| CPC G06V 10/774 (2022.01) [G06V 10/225 (2022.01); G06V 10/776 (2022.01); G06V 20/58 (2022.01); G06V 2201/08 (2022.01)] | 18 Claims |
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1. A method for training a machine-learning model, the method comprising:
accessing a library of training image data, the library of training image data including sets of images representing respective time periods, at least a plurality of sets of images of the library including respective representations of a respective first vehicle from a perspective of a respective second vehicle over a respective time period, the plurality of sets of images including only sets of images where the respective first and second vehicle are positioned in a respective common lane of travel, and each set of images in the plurality of sets of images associated with a collision label indicating whether the respective first vehicle and the respective second vehicle collide in the respective time period;
for each set of images in the plurality of sets of images:
applying, by at least one processor, an object detection model to each image in the set of images to determine a respective bounding box representation of the respective first vehicle for each image in the set of images;
determining, by the at least one processor, a rate of change of at least one spatial parameter of the bounding box representation for the respective first vehicle across at least a subset of images of the set of images;
determining a confidence of collision indicator for the set of images by applying, by the at least one processor, a collision detection model to the rate of change of the at least one spatial parameter; and
evaluating a collision loss function, the collision loss function including a difference between an indication of collision in a respective collision label for the set of images and the determined confidence of collision indicator; and
training the collision detection model by adjusting model parameters to minimize the collision loss function over the plurality of sets of images.
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