{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/active-learning-augmented-intention-aware","title":"Active Learning-augmented Intention-aware Obstacle Avoidance of Autonomous Surface Vehicles in High-traffic Waters","arxiv_id":"2411.01011","date":"2024-11-01","proceeding":null,"authors":["Mingi Jeong","Arihant Chadda","Alberto Quattrini Li"],"abstract":"This paper enhances the obstacle avoidance of Autonomous Surface Vehicles (ASVs) for safe navigation in high-traffic waters with an active state estimation of obstacle's passing intention and reducing its uncertainty. We introduce a topological modeling of passing intention of obstacles, which can be applied to varying encounter situations based on the inherent embedding of topological concepts in COLREGs. With a Long Short-Term Memory (LSTM) neural network, we classify the passing intention of obstacles. Then, for determining the ASV maneuver, we propose a multi-objective optimization framework including information gain about the passing obstacle intention and safety. We validate the proposed approach under extensive Monte Carlo simulations (2,400 runs) with a varying number of obstacles, dynamic properties, encounter situations, and different behavioral patterns of obstacles (cooperative, non-cooperative). We also present the results from a real marine accident case study as well as real-world experiments of a real ASV with environmental disturbances, showing successful collision avoidance with our strategy in real-time.","url_abs":"https://arxiv.org/abs/2411.01011v1","url_pdf":"https://arxiv.org/pdf/2411.01011v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"active-learning-augmented-intention-aware","repo_url":"https://github.com/dartmouthrobotics/passing_intention_lstm","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}