TECHNICAL FIELD

The present disclosure relates generally to vehicle collision detection systems, and more specifically to an ego part exclusion training set for neural network based collision detection systems.

BACKGROUND

Modern vehicles include multiple sensors and cameras distributed about all, or a portion, of the vehicle. The cameras provide video images to a controller, or other computerized systems within the vehicle. Included on some vehicles are collision detection systems. The collision detection systems analyze the incoming video images and determine when an object is likely to collide with the vehicle.

In some instances, ego parts (i.e. parts that are connected to, but distinct from, a vehicle) are attached to the vehicle and can be within the field of vision of one or more of the cameras providing images to the collision detection system. Due to the nature of existing camera systems, the image of the ego parts are analyzed along with a remainder of the incoming video. This analysis wastes computing power on analyzing sections of the video feed where we do not expect another object to be present due to the presence of the ego part(s). The waste can be substantial, as the ego part(s) often occupy a significant portion of the field of view of a given camera feed. Further in some systems, the ego part(s) can trigger a false collision warning due to their independent motion or due to a similarity between a given ego part and other objects in the view of the camera.

SUMMARY OF THE CLAIMS

In one exemplary embodiment a vehicle includes a vehicle body having a camera and at least one ego part connection, an ego part connected to the vehicle body via the ego part connection, a collision detection system communicatively coupled to the camera and configured to receive a video feed from the camera, and the collision detection system being configured to identify an exclusion region of each frame corresponding to the ego part, perform an object detection on a remainder of each frame, and generate a collision detection warning in response to an object being detected by the object detection system.

In another example of the above described vehicle the collision detection system is a neural network trained via a data set including exclusion zones corresponding to at least one type of ego part.

In another example of any of the above described vehicles the data set includes exclusion zones corresponding to a plurality of types of ego parts.

In another example of any of the above described vehicles the plurality of types of ego parts includes a bucket, a ripper, a door, a staircase, a trailer, and a dolly.

In another example of any of the above described vehicles each frame of the data set includes at least one annotation corresponding to the exclusion zone defined in the frame.

In another example of any of the above described vehicles the at least one annotation is an image plane annotation corresponding to at least one feature of the image.

In another example of any of the above described vehicles the at least one annotation is a 3D space annotation corresponding to a relative positioning of the ego part and the vehicle.

In another example of any of the above described vehicles the data set is augmented using at least one of Y-axis flipping, image smoothing/blurring/stretching/skewing, addition of Gaussian noise, and increasing or decreasing contrast.

In another example of any of the above described vehicles the collision detection system is configured to generate a 3D geometry of an ego part corresponding to the identified exclusion region.

In another example of any of the above described vehicles the collision detection system is configured to merge the generated 3D geometry of the identified exclusion region and a stored 3D geometry of the vehicle body, thereby creating a combined geometry.

In another example of any of the above described vehicles the collision detection warning is generated in response to the collision detection system determining that the detected object will collide with the combined geometry.

In another example of any of the above described vehicles the exclusion zone includes at least one fully surrounded inclusion zone.

An exemplary method for operating a vehicle collision detection system includes, receiving a video feed from a camera, identifying an ego part disposed within the video feed, identifying an exclusion zone corresponding to the ego part, performing an object detection on a first portion of each frame of the video feed, wherein the first portion excludes the exclusion zone, and generating a collision detection warning in response to an object being detected by the object detection system.

In another example of the above described method for operating a vehicle collision detection system identifying the exclusion zone includes identifying at least one inclusion zone defined within the exclusion zone.

In another example of any of the above described methods for operating a vehicle collision detection system the first portion further includes at least one inclusion zone.

In another example of any of the above described methods for operating a vehicle collision detection system identifying the ego part disposed within the view further includes determining a 3D geometry of the ego part and combining the 3D geometry of the ego part with a 3D geometry of the vehicle, thereby creating a combined geometry.

In another example of any of the above described methods for operating a vehicle collision detection system performing the object detection on the first portion of each frame of the video feed includes comparing an object position with the combined geometry.

In another example of any of the above described methods for operating a vehicle collision detection system the collision detection system is a neural network trained via a data set including exclusion zones corresponding to at least one type of ego part.

In another example of any of the above described methods for operating a vehicle collision detection system the data set includes exclusion zones corresponding to a plurality of types of ego parts.

