Self-supervised estimation of observed vehicle pose

A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to:

• receive a first image captured by a camera at a first time instance, wherein the first image includes at least a portion of an observed vehicle;
• determine a first ray angle based on a coordinate system of an ego-vehicle and a first coordinate system of the observed vehicle based on the first image;
• receive a second image captured by the camera at a second time instance, wherein the second image includes at least a portion of the observed vehicle oriented at a different viewpoint;
• determine a second ray angle based on the coordinate system of the ego-vehicle and a second coordinate system of the observed vehicle based on the second image;
• receive vehicle odometry data;
• determine a local angle difference based on the first ray angle, the second ray angle, and the vehicle odometry data;
• and
• train a deep neural network using the local angle difference, the first image, and the second image to output a position of the ego-vehicle with respect to the observed vehicle for operating the ego-vehicle,
• wherein the deep neural network comprises a Siamese neural network and wherein the Siamese neural network determines a first local angle based on a first color image and a second local angle based on a second color image and determines a contrastive loss based on a difference between the first local angle and the second local angle.