Abstract: System and methods are disclosed for capturing license plate (LP) information of a vehicle in relative motion to a camera device. In one example, the camera system detects the LP in multiple frames, then aligns and geometrically rectifies the image of the LP by scaling, warping, rotating, and/or performing other functions on the images. The camera system may optimize capturing of the LP information by executing a temporal noise filter on the aligned, geometrically rectified images to generate a composite image of the LP for optical character recognition. In some examples, the camera device may include an image sensor, such as a high dynamic range (HDR) sensor, modified to set long and short exposures of the HDR sensor to capture frames of a vehicle's LP, but without consolidating the images into a composite image. The camera system may set optimal exposure settings based on detected relative speed of the vehicle.

Abstract: System and methods are disclosed for capturing license plate (LP) information of a vehicle in relative motion to a camera device. In one example, the camera system detects the LP in multiple frames, then aligns and geometrically rectifies the image of the LP by scaling, warping, rotating, and/or performing other functions on the images. The camera system may optimize capturing of the LP information by executing a temporal noise filter on the aligned, geometrically rectified images to generate a composite image of the LP for optical character recognition. In some examples, the camera device may include an image sensor, such as a high dynamic range (HDR) sensor, modified to set long and short exposures of the HDR sensor to capture frames of a vehicle's LP, but without consolidating the images into a composite image. The camera system may set optimal exposure settings based on detected relative speed of the vehicle.

Abstract: A map labeling system trains a machine-learned model using a set of training data and generates a set of test predictions for a set of test properties by applying the machine-learned model to a set of testing data. Each prediction in the set of test predictions comprises a confidence score representing the machine-learned model's confidence in the prediction. The map labeling system determines a correctness of each prediction in the set of predictions and determines a relationship between the confidence scores and the correctness of the test predictions. The map labeling system establishes a confidence threshold for the machine-learned model based on the determined relationship and labels a production property by applying the machine-learned model to production data.

Abstract: A method and apparatus for estimating and tracking a 3D object shape and pose estimation is disclosed A plurality of 3D object models of related objects varying in size and shape are obtained, aligned and scaled, and voxelized to create a 2D height map of the 3D models to train a principle component analysis model. At least one sensor mounted on a host vehicle obtains a 3D object image. Using the trained principle component analysis model, the processor executes program instructions to estimate the shape and pose of the detected 3D object until the shape and pose of the detected 3D object matches one principle component analysis model. The output of the shape and pose of the detected 3D object is used in one vehicle control function.

Abstract: In various embodiments, electronic apparatus, systems, and methods include a unified compact spatiotemporal method that provides a process for machines to deal with space and time and to deal with sensors and effectors. Additional apparatus, systems, and methods are disclosed.

Abstract: In various embodiments, electronic apparatus, systems, and methods include a unified compact spatiotemporal method that provides a process for machines to deal with space and time and to deal with sensors and effectors. Additional apparatus, systems, and methods are disclosed.

Abstract: In various embodiments, electronic apparatus, systems, and methods include a unified compact spatiotemporal method that provides a process for machines to deal with space and time and to deal with sensors and effectors. Additional apparatus, systems, and methods are disclosed.