Trajectory tracking control of unmanned surface vessels (USVs) has become a popular topic. Regarding the problem of ship collision avoidance encountered in trajectory tracking, more attention needs to be paid to the algorithm application, namely the characteristics of flexibility and accessibility. Thus, a fusion framework of field theoretical planning and a model predictive control (MPC) algorithm is proposed in this paper to obtain a realizable collision-free tracking trajectory, where the trajectory smoothness and collision avoidance constraints under a complex environment need to be considered. Through the designed fast matching (FM) method based on the electric field model, the algorithm gains the direction trend of collision avoidance planning and then combines it with a flexible distance to reconstruct the architecture of the MPC and constraint system, generating the optimal trajectory tracking controller. The new algorithm was tested and validated for several situations, and it can potentially be developed to advance collision-free trajectory tracking navigation in multivessel situations.

Driven by the commercialization of intelligent ships, the increasingly complex mixed maritime traffic environment presents significant challenges for collision avoidance between multiple ships due to cognitive and behavioral differences between intelligent and traditional ships. Therefore, it is essential to develop a human-like collision avoidance strategy that incorporates traditional navigational experience and handling practices, enhancing explainability and autonomy. By addressing the actual decision-making needs for predicting other ships’ intentions and considering potential risk impacts, a hierarchical strategy is designed that first seeks course direction adjustment and then determines the magnitude of adjustment. A direction adjustment intention estimation model is proposed, accounting for risk membership and COLREGS, to predict other ships’ collision avoidance intentions. Additionally, an intention influence model and a state influence model are introduced to design decision-making objectives, forming an optimization function based on angle range and maneuvering time constraints to determine the appropriate adjustment magnitude. The results demonstrate the strategy’s effectiveness across various scenarios. Specifically, the distance between ships increased by nearly 25% during the process, significantly enhancing safety. It is worth mentioning that the model has the potential to enhance intelligent ships’ capabilities in complex situational handling and intention understanding.

The advent of remote control and autonomous technologies is fundamentally transforming ship manning models, necessitating adaptations in seafarer standards for L2-class autonomous vessels. To address these challenges, the International Maritime Organization’s (IMO) Maritime Safety Committee (MSC) is developing a non-mandatory Code for Maritime Autonomous Surface Ships (MASS). This paper first conducts an in-depth analysis of the immutable legal obligations and variable technical standards governing seafarer manning for L2 autonomous ships, drawing on the China Classification Society’s (CCS) Rules for Intelligent Ships. It then scrutinizes the international legal barriers confronting CCS manning requirements, particularly concerning remote operator qualifications. The study focuses on aligning China’s regulatory framework with emerging IMO standards and proposes four targeted reforms to facilitate this alignment for L2 vessels: broadening the “seafarer” definition, updating minimum manning principles and procedures, modernizing competency standards, and establishing inclusive operational testing systems.