIE datasets. It also provides configuration files, environment specifications, and supporting documentation. A Replication Explanatory File describes the package structure, data preparation, model training, and evaluation procedures.

This package contains the numerical propagated interaction-field model, context-conditioned physics-informed neural network (PINN), field-aware reinforcement-learning pipeline, and processed measurements supporting the principal validation and simulator findings. 

The statistical workflow preserves the original observation/reward factorial and replaces only its complete-model merge candidate with the selected 0.25-times comfort profile. All five calibrated merging Soft Actor-Critic checkpoints and their five paired stock checkpoints are included. Policy-training seeds are 0-4; each policy is evaluated on traffic seeds 10000-10049 at density 0.3. The calibrated profile uses jerk weight 0.000375 and steering/throttle-change weights 0.00025. An indexed mapping connects each checkpoint to its effective training settings and episode CSV. 

The PINN checkpoint is accompanied by its available architecture, input channels, normalization, numerical-solver settings, loss weights, calibration-cache signatures, hardware record, training duration and 6,000-step loss history. 

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.