A method for risk based processing, the method may include detecting, based on first sensed information sensed at a first period, a suspected risk within an environment of a vehicle; selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; selecting, from reference information, a suspected risk related subset reference information, wherein the potential risk related subset of the reference information is related to the potential risk; and determining whether the suspected risk is an actual risk, based at least on part on the suspected risk related subset reference information.

A method for situation aware processing, the method may include detecting a situation, based on first sensed information at a first period; selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; and performing, by a situation related processing unit, a situation related processing, wherein the situation related processing is based on the situation related subset of the reference information and on second sensed information sensed at a second period, wherein the situation related processing comprises at least one out of object detection and object behavior estimation,

A method for safe transfer between manned and autonomous driving modes, the method may include detecting, based on first sensed information sensed during a first period, a situation related to an environment of the autonomous vehicle; searching for one or more matching concepts of a group of reference concepts, to which the situation belongs, each reference concept of the group represents a plurality of situations and has a reference concept safety level; wherein for each reference concept of at least a sub-group of the group of reference concepts the safety level of the reference concept is based on a tested success level of at only some of the plurality of scenarios represented by the reference concept; and determining, based on an outcome of the searching, whether the vehicle is capable to safely autonomously drive through the environment.

V2V communication-less truck platooning.

A method for detecting fallen cargo, the method may include receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect fallen cargo and to classify the fallen cargo to fallen cargo classes; estimating, from the sensed information, an impact of at least some of the fallen cargo classes on a behavior of at least some of the multiple vehicles; and determining, based on the impact, at least one suggested vehicle behavior as a response to a detection of at least some of the fallen cargo classes.

An approach is provided for generating navigation data of a geographical location. The approach involves identifying a landmark located along a source road from a source image and segmenting the source image using a deep learning model to identify a segmentation mask. The approach also involves generating a template image based on the segmentation mask and a street image of the landmark, and matching the template image successively with a sequence of images of the landmark to determine a confidence score. The approach further involves, identifying a first image from the sequence of images with confidence score below a predetermined threshold, and selecting a second image with confidence score above the predetermined threshold from the sequence of images. The approach further involves calculating a visibility distance of the landmark based on the source image and the second image, and generating the navigation data based on the calculated visibility distance.

A method for determining a property of an object is disclosed, which includes: recording a first image of the object from a first direction; recording a second image of the object from a second direction; determining a first position in the first image, the first position representing a location of the object, and a second position in the second image, the second position representing the same location of the object, for a multiplicity of locations of the object; and calculating a value of an object property for each of the multiplicity of locations of the object. The value assigned to a location of the multiplicity of locations of the object is calculated using an intensity value at the first position, which represents the location, in the first image and an intensity value at the second position, which represents the location, in the second image.

Embodiments described herein provide a system for facilitating image sampling for training a target detector. During operation, the system obtains a first image depicting a first target. Here, the continuous part of the first target in the first image is labeled and enclosed in a target bounding box. The system then generates a set of positive image samples from an area of the first image enclosed by the target bounding box. A respective positive image sample includes at least a part of the first target. The system can train the target detector with the set of positive image samples to detect a second target from a second image. The target detector can be an artificial intelligence (AI) model capable of detecting an object.

A method of label detection includes: obtaining, by an imaging controller, an image depicting a shelf; increasing an intensity of a foreground subset of image pixels exceeding an upper intensity threshold, and decreasing an intensity of a background subset of pixels below a lower intensity threshold; responsive to the increasing and the decreasing, (i) determining gradients for each of the pixels and (ii) selecting a candidate set of the pixels based on the gradients; overlaying a plurality of shelf candidate lines on the image derived from the candidate set of pixels; identifying a pair of the shelf candidate lines satisfying a predetermined sequence of intensity transitions; and generating and storing a shelf edge bounding box corresponding to the pair of shelf candidate lines.

In a case where a target image that is a diagnostic target is selected and a reference image reference tag is operated, a similar image similar to the target image is detected. Then, an image referred to in the past diagnosis using the similar image is detected, and the detected image is displayed on a diagnostic support screen as a support image. On the diagnostic support screen, the support image is displayed as a thumbnail image. In a case where any thumbnail image is selected, the selected support image is enlarged and displayed. On the diagnostic support screen, the degree of effectiveness indicating how much the support image has been referred to is displayed so as to be associated with the support image.