Brian James Murray

Title of the thesis:

«Machine Learning for Enhanced Maritime Situation Awareness: Leveraging Historical AIS Data for Ship Trajectory Prediction»

Popular scientific abstract:

The main objective of this study is to develop methods that can improve the safety of maritime transportation through enhanced maritime situation awareness. Ship navigators likely leverage their situation awareness to conduct mental simulations of future ship traffic that facilitate proactive collision avoidance actions. Such actions may include minor speed and/or heading alterations that can prevent future close-encounter situations from arising. However, such actions must adhere to relevant rules and regulations. As a result, methods to facilitate long-range trajectory predictions, up to 30 minutes into the future, are developed and evaluated to support the same actions in this study. These methods are facilitated by leveraging machine learning to emulate human situation awareness, where navigational experience is artificially represented by historical Automatic Identification System data. The methods can be used to aid ship navigators as well as future autonomous ships.

The thesis is published in Munin and is available at: https://munin.uit.no/handle/10037/20984

Supervisors:

• Associate Professor Lokukaluge Prasad Perera, Department of Technology and Safety, UiT (main supervisor) 
• Professor Egil Pedersen, Department of Technology and Safety, UiT
• Associate Professor Henrique Gaspar, Department of Ocean Operations and Civil Engineering, NTNU

Evaluation committee:

• Associate Professor Ângelo Manuel Palos Teixeira, Instituto Superior Técnico (IST), University of Lisbon
• Professor Houxiang Zhang, Department of Ocean Operations and Civil Engineering, NTNU
• Associate Professor Bjørn-Morten Batalden, Department of Technology and Safety, UiT (internal member and leader of the committee)

Leader of the public defense:
The leader of the public defense is Professor Camilla Brekke, Vice-Dean Research, Faculty of Science and Technology, UiT.

Opposition ex auditorio:
If you have any questions for the candidate during the public defence, please send an e-mail to the leader of the public defence. They will announce the questions during the defence.

Trial lecture:

The trial lecture is held Monday May 3rd at 10:15.

Title of the trial lecture: «Applying AIS data to maritime transportation: Applications, challenges and opportunities»

Streaming:

The defense and trial lecture will be streamed via Mediasite: 

https://mediasite.uit.no/Mediasite/Catalog/Full/4c0cabfd40b74952abe69e38d2a12a2721

Audience:

All audience are to use mediasite to view the defence.