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Browsing by Subject "maritime industry"

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  • Autonomous ships : legal challenges and solutions for data sharing 
    Tarkiainen, Lauri Vilppu Juhani (2019)
    The purpose of this study is to research legal challenges and solutions for data sharing with autonomous ships. Autonomous ships store and share a significant amount of data, and data sharing occurs between various parties with autonomous ships. The aim of this study is to analyze and examine the legal challenges and solutions related to different types of data sharing activities in autonomous shipping, as well as to research the general legality of autonomous ships. The first part of this study is to study how well autonomous ships fit into the existing legislative framework. The existing legislative framework is mainly based on IMO conventions, and the purpose of this study is to research those conventions from the perspective of autonomous ships. Based on this research, amendments are proposed when necessary to better support the legality and development of autonomous ships. In addition to the IMO conventions, other relevant sources, such as guidelines on MASS trials, are examined to highlight guidance on the development of autonomous ships. The legal analysis on IMO conventions and other sources shows that as the level of autonomy of a ship increases, the more challenging the ship is for the legal framework. Several conventions directly mention the need for a master and crew to be physically present on board, and various watchkeeping duties are required to be performed by human senses. Regarding the use of human senses, a legal argument can be made to accept technological means as long as they are at least equally functional than human senses. For remotely controlled ships, a legal question is whether a master and crew can operate from the SCC and how well this satisfies the requirement to operate on board. Recommended action is to amend those IMO conventions that require physical human presence and decision-making to accept the lack of manned crew and the presence of autonomous decision-making. However, technical requirements are recommended to be included in the legal amendments to the conventions to ensure a high level of safety and functionality. The second part of the thesis examines legal challenges and solutions for data sharing with autonomous ships. First, a factual assessment of data sharing principles with autonomous ships are described and discussed, and afterwards a legal analysis is conducted. The legal analysis on data sharing and autonomous ships examines what kind of legal challenges exist with data sharing and autonomous ships and how to solve them by legal solutions. Cyber security is a key challenge with autonomous ships, and its role in data sharing is analyzed and requirements to have robust cyber security systems are recommended. For operational data sharing, the issue of ensuring a functional data flow is necessary. Autonomous ships should be legally required to have strong data sharing and connectivity capabilities in order to comply with requirements to share information. Also, this requirement is to achieve as safe and functional navigation as possible. The role of ship-to-ship and ship-to-port data sharing are examined, and legal requirements should facilitate their maximal utilization. At the end of this study, a contractual framework is applied by using the Sitra Rulebook on data sharing in order to illustrate how contractual means can support data sharing with autonomous ships.
  • Geospatial DBSCAN Hyperparameter Optimization with a Novel Genetic Algorithm Method 
    Steenari, Jussi (2023)
    Ship traffic is a major source of global greenhouse gas emissions, and the pressure on the maritime industry to lower its carbon footprint is constantly growing. One easy way for ships to lower their emissions would be to lower their sailing speed. The global ship traffic has for ages followed a practice called "sail fast, then wait", which means that ships try to reach their destination in the fastest possible time regardless and then wait at an anchorage near the harbor for a mooring place to become available. This method is easy to execute logistically, but it does not optimize the sailing speeds to take into account the emissions. An alternative tactic would be to calculate traffic patterns at the destination and use this information to plan the voyage so that the time at anchorage is minimized. This would allow ships to sail at lower speeds without compromising the total length of the journey. To create a model to schedule arrivals at ports, traffic patterns need to be formed on how ships interact with port infrastructure. However, port infrastructure is not widely available in an easy-to-use form. This makes it difficult to develop models that are capable of predicting traffic patterns. However, ship voyage information is readily available from commercial Automatic Information System (AIS) data. In this thesis, I present a novel implementation, which extracts information on the port infrastructure from AIS data using the DBSCAN clustering algorithm. In addition to clustering the AIS data, the implementation presented in this thesis uses a novel optimization method to search for optimal hyperparameters for the DBSCAN algorithm. The optimization process evaluates possible solutions using cluster validity indices (CVI), which are metrics that represent the goodness of clustering. A comparison with different CVIs is done to narrow down the most effective way to cluster AIS data to find information on port infrastructure.

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