Ongoing advancements in collision avoidance systems (CAS) for autonomous ships, built on diverse algorithms, have significantly enhanced performance.These systems, underpinned by a range of sophisticated algorithms, demonstrate increased accuracy in detecting potential collisions, quicker response times, and more reliable path prediction and avoidance maneuvers.Nevertheless, a prevalent trend in these systems is the insufficient consideration of the broader behavioral context of maritime navigation, especially for manned ships.The objective of this research is to present a methodology that can improve the capabilities of CAS in autonomous ships based on target ships' maneuvering behaviors.This methodology will enable autonomous ships to respond adaptively to the actions of target ships, particularly manned ships, while also reacting to immediate environmental stimuli.The ultimate goal is to enhance the safety of navigation.This research utilizes information derived from simulated ship handling training, focusing on feature extraction within steering, spatial, and temporal elements.The tests prioritize the "Average Distance Deviation" metric for designing test scenarios.Simulation-based evaluation tests feature a velocity obstacle algorithm that discerns CAS action in single-ship crossing situations.The research reveals the potential of integrating behavioral dynamics into CAS, which enhances safety in autonomous maritime navigation and fosters a more integrated approach, where autonomous and manned ships coexist synergistically in shared environments.