A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that a ground surface is within the field of view of the camera during taxiing of the aircraft. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera for docking guidance by identifying, within the captured image data, a region on the ground surface corresponding to an alignment fiducial indicating a parking location for the aircraft, determining, based on the region of the captured image data corresponding to the alignment fiducial indicating the parking location, a relative location of the aircraft with respect to the alignment fiducial, and outputting an indication of the relative location of the aircraft to the alignment fiducial.

A method of processing an image, an electronic device, and a storage medium, which relate to the artificial intelligence field, in particular to fields of computer vision and intelligent transportation technologies. The method includes: determining at least one key frame image in a scene image sequence captured by a target camera; determining a camera pose parameter associated with each key frame image in the at least one key frame image, according to a geographic feature associated with the key frame image; and projecting each scene image in the scene image sequence to obtain a target projection image according to the camera pose parameter associated with the key frame image, so as to generate a scene map based on the target projection image. The geographic feature associated with any key frame image indicates localization information of the target camera at a time instant of capturing the corresponding key frame image.

Automated vehicle repair estimation by aggregate ensembling of multiple artificial intelligence functions is provided. A method comprises receiving a plurality of vehicle repair recommendation sets for a damaged vehicle, wherein each of the vehicle repair recommendation sets identifies at least one recommended vehicle repair operation of a plurality of the vehicle repair operations for the damaged vehicle; aggregating a plurality of the recommended vehicle repair operations; generating a composite vehicle repair recommendation set that identifies the aggregated recommended vehicle repair operations; and providing the composite vehicle repair recommendation set to one or more vehicle repair insurance claims management systems.

In one embodiment, a method for extracting point cloud features for use in localizing an autonomous driving vehicle (ADV) includes selecting a first set of keypoints from an online point cloud, the online point cloud generated by a LiDAR device on the ADV for a predicted pose of the ADV; and extracting a first set of feature descriptors from the first set of keypoints using a feature learning neural network running on the ADV, The method further includes locating a second set of keypoints on a pre-built point cloud map, each keypoint of the second set of keypoints corresponding to a keypoint of the first set of keypoint; extracting a second set of feature descriptors from the pre-built point cloud map; and estimating a position and orientation of the ADV based on the first set of feature descriptors, the second set of feature descriptors, and a predicted pose of the ADV.

A method includes receiving a first set of images from an image capture device of a vehicle. The method also includes performing a first analysis of movement of biomechanical points of occupants of the vehicle in the first set of images. The method further includes receiving an indication that a traffic incident has occurred. The method also includes receiving a second set of images from the image capture device corresponding to when the traffic incident occurred. The method further includes performing a second analysis of movement of the biomechanical points of the occupants in the second set of images. The method also includes determining a likelihood of injury or a severity of injury to the occupants based on the first analysis of movement and the second analysis of movement.

A system for imaging and analyzing a vehicle may include a frame having a central passage, wherein the central passage is configured and dimensioned to allow a vehicle to pass through. The frame may include, for example, a pair of substantially vertical legs connected at the top by a cross member, wherein the legs and cross member define the central passage. One or more bollards may be positioned in front of and/or behind the frame. A plurality of cameras within the each leg, cross member, and/or bollard may be directed toward the passage to record video images of a passing vehicle. Integrated LED array panels may provide bands of light to aid in detection of surface anomalies, for example by simultaneous analysis of symmetrical sides of the vehicle.

A vehicle control system for moving a vehicle to a target location is disclosed. According to examples of the disclosure, a camera captures one or more images of a known object corresponding to the target location. An on-board computer having stored thereon information about the known object can process the one or more images to determine vehicle location with respect to the known object. The system can use the vehicle's determined location and a feedback controller to move the vehicle to the target location.

Provided are methods for localization functional safety, which can include systems, methods, and computer program products are also provided. In examples, a method includes applying a transform to a source point cloud and calculating a second metric based on the application of the transform to the source point cloud and a map at a higher ASIL level. A first metric is determined based on a localization function that executes at a lower ASIL level. A deviation between a first metric and the second metric, is determined wherein the vehicle localization is validated when the deviation is less than a predetermined threshold.

Disclosed are apparatuses, systems, and techniques that may perform efficient deployment of machine learning for detection and classification of moving objects in streams of images. A set of machine learning models with different input sizes may be used for parallel processing of various regions of interest in multiple streams of images. Both the machine learning models as well as the inputs into these models may be selected dynamically based on a size of the regions of interest.

Provided are methods for deep learning-based camera calibration, which can include receiving first and second images captured by a camera, processing the first image using a first neural network to determine a depth of the first image, processing the first image and the second image using a second neural network to determine a transformation between a pose of the camera for the first image and a pose of the camera for the second image, generating a projection image based on the depth of the first image, the transformation of the pose of the camera, and intrinsic parameters of the camera, comparing the second image and the projection image to determine a reprojection error, and adjusting at least one of the intrinsic parameters of the camera based on the reprojection error. Systems and computer program products are also provided.