Abstract: First sensor data and second sensor data can be combined by inputting the first sensor data and second sensor data to a deep neural network. A segmentation map from the combined sensor data that includes labeled segments, wherein the labeled segments include (a) pixels corresponding to objects in the combined sensor data, (b) hazard probabilities for respective labeled segments included in the segmentation map can be determined in the deep neural network based on the combined first sensor data and the second sensor data. The segmentation map and the hazard probabilities can be output.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to input an image to a first layer of a machine learning program, the first layer trained to identify one or more quadrilateral regions in the image, upon identifying the one or more quadrilateral regions, input the collected image to a second layer of a machine learning program, the second layer trained to identify a plurality of sets of vertices, each set of vertices defining a respective polygonal area, identify one of the polygonal areas in which to park a vehicle, and actuate one or more vehicle components to move the vehicle into the identified polygonal area.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to input an image to a first layer of a machine learning program, the first layer trained to identify one or more quadrilateral regions in the image, upon identifying the one or more quadrilateral regions, input the collected image to a second layer of a machine learning program, the second layer trained to identify a plurality of sets of vertices, each set of vertices defining a respective polygonal area, identify one of the polygonal areas in which to park a vehicle, and actuate one or more vehicle components to move the vehicle into the identified polygonal area.