About the project
How can autonomous ships recognise when changing traffic, environmental or system conditions make continued autonomous operation unsafe? This project will develop dynamic risk intelligence methods to assess operational conditions against autonomous ship capability boundaries, identify evolving risk states, and support explainable decisions on continued operation, degradation, fallback and contingency.
Maritime autonomous surface ships must operate safely in environments that change continuously. Traffic density, weather, visibility, communication availability, sensor performance and autonomous system capability can all affect whether an autonomous ship or individual autonomous function remains able to operate safely. A key challenge is therefore to determine when autonomous operation can continue and when intervention, degradation or fallback becomes necessary.
This project will develop dynamic risk intelligence methods for safe autonomous shipping. The research will investigate how Operational Envelopes (OEs) and Operational Design Domains (ODDs) can be represented as capability boundaries for autonomous ships and their functions, and how changing operational conditions can be assessed against these boundaries.
The project will develop methods for modelling the relationships between operational conditions, autonomous functions, vessel interactions and navigational risk. Particular attention will be given to uncertainty and dynamic risk evolution, including transitions between normal, degraded, fallback-required and contingency-required operating states. Potential approaches include probabilistic risk modelling, Bayesian Networks, Dynamic Bayesian Networks, system-theoretic safety analysis and data-driven methods.
The research will also investigate how dynamic risk assessment can be translated into explainable operational information to support decisions such as continued autonomous operation, increased safety margins, function degradation, remote supervision, mode switching and fallback. The proposed methods will be evaluated using representative autonomous navigation scenarios, historical maritime traffic data and simulation-generated scenarios. The research will contribute to risk-aware autonomous navigation and provide methods supporting the safety assurance and future deployment of Maritime Autonomous Surface Ships.
The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
