STPredictor is an explainable ship trajectory prediction framework powered by large language models. It reformulates trajectory prediction as a language modeling task using natural-language prompts and integrates Chain-of-Thought reasoning for more transparent and reliable predictions. Experiments on two large-scale AIS datasets show strong performance and improved interpretability over existing baselines.

This replication package provides the complete source code and configuration files used in the study “CogDrive: Cognition-Driven Multimodal Prediction-Planning Fusion for Safe Autonomy.”

The package enables full reproduction of the proposed cognition-driven multimodal trajectory prediction and safety-aware planning framework, including model architecture implementation, training procedures, evaluation scripts, and planning modules.

The provided code supports both open-loop prediction evaluation and closed-loop planning simulations as reported in the manuscript. All scripts are organized to facilitate reproducibility of the main experimental results on public autonomous driving benchmarks.

This paper identifies, analyzes, and addresses case-level forgetting in continual learning for trajectory prediction. We propose the Dynamically Expandable Interactive Trajectory Predictor (DEITP), a novel framework that preserves previously learned knowledge through a dynamic model expansion mechanism. The mechanism regulates expansion timing by assessing model similarity, thereby controlling model growth while preventing catastrophic forgetting. Furthermore, to operate in realistic task-free settings where task identity is unavailable at test time, we introduce a task identification strategy based on a familiarity autoencoder that selects the most appropriate expert for prediction. Extensive experiments on real-world datasets demonstrate that DEITP substantially mitigates forgetting and achieves zero-forgetting performance when task identities are known.

The entire project package is uploaded. Simply unzip it and run the train and test functions separately; the data have already been preprocessed.

Two standard datasets on travel mode choice are used, namely LPMC and Swissmetro (SM). The LPMC dataset consists of single-day travel diary data obtained by the London Travel Demand Survey from 2012 to 2015. The dataset includes 81,096 samples, each of which corresponds to a trip taken by a person in one of the 17,616 households participating in the survey. The dataset contains four travel modes, namely walking, cycling, public transportation (PT), and car. Moreover, the SM dataset comprises passenger survey data gathered in Switzerland. It includes samples from 1291 passengers across nine travel scenarios designed with three kinds of modes, i.e., train, SM, and car. In the materials, we make separate divisions based on the datasets. Among them, the "data" file contains the original data, the processed data, and the data processing code. The configuration details are described in the "conf" folder. The analysis code is included in the "utilities" file. Then, the codes for the centralized, federated learning framework and the federated meta-learning framework are also attached.