Experimental trust dynamics modelling in supervised autonomous ship navigation in collision avoidance scenarios

Abstract

Maritime Autonomous Surface Ships (MASS) are advancing the shipping industry, requiring a mixed waterborne transport system (MWTS) where human supervision provides a supporting role for maintaining safety and efficiency, particularly in complex scenarios. This study explores the dynamics of seafarers’ trust in MASS during collision avoidance (CA) scenarios involving a vessel approaching from the starboard side. An empirical study with 26 participants representing diverse maritime experience levels examined how time, demographic factors, and collision avoidance strategies influence trust. Using a linear mixed model (LMM), trust was found to fluctuate across navigation stages: gradual accumulation during the routine navigation stage, sharp dissipation during strategy determination and execution stages, and partial recovery at the final stage. Strategies aligned with maritime regulations and appropriately timed evasive actions fostered higher trust, while overly early or imminent actions reduced trust. Additionally, a factor analysis consolidated the five trust dimensions, including dependability, predictability, anthropomorphism, faith, and safety, into two aspects: System Competence, encompassing the first four dimensions, and Situational Safety, representing safety-related trust. Furthermore, Bayesian Network (BN) is developed to model trust in the autonomous decision-making of MASS, integrating human observers demographics and situational factors. The model captures sequential trust dependencies, revealing the cascading effects of trust across various stages and the role of System Competence in shaping overall trust in the entire decision-making process. These findings provide actionable insights for designing MASS that support trust-building and optimise collision avoidance strategies, contributing to safer and more efficient autonomous maritime operations.