Philipp Last

• The Automatic Identification System (AIS) has been globally introduced to increase the maritime safety. When introducing AIS two very high frequency (VHF) channels have been reserved worldwide allowing maritime entities to transmit individual data. The automatically exchanged data provided by shipborne AIS may include the speed of a vessel, its course, heading, rate of turn or its Global Positioning System (GPS) position. Modern bridge devices are connected to AIS allowing to decode and visually represent received AIS data. Due to the AIS data density, variety and the low effort required to obtain AIS data they have become a research object within the past years. Because of the meaning of AIS data for the maritime field and related research this thesis evaluates AIS data for maritime safety. Hence, a comprehensive analysis of AIS data is part of this work. This analysis gives an essential overview about how shipborne AIS systems are currently configured and used. AIS data attributes relevant for vessel movement prediction and their availability are evaluated and discussed. Furthermore, the AIS reporting intervals are evaluated in detail. With respect to the graphical representation of AIS data on radar systems so far not visually encoded AIS data attributes have been identified in this thesis. An expert group gave feedback about the relevance of these attributes with respect to maritime safety. In addition, visual encodings for these attributes are proposed which have also been judged by the expert group. Since human error has been identified as the main maritime incident reason a full mission bridge simulator has been used for an integration of live AIS data within this thesis. Moreover, an interactive history-based vessel movement prediction algorithm is proposed within this thesis. The prediction outcome including uncertainty is visualized to the user in a way that mariners are supported in performing collision avoidance.