Autonomous vessel collision avoidance verification using geometric optimization

Abstract

The rapid advancement of Maritime Autonomous Surface Ships (MASS) has highlighted the necessity of revisiting the International Regulations for Preventing Collisions at Sea (COLREG), as the current framework does not fully account for the operational and decision-making challenges introduced by autonomous navigation. This paper extends our previous work on maritime safety by explicitly incorporating relative speed variations between vessels, whereas the earlier study was limited to encounter scenarios involving ships with identical speeds. When the analysis assumptions were relaxed, it was necessary to adapt the calculation of other navigation-related parameters, such as the collision angle, to evaluate how speed differences influence the collision-risk geometry. As in our earlier approach, the derived analytical constraints are used to reduce the constraint space explored by Constraint Logic Programming (CLP) to uncover potential gaps and ambiguities in COLREG compliance for MASS. We also refine our geometric optimization method further to restrict the search space in standard navigation situations. By focusing on two-dimensional spatial parameters within a bounded surface domain, the extended approach preserves near-polynomial search complexity while enabling a more realistic and comprehensive analysis of vessel encounter situations safety. According to the performance analysis, the identification of adversary vessel positions that lead to close encounter prediction requires less than a half second, which is sufficient to be deployed onboard navigation warning system.