Risk-informed collision avoidance system design for maritime autonomous surface ships

The maritime industry is paving the way towards developing Maritime Autonomous Surface Ships (MASS) with the adoption of key enabling technologies for safety-critical operations, such as the collision avoidance system (CAS), which are associated with new challenges, especially at early design phase. This study aims at developing a methodology to conducts risk analysis of CAS to support its risk-informed design. Pertinent regulatory instruments are reviewed to identify functional and system requirements, which are elaborated to derive a baseline CAS configuration at components level. Quantitative Fault Tree Analysis is performed and appropriate risk metrics, such as probability of failure, importance measures, and minimal cut sets, are assessed to identify design improvements. A short sea shipping case study is investigated considering four modes with varying weather and illumination conditions. Results demonstrate that the mode operating in adverse weather and daylight illumination condition exhibit the highest probability of CAS failure, whereas the most critical components are communication, situation awareness, and artificial intelligence software related systems. The baseline CAS reconfiguration leads to 89.3% reduction of the probability of failure. This study can provide reference for various risk-informed decision-making pertaining to the design of safer CAS for the next generation MASS.