Abstract: The present invention relates to methods, computer program products and computing devices for calibrating a Digital Twin for an autonomous vehicle using machine learning and to the use of the calibrated Digital Twin to both tune at least one controller, navigation algorithms and/or guidance algorithms for an autonomous vehicle using machine learning and to optimising a vehicle shape of an autonomous vehicle using machine learning.

Abstract: A diagnostic tools and methods are disclosed. The diagnostic tool is configured or operable to nominate a commonly connected component in a target system as being faulty if more than a pre-determined number of active components connected to the commonly connected component appear faulty.

Abstract: A diagnostics system comprising a topological map of a target system that has nodes (38, 40, 42, 44, 46, 48) that correspond to components (29, 30, 32, 34, 36) of the target system and links that correspond to connections between the components. Associated with the topological map is a knowledge store (50) that has a structure that reflects or corresponds to that of the topological map. Included in this store (50) is a plurality of sections or libraries each of which is provided for storing design specific data associated with one of the nodes (38, 40, 42, 44, 46, 48) of the topological map. Data received from one or more sensors on the target system is included in the topological map, and used together with the design specific information in the knowledge store to diagnose faults.

Abstract: A diagnostic tools and methods are disclosed. The diagnostic tool is configured or operable to nominate a commonly connected component in a target system as being faulty if more than a pre-determined number of active components connected to the commonly connected component appear faulty.

Abstract: A diagnostics system comprising a topological map of a target system that has nodes (38, 40, 42, 44, 46, 48) that correspond to components (29, 30, 32, 34, 36) of the target system and links that correspond to connections between the components. Associated with the topological map is a knowledge store (50) that has a structure that reflects or corresponds to that of the topological map. Included in this store (50) is a plurality of sections or libraries each of which is provided for storing design specific data associated with one of the nodes (38, 40, 42, 44, 46, 48) of the topological map. Data received from one or more sensors on the target system is included in the topological map, and used together with the design specific information in the knowledge store to diagnose faults.