Verizon Connect Development Limited

Verizon Patent and Licensing Inc.

Verizon Connect Ireland Limited

Verizon Connect Development Limted

Andrea Benericetti

Leonardo Taccari

Francesco Sambo

Leonardo Sarti

Tommaso Bianconcini

Luca Bravi

Douglas Coimbra De Andrade

Matteo Simoncini

Aurel Pjetri

Luca Kubin

David Di Lorenzo

Tommaso Innocenti

Alessio Frusciante

Marco Gualtieri

Samuele Salti

Alessandro Lori

Stefano Caprasecca

Luca Noce

Alex Quintero Garcia

David Leonardo Spain Contini

Francesca Chiti

S M Murtoza Habib

Andrea Giannoni

A device may receive video data identifying videos, and may process the video data with a machine learning model, to determine classifications. The device may generate labels for the videos, and may calculate event severity scores and event severity labels. The device may calculate event severity incoherence scores, and may calculate user feedback scores of users associated with the device. The device may determine reviewer mistrust scores, and may calculate time review scores. The device may calculate reviewer bias scores, and may determine relabeling scores for the videos based on the event severity incoherence scores, the user feedback scores, the reviewer mistrust scores, the time review scores, and the reviewer bias scores. The device may generate new labels for one or more of the videos based on the relabeling scores, and may retrain the machine learning model, with the new labels, to generate a retrained machine learning model.

Systems and methods for determining when to relabel data for a machine learning model

A device may receive driver facing video data associated with a driver of a vehicle and forward facing video data associated with the vehicle, and may process the driver facing video data, with a face model, to identify driver head orientation and driver gaze. The device may generate a first transformation matrix mapping the driver facing video data, the driver head orientation, and the driver gaze, and may generate a second transformation matrix mapping the driver facing video data and the forward facing video data. The device may utilize the first transformation matrix and the second transformation matrix to estimate image coordinates, and may aggregate the image coordinates to generate aggregated coordinates. The device may generate heat maps based on the aggregated coordinates, may train machine learning model, with the heat maps, to generate a trained machine learning model, and may perform actions based on the trained machine learning model.

Systems and methods for determining road object importance based on forward facing and driver facing video data

A device may process the video data, with a first machine learning model, to identify a driver of a vehicle and may process the video data associated with the driver, with a second machine learning model, to detect behavior data identifying a behavior of the driver. The device may process the behavior data, with a third machine learning model, to determine distraction data identifying whether the behavior is classified as a distracted behavior. The device may process the behavior data, with a fourth machine learning model, to determine policy compliance data identifying whether the behavior satisfies one or more policies. The device may calculate a distraction score based on the distraction data and the video data, and may calculate a policy compliance score based on the policy compliance data and vehicle data. The device may perform one or more actions based on the distraction score and the policy compliance score.

Systems and methods for identifying distracted driver behavior from video

A load planning platform may generate, according to one or more loading rules, a preliminary packing solution that simulates placing unpacked items, in a set of items, into a container. The load planning platform may generate a set of packing solutions by applying one or more available moves to the preliminary packing solution. The load planning platform may select a final packing solution from the set of packing solutions based on one or more optimization criteria associated with the container and the set of items. The load planning platform may provide access to a three-dimensional rendering of the final packing solution that differentiates each item in the set of items based on a sequence in which the set of items are to be unloaded from the container.

Load planning optimization using automated 3D packing

A device may receive sensor data and video data associated with a vehicle, and may process the sensor data, with a rule-based detector model, to determine whether a probability of a vehicle accident satisfies a first threshold. The device may preprocess acceleration data of the sensor data to generate calibrated acceleration data, and may process the calibrated acceleration data, with an anomaly detector model, to determine whether the calibrated acceleration data includes anomalies. The device may filter the sensor data to generate filtered sensor data, and may process the filtered sensor data and anomaly data, with a decision model, to determine whether the probability of the vehicle accident satisfies a second threshold. The device may process the filtered sensor data, the anomaly data, and the video data, with a machine learning model, to determine whether the vehicle accident has occurred, and may perform one or more actions.

Systems and methods for utilizing models to identify a vehicle accident based on vehicle sensor data and video data captured by a vehicle device

A trip planning system may receive, from a user device, trip data identifying a starting point and a destination point for a trip and feature data identifying weights for trip features. The trip planning system may determine a geographical region of interest based on the starting point and the destination point. The trip planning system may receive viewpoint data identifying viewpoints located within the geographical region and may calculate, based on the feature data, viewpoint relevance scores for the viewpoints. The trip planning system may determine, based on the viewpoint data and the trip data, paths for the trip and driving times for the paths. The trip planning system may calculate an optimized path, from the paths, based on the viewpoint relevance scores and the driving times associated with the paths. The trip planning system may perform one or more actions based on the optimized path.

Systems and methods for determining an optimized path for navigation based on features associated with points of interest

A load planning platform generates a preliminary packing solution based on loading constraints for a three-dimensional container and parameters for a set of items to be loaded into the three-dimensional container, wherein the preliminary packing solution simulates placing unpacked items into the three-dimensional container according to one or more loading rules based on a sequence in which the set of items are to be unloaded from the three-dimensional container. The load planning platform generates a set of packing solutions by applying one or more available moves changing a simulated placement for one or more items in the set of items. The load planning platform selects a final packing solution from the set of packing solutions based on one or more optimization criteria and provides access to a three-dimensional rendering of the final packing solution and instructions for implementing the final packing solution.

A device can receive message data associated with a vehicle tracking unit (VTU). The device can identify, based on the message data, the VTU as an inactive VTU. The device can compute a feature vector, associated with the VTU, based on the message data and on environmental data associated with the VTU. The feature vector can be computed based on identifying the VTU as an inactive VTU. The device can determine, using an inactivity classification model, an inactivity classification associated with the VTU. The inactivity classification can be determined based on providing the feature vector, associated with the VTU, as an input to the inactivity classification model. The device can cause an inactivity action to be performed based on the inactivity classification.

Automatic classification of inactive vehicle tracking unit

Growing community of inventors

Prato, Italy

Andrea Benericetti