Abstract: A method for identifying a current context and determining a sensor combination based on the current environment. The method may comprise accepting a plurality of sensor outputs and implementing a CNN to convert the sensor outputs into a plurality of features that can be used to identify the context through use of a gating algorithm that additionally determines which sensors have the most importance and which have little or no importance. The method may further comprise executing sensor fusion in a manner corresponding to the context. For example, the context determines whether early fusion or late fusion should be implemented and which sensor outputs should be included in the said fusion.

Abstract: The present invention is directed to a Spatiotemporal scene-graph embedding methodology that models scene-graphs and resolves safety-focused tasks for autonomous vehicles. The present invention features a computing system comprising instructions for accepting the one or more images, extracting one or more objects from each image, computing an inverse-perspective mapping transformation of the image to generate a bird's-eye view (BEV) representation of each image, calculating relations between each object for each image, and generating a scene-graph for each image based on the aforementioned calculations. The system may further comprise instructions for calculating a confidence value for whether or not a collision will occur through the generation of a spatio-temporal graph embedding based on a spatial graph embedding and a temporal model.