# The Branching-Course MPC Algorithm for Maritime Collision Avoidance

arXiv: https://arxiv.org/abs/1907.00039 · HTML: https://mlsanity.com/paper/1907.00039/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea](https://mlsanity.com/paper/2402.08502/) (2024)
- [Distributional Reinforcement Learning based Integrated Decision Making and Control for Autonomous Surface Vehicles](https://mlsanity.com/paper/2412.09466/) (2024)
- [On the Verification and Validation of AI Navigation Algorithms](https://mlsanity.com/paper/2101.06091/) (2021)

## Abstract (arXiv)

This article presents a new algorithm for short-term maritime collision avoidance (COLAV) named the branching-course MPC (BC-MPC) algorithm. The algorithm is designed to be robust with respect to noise on obstacle estimates, which is a significant source of disturbance when using exteroceptive sensors such as e.g. radars for obstacle detection and tracking. Exteroceptive sensors do not require vessel-to-vessel communication, which enables COLAV toward vessels not equipped with e.g. automatic identification system (AIS) transponders, in addition to increasing the robustness with respect to faulty information which may be provided by other vessels. The BC-MPC algorithm is compliant with rules 8 and 17 of the International Regulations for Preventing Collisions at Sea (COLREGs), and favors maneuvers following rules 13-15. This results in a COLREGs-aware algorithm which can ignore rules 13-15 when necessary. The algorithm is experimentally validated in several full-scale experiments in the Trondheimsfjord in 2017 using a radar-based system for obstacle detection and tracking. The COLAV experiments show good performance in compliance with the desired algorithm behavior.