This paper presents the extent of the currently achieved progress in autonomous and remotely controlled ships in the maritime sector. Major researches, statements from relevant sources and various anticipations on this subject are presented to outline a comprehensive scope of such progress. The engine room on conventional merchant ships is used as a viewpoint because it comprises numerous and complex systems. The main purpose of this paper is to establish a link between the levels of autonomy and the engine room with its associated systems on a conventional ship. At each level, the link should describe the relations between autonomy and the systems which are commonly found in the engine room on conventional ships. To create this link, comparison analysis uses the latest statements from the International Maritime Organization (IMO) and Classification Societies. Technical standards for autonomous and offshore vessels are derived from the guidelines provided by Classification Societies. Technical standards and requirements, related to the engine room of such ships, are individually described and compared to provide accurate and comprehensive scope of their current progress.

The efficiency and safety of maritime traffic in a given area can be measured by analyzing traffic density and ship collision probability. Maritime traffic density is the number of ships passing through a given area in a given period of time. It can be measured using vessel tracking systems, such as the Automatic Identification System (AIS). The information provided by AIS is real-time data designed to improve maritime safety. However, the AIS data can also be used for scientific research purposes to improve maritime safety by developing predictive models for collisions in a research area. This article proposes a ship collision probability estimation model based on Monte Carlo simulation (MC) and bidirectional long short-term memory neural network (Bi-LSTM) for the maritime region of Split. The proposed model includes the processing of AIS data, the verification of AIS data, the determination of ports and ship routes, MC and the collision probability, the Bi-LSTM learning process based on MC, the ship collision probability for new or existing routes, and the traffic density. The results of MC, i.e., traffic/vessel route and density, and collision probability for the study area can be used for Bi-LSTM training with the aim of estimating ship collision probability. This article presents the first part of research that includes MC in detail, followed by a preliminary result based on one day of processed AIS data used to simulate MC and propose a model architecture that implements Bi-LSTM for ship collision probability estimation.