Abstract: Embodiments relate to the detection of edge cases through application of a neural network to predict future vehicle environment data and identifying an edge case when the prediction error exceeds a given threshold. This allows edge cases to be identified based on unexpected vehicle environmental conditions or conditions that otherwise cause the neural network to make inaccurate predictions. These edge cases can then be utilised to better train machine learning systems, for instance, to train autonomous vehicle control systems. Alternatively, the identification of an edge case can highlight the need for remedial action, and can therefore trigger an alert to a vehicle control system to take remedial action. Further methods and systems described herein improve environmental sensing by providing a computationally efficient and accurate means for fusing sensor data and using this fused data to control sensors to focus on areas that would most reduce the uncertainty in the sensing system.

Abstract: The embodiments described herein aim to improve environmental sensing by providing a computationally efficient and accurate means for fusing sensor data and using this fused data to control sensors to focus on areas that would most reduce the uncertainty in the sensing system. In this way, the system can direct sensors to focus on the most important areas and features within the environment in order to provide the most effective sensor data (e.g. for use by a control system). The methods described herein make use of multi-agent sensor-action fusion. The methods are multi-agent in that a set of machine learning agents are trained in order to control the sensors to focus on the most important features and regions. The embodiments implement sensor-action fusion in that sensor fusion is performed in order to obtain a combined view of the environment and this combined view is utilised to determine the most appropriate actions.

Abstract: A system, method and computer program product for modeling electronic circuits via a sparse solution, or a sparse representation of a recurrent single or multi kernel support vector regression machine is provided. In one embodiment, the sparse representation may be attained, for example, by limiting a number of training data points for the method involving support vector regression. Each training data point may be selected based on the accuracy of a non-recurrent or fully recurrent model using an active learning principle applied to the non-successive or successive (time domain) data. A training time may be adjusted, for example, by (i) selecting how often one or more hyperparameters are optimized; or (ii) limiting the number of iterations of the method and consequently the number of support vectors.