Abstract: A system may receive events derived from historical sensor data. The system may generate repeat event records based on the events. The system may cluster the repeat event records into repeat event clusters. The system may determine filter ranges for the repeat event clusters. The system may generate a binarization filter comprising instructions to generate a binary sequence indicative of at least one of the filter ranges. The system may execute the binarization filter to generate binary sequences corresponding to the repeat event records, respectively. The system may train a prediction model based on the binary sequences. The system may deploy the binarization filter and the prediction model to a real-time platform.

Abstract: A method for determining a current distance and/or a current speed of a target object relative to a motor vehicle based on an image of the target object, in which the image is provided by a camera of the motor vehicle, where characteristic features of the target object are extracted from the image and a reference point associated with the target object is determined based on the characteristic features for determining the distance and/or the speed, wherein the distance and/or the speed are determined based on the reference point, and a baseline is determined in the image based on the characteristic features, which is in a transition area from the depicted target object to a ground surface depicted in the image, and a point located on the baseline is determined as the reference point.

Abstract: A system may receive events derived from historical sensor data. The system may generate repeat event records based on the events. The system may cluster the repeat event records into repeat event clusters. The system may determine filter ranges for the repeat event clusters. The system may generate a binarization filter comprising instructions to generate a binary sequence indicative of at least one of the filter ranges. The system may execute the binarization filter to generate binary sequences corresponding to the repeat event records, respectively. The system may train a prediction model based on the binary sequences. The system may deploy the binarization filter and the prediction model to a real-time platform.

Abstract: A device obtains an image and generates, based on the image and using an image generation model, a set of candidate images for object recognition. The device determines, based on the set of candidate images and using an image evaluation model, that one or more candidate images, of the set of candidate images, are processed during image generation and using the image generation model to improve a resolution of the image. The device determines image embeddings for the one or more candidate images and based on the image, and determines an embeddings error for the one or more candidate images based on the image embeddings and the image. The device determines an identification of a feature of the image based on the image embeddings and the embeddings error, and performs an action relating to the identification of the feature based on determining the identification of the feature.

Abstract: Systems and methods for predictively responding to real-time sensor data from an computer-augmented environment are provided. A system may receive sensor data from the computer-augmented environment. The system may obtain a feature model, a prediction model, and a filter criteria. The system may derive, based on the feature model and the sensor data, a first event. The system may determine the first event is associated with an authorized event identifier in the filter criteria. The system may forecast, based on the authorized event identifier and the prediction model that a second event occurs after the first event. The system may transmit an action message to the computer-augmented environment, the action message indicative of the second event. Updates to the feature model, the prediction model, and/or the filter criteria may occur in batch using machine learning and statistical analytics while the real-time platform predictively responds to the sensor data.

Abstract: The invention relates to a method for identifying an object (3) in a surrounding region (4) of a motor vehicle (1) as a pedestrian (5), in which the surrounding region (4) is captured in images (B) by means of at least one vehicle-side camera (6), wherein a shadow (11) that is projected from the object (3) onto a ground (12) in the surrounding region (4) and a contrast pattern (13) that is formed on the ground (12) and depends on the shadow (11) are detected in at least one of the captured images (B) and the object (3) is identified on the basis of a comparison of the contrast pattern (13) that was formed with a predetermined contrast pattern that characterizes a pedestrian (5). The invention moreover relates to a driver assistance system (2) and a motor vehicle (1).

Abstract: Systems and methods for predictively responding to real-time sensor data from an computer-augmented environment are provided. A system may receive sensor data from the computer-augmented environment. The system may obtain a feature model, a prediction model, and a filter criteria. The system may derive, based on the feature model and the sensor data, a first event. The system may determine the first event is associated with an authorized event identifier in the filter criteria. The system may forecast, based on the authorized event identifier and the prediction model that a second event occurs after the first event. The system may transmit an action message to the computer-augmented environment, the action message indicative of the second event. Updates to the feature model, the prediction model, and/or the filter criteria may occur in batch using machine learning and statistical analytics while the real-time platform predictively responds to the sensor data.

Abstract: A device obtains an image and generates, based on the image and using an image generation model, a set of candidate images for object recognition. The device determines, based on the set of candidate images and using an image evaluation model, that one or more candidate images, of the set of candidate images, are processed during image generation and using the image generation model to improve a resolution of the image. The device determines image embeddings for the one or more candidate images and based on the image, and determines an embeddings error for the one or more candidate images based on the image embeddings and the image. The device determines an identification of a feature of the image based on the image embeddings and the embeddings error, and performs an action relating to the identification of the feature based on determining the identification of the feature.

Abstract: The invention relates to a method for identifying an object (3) in a surrounding region (4) of a motor vehicle (1) as a pedestrian (5), in which the surrounding region (4) is captured in images (B) by means of at least one vehicle-side camera (6), wherein a shadow (11) that is projected from the object (3) onto a ground (12) in the surrounding region (4) and a contrast pattern (13) that is formed on the ground (12) and depends on the shadow (11) are detected in at least one of the captured images (B) and the object (3) is identified on the basis of a comparison of the contrast pattern (13) that was formed with a predetermined contrast pattern that characterizes a pedestrian (5). The invention moreover relates to a driver assistance system (2) and a motor vehicle (1).

Abstract: A method for determining a current distance and/or a current speed of a target object relative to a motor vehicle based on an image of the target object, in which the image is provided by a camera of the motor vehicle, where characteristic features of the target object are extracted from the image and a reference point associated with the target object is determined based on the characteristic features for determining the distance and/or the speed, wherein the distance and/or the speed are determined based on the reference point, and a baseline is determined in the image based on the characteristic features, which is in a transition area from the depicted target object to a ground surface depicted in the image, and a point located on the baseline is determined as the reference point.

Abstract: A method and apparatus for conditioning communication network data relating to a distribution of network entities across a space, for subsequent processing of the network data, is disclosed. The method comprises dividing the space into a grid comprising a plurality of discrete cells, so that each cell comprises a unique location within the space. The network data relating to each entity is subsequently processed using a processor to assign the network entity to a cell in dependence of the location of the entity within the space, relative to the cells. Following the discretisation of network entities to a particular cell, the number of entities within the cells is determined. The number of entities associated with the cells is then separately compared with the number of entities within each cell of a respective cell distribution using the processor, to determine the cell maxima of each distribution which comprises the most entities.