Here Global B.v.

Nicholas Dronen

Stephen O'Hara

Jan Van Sickle

Brad Keserich

Vladimir Shestak

Raghavendran Balu

Shubhabrata Roy

Amritpal Singh Gill

Andrew Adare

An apparatus, or corresponding method, for building or updating a map database is described. In one example, the apparatus includes an image correlation module, a training device, and a learned model or neural network. The image correlation module is configured to correlate a first aerial image and terrestrial sensor data collected at a terrestrial vehicle based on at least one control point from the terrestrial data. The learned model training device is configured to define a learned model based using at least one control point from the terrestrial sensor data as ground truth for analysis of the first aerial image. The learned model inference module is configured to receive a second aerial image and apply the learned model on the second aerial image for identification of mapping information for the map data.

Automatic feature extraction from imagery

A method, apparatus, and computer program product are provided for providing improved neural network implementations using a repeated convolution-based attention module. Example embodiments implement a repeated convolution-based attention module that utilizes multiple iterations of a repeated convolutional application layer and subsequent augmentations to generate an attention module output. Example methods may include augmenting an attention input data object based on a previous iteration convolutional output to produce a current iteration input parameter, inputting the input parameter to a repeated convolutional application layer to generate a current iteration input parameter, repeating for multiple iterations, and augmenting the attention input data object based on the final convolutional output to produce an attention module output. Other methods may include an initial convolutional application layer, and/or apply and augment the output of the initial convolutional application layer, and include convolutional application layer(s) having at least two sub-layers.

Methods, apparatuses, and computer program products using a repeated convolution-based attention module for improved neural network implementations

An approach is provided for selecting training observations for machine learning models. The approach involves determining a first distribution of a plurality of features observed in the training data set, and a second distribution of the plurality of features observed in the candidate pool of observations. The approach further involves selecting one or more observations in the candidate pool of observations for annotation based on the first distribution and the second distribution. The approach further involves adding the one or more observations to the training data set after annotation. The training data set is used for training the machine learning model.

Method, apparatus, and system for in-vehicle data selection for feature detection model creation and maintenance

Changes in a road network may be detected and information/data regarding the change may be locally propagated in at least near real time with respect to the detection of the change. A vehicle apparatus onboard a vehicle analyzes sensor data collected as the vehicle traverses at least a portion of a road network. The sensor data is captured by sensors onboard the vehicle. The sensor data is analyzed in at least near real time with respect to the capturing of the sensor data. The vehicle apparatus detects an inconsistency between a result of the analysis of the sensor data and map data stored in the memory. Responsive to the detected inconsistency satisfying a vehicle-to-vehicle criterion, the vehicle apparatus generates a notification message comprising an indication of the detected inconsistency. The vehicles apparatus transmits the notification message via a communication interface using a short or medium range communication protocol.

Road network change detection and local propagation of detected change

An apparatus, method and computer program product are provided to train a feature detector to identify a respective feature from images captured by a camera. With respect to an apparatus, the apparatus causes at least one feature from one or more images that have been labelled to be projected onto a map. The apparatus is also caused to refine a representation of a path of a vehicle that carries a camera that captured the one or more images based upon registration of the at least one feature that has been projected with the map. Based upon the path of the vehicle following refinement, the apparatus projects one or more other features that have not been labelled from the map into the one or more images and then utilizes the images to train a feature detector.

Apparatus and method for efficiently training feature detectors

Synthetic training information/data of a second probe style is generated based on first probe information/data of a first probe style using a style transfer model. First probe information/data is defined. An instance of first probe information/data comprises labels and first probe style sensor information/data. A style transfer model generates training information/data based on at least a portion of the first probe information/data. An instance of training information/data corresponds to an instance of first probe information/data and comprises second probe style sensor information/data. The first and second probe styles are different. A second probe style model is trained using machine learning and the training information/data. The second probe style model is used to analyze second probe style second probe information/data to extract map information/data from the second probe information/data. Each instance of second probe data is captured by one or more second probe sensors of a second probe apparatus.

Machine learning a feature detector using synthetic training data

An approach is provided for dynamic adaptation of an in-vehicle feature detector. The approach involves embedding a feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof. The approach also involves deploying the feature detection model, the precomputed weights, or a combination thereof to adapt an in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof. The in-vehicle feature detector can then use the feature detection model, the precomputed weights, or a combination thereof to process sensor data collected while in the geographic area to detect one or more features.

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Chicago, IL, United States of America

Nicholas Dronen