Zimeno Inc.

A vehicle row follow system may include a vehicle comprising at least one sensor to output signals serving as a basis for a three-dimensional (3D) point cloud and to output signals corresponding to a two-dimensional (2D) image of a region forward the vehicle. The system may further include a non-transitory computer readable medium containing instructions to direct a processor to: determine plan row lines in a 2D image; determine a yaw of the vehicle based upon a slope of a heading line relative to a centerline between the plant row lines in the 2D image; determine a lateral offset of the vehicle from the centerline between the consecutive plant rows based upon an identity of the consecutive plant rows in the 3D point cloud; and output steering control signals based upon the determined yaw and lateral offset of the vehicle.

A vehicle may include a movable roof, a sensor supported by the roof, and an actuator for selectively raising and lowering the roof.

An obstruction avoidance system may include a agronomy vehicle, at least one sensor configured to output signals serving as a basis for a three-dimensional (3D) point cloud and to output signals corresponding to a two-dimensional (2D) image, and instructions to direct a processor to capture a particular 2D image and to obtain signals serving as a basis for a particular 3D point cloud; identify an obstruction candidate in the particular 2d image; correlate the obstruction candidate to a portion of the particular 3D point cloud to determine a value for a parameter of the obstruction candidate; classify the obstruction candidate as an obstruction based upon the parameter; and output control signals to alter a state of the agronomy vehicle in response to the obstruction candidate being classified as an obstruction.

A vehicle may include a movable roof, a sensor supported by the roof, and an actuator for selectively raising and lowering the roof.

A vehicle row follow system may include a vehicle comprising at least one sensor to output signals serving as a basis for a three-dimensional (3D) point cloud and to output signals corresponding to a two-dimensional (2D) image of a region forward the vehicle. The system may further include a non-transitory computer readable medium containing instructions to direct a processor to: determine plan row lines in a 2D image; determine a yaw of the vehicle based upon a slope of a heading line relative to a centerline between the plant row lines in the 2D image; determine a lateral offset of the vehicle from the centerline between the consecutive plant rows based upon an identity of the consecutive plant rows in the 3D point cloud; and output steering control signals based upon the determined yaw and lateral offset of the vehicle.

A vehicle may include a movable roof, a sensor supported by the roof, and an actuator for selectively raising and lowering the roof.

An image frame streaming system may include a vehicle, a video camera carried by the vehicle to output image frames, a multi-media framework carried by the vehicle and configured to generate a first stream and a second stream from the image frames, a first consumer to receive the first stream and a second consumer to receive the second stream. The first consumer and the second consumer are each selected from a group of consumers consisting of: a neural network; a live video stream presenter; and a historical time clip storage.

An object detection system may apply an object detection model to the first image frame to predict a location of a first bounding box of an object in the first image frame and apply a confidence value to the predicted location of first bounding box. In response to the confidence level exceeding a predetermined threshold, a location of a second bounding box in the second image frame may be estimated based on the location of the first bounding box and non-zero movement of the vehicle. The object detection model is updated based on the estimated location of the second bounding box in the second image frame. A location of a third bounding box in the third image frame is predicted using the updated object detection model. An operation of the vehicle is controlled based on the predicted location of the third bounding box.

Optical flow may be carried out on a series of images captured by the camera during movement of a candidate implement. Particular pixels may be aggregated based on relative pixel motion and classifying the particular pixels as belonging to the candidate implement. A candidate implement contour may be determined based on the particular pixels classified as belonging to the candidate implement. Based upon the comparison, control signals may be output to adjust operation of at least one of a tractor and the candidate implement attached to the tractor. In some implementations, the implement or its contour may be tracked and compared to a boundary for further vehicle control or operator alerts.

A vehicle vision system is configured to visibly present a view of a scene to an operator, the operator's view of the scene being otherwise visibly obstructed by an obstruction movably supported by a vehicle.