The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/776e311c-9ccf-4fab-886d-c1886ff15fcf

Jansma, Walter; Trevisan, Elia; Serra-Gómez, Álvaro; Alonso-Mora, Javier (2024): Trained Model and Data for Trajectory Predictions of Autonomous Surface Vessels in Urban Canals. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/776e311c-9ccf-4fab-886d-c1886ff15fcf.v1

Dataset

• Water Transport
• Transport

Autonomous robots, Autonomous Vehicles, Multi-agent systems, Navigation, Planning, Predictive models, Trajectories, Vessels

CC0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Walter Jansma , Elia Trevisan , Álvaro Serra-Gómez , Javier Alonso-Mora

This dataset contains artificial data consisting of trajectories of multiple vessels navigating in to-scale canal maps of Amsterdam and a trained prediction model on this data. This dataset is related to the IEEE 2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) proceedings titled "Interaction-Aware Sampling-Based MPC with Learned Local Goal Prediction".

• 2024-11-21 first online, published, posted

4TU.ResearchData

ZIP folders

Interaction-Aware Sampling-Based MPC with Learned Local Goal Prediction

• https://autonomousrobots.nl/paper_websites/ia-mppi

• TRiLOGy Sustainable Transportation and Logistics Over Water: Electrification, Automation, and Optimization (grant code P80430 ) [more info...] NWO

TU Delft, Faculty of Mechanical Engineering, Department of Cognitive Robotics