Abstract: According to one aspect, systems, methods, and/or techniques associated with operator take-over prediction may include receiving a two-channel series of images of an operating environment through which a vehicle is travelling. A first series of images may be represented by labels corresponding to classified objects. A second series of images may be represented as a gaze heatmap or an eye tracking heatmap. Additionally, feeding encoded series of images through a three-dimensional (3D) convolutional neural network (CNN) to produce a first output, receiving sets of information corresponding to a first series of images and a second series of images in time, feeding sets of information through processing layers to produce additional outputs, concatenating the first output and the additional outputs to produce a concatenation output, and feeding the concatenation output through additional processing layers to generate an operator take-over prediction may be performed.

Abstract: According to one aspect, systems, methods, and/or techniques associated with operator take-over prediction may include receiving a two-channel series of images of an operating environment through which a vehicle is travelling. A first series of images may be represented by labels corresponding to classified objects. A second series of images may be represented as a gaze heatmap or an eye tracking heatmap. Additionally, feeding encoded series of images through a three-dimensional (3D) convolutional neural network (CNN) to produce a first output, receiving sets of information corresponding to a first series of images and a second series of images in time, feeding sets of information through processing layers to produce additional outputs, concatenating the first output and the additional outputs to produce a concatenation output, and feeding the concatenation output through additional processing layers to generate an operator take-over prediction may be performed.