AI-Based Behavioral Competency Assessment Tool to Enhance Navigational Safety

Abstract

The human element has been cited as the main contributory factor for various maritime navigational incidents. Measuring and assessing human performance is key to enhance and maintain maritime navigational safety. In order to address the above problem, there is a need to develop an efficient measurement and assessment tool for navigational competencies for various behaviors such as situational awareness, decision making, teamwork, and communication and influencing skills. Multiple forms of data is collected in a Navigation Bridge Simulator in a Bridge Team Scenario to conduct this research. The topic is especially relevant to improve the safety of maritime navigation in crowded waters such as the Singapore Strait. An effective AI-based competency assessment tool for safe navigation (AICATSAN) is developed to measure and assess navigational competence. Hybrid deep learning methods using eye trackers are employed to develop AICATSAN, which was assessed to produce accurate results with preliminary test data sample. Convolutional neural network (CNN) and long short-term memory (LSTM) networks are used for multi-modal, multi-feature and multisensory for recognition of complex and concurrent activity details. The AICATSAN is able to quickly analyze the human performance of a given navigational scenario. As an indicative assessment system to evaluate behavioral competence through the use of navigation simulator, this AICATSAN system will observe key indicators to analyze human behavioral competencies and facilitate the evaluation of navigational competence. It has the potential to be used for training, recruitment or promotion to the higher ranks.