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.

The code and data for MILD. To access the original video of AIDE, DMD, and DriveLM, please refer to their source papers.

Ship navigation is strongly influenced by the ocean environment. Adverse sea environment not only increases fuel consumption but can also endanger navigation safety. For the challenge, a ship weather routing framework based on Deep Reinforcement Learning (DRL) is proposed.

Due to limitations on the amount of data that can be uploaded, the replication package only contains the key code.

The complete key code and data will be attached as additional attachments.

In Part I of this companion paper series, we introduced SWIFTraj, an open-source vehicle trajectory dataset collected by a UAV swarm. It provides long-distance continuous trajectories by connecting vehicle trajectories across consecutive UAV videos, with the longest trajectory exceeding 4.5 km, and covers an integrated network of freeways and connected urban roads. However, trajectory connection in UAV swarms is challenging because of video time-offset errors and irregular UAV layouts. To address these issues, this paper proposes a graph-based trajectory connection method. An undirected graph is used to represent flexible UAV layouts, an automatic time-alignment method is developed by minimizing trajectory matching costs, and cross-video vehicle association is performed using a Hungarian-algorithm-based matching table. Experiments on real-world and simulated data show that the proposed method achieves time alignment errors within three frames, about 0.1 s, and consistently high vehicle-matching F1-scores.

This paper presents a detailed description and characterization of a new open-source vehicle trajectory dataset, namely SWIFTraj, constructed from videos recorded by a swarm of 16 drones equipped with 5.4K-resolution cameras. The dataset is distinguished from existing open-source trajectory datasets in several aspects. First, it provides long-distance continuous trajectories of up to 4.5 km on a freeway, enabling in-depth investigation of traffic phenomena and their spatial and temporal evolution. Second, the data collection site covers an integrated network consisting of a long freeway corridor and parts of its connected urban network, facilitating traffic analysis and modeling from a network perspective. The dataset is publicly available at the SWIFTraj website (https://www.swiftraj.com).