A system for classifying objects or situations in the environment of a vehicle , comprising: • at least one lossy data compressor (1, 1a-1d), communicatively coupled to at least one sensor (52a-52d) that is configured to obtain physical measurement data from at least part (53a-53d) of the environment of the vehicle ; and • a classifier module , communicatively coupled to the lossy data compressor and configured to classify whether output of the lossy data compressor indicates the presence of at least one object or at least one situation in the environment of the vehicle , wherein the lossy data compressor comprises a parametrized mapping network that, when applied to a data point x of the physical measurement data that lives in a space X, produces a point z in a Riemannian manifold Z that has a lower dimensionality than the space X, and configured to provide a point z on Z as the output in response to receiving said data point x as input, wherein • the manifold Z is a continuous hypersurface that only admits fully continuous paths between any two points on the hypersurface; and • parameters θ of the mapping network are trainable or trained towards an objective that comprises minimizing, on the manifold Z, a distance between a given prior distribution Pz and a distribution P Q induced on the manifold Z by mapping a given set P D of physical measurement data from space X onto manifold Z using the mapping network , according to a given distance measure, • wherein the distance measure is well-defined both for distances between distributions and for distance between individual points, and wherein the classifier module is communicatively coupled to the lossy data compressor (1a-1d) via a shared-medium bus network to which further systems of the vehicle are coupled, whereas the lossy data compressor (1a-1d) is communicatively coupled to said at least one sensor (52a-52d) via a dedicated broadband connection (56a-56d). • at least one lossy data compressor (1, 1a-1d), communicatively coupled to at least one sensor (52a-52d) that is configured to obtain physical measurement data from at least part (53a-53d) of the environment of the vehicle ; and • a classifier module , communicatively coupled to the lossy data compressor and configured to classify whether output of the lossy data compressor indicates the presence of at least one object or at least one situation in the environment of the vehicle , wherein the lossy data compressor comprises a parametrized mapping network that, when applied to a data point x of the physical measurement data that lives in a space X, produces a point z in a Riemannian manifold Z that has a lower dimensionality than the space X, and configured to provide a point z on Z as the output in response to receiving said data point x as input, wherein • the manifold Z is a continuous hypersurface that only admits fully continuous paths between any two points on the hypersurface; and • parameters θ of the mapping network are trainable or trained towards an objective that comprises minimizing, on the manifold Z, a distance between a given prior distribution Pz and a distribution P Q induced on the manifold Z by mapping a given set P D of physical measurement data from space X onto manifold Z using the mapping network , according to a given distance measure, • wherein the distance measure is well-defined both for distances between distributions and for distance between individual points, and wherein the classifier module is communicatively coupled to the lossy data compressor (1a-1d) via a shared-medium bus network to which further systems of the vehicle are coupled, whereas the lossy data compressor (1a-1d) is communicatively coupled to said at least one sensor (52a-52d) via a dedicated broadband connection (56a-56d). <EOS>