Verizon Connect Development Limited

Verizon Patent and Licensing Inc.

Verizon Connect Ireland Limited

Tommaso Bianconcini

Leonardo Taccari

Leonardo Sarti

David Di Lorenzo

Francesco Sambo

Andrea Benericetti

Alessio Frusciante

Luca Bravi

Stefano Caprasecca

Tommaso Mugnai

Fabio Schoen

Luca Kubin

Tommaso Innocenti

Marco Gualtieri

Vidhya Seran

Douglas Coimbra De Andrade

Alessandro Lori

Peter Mitchell

Aurel Pjetri

Jerry Gamble, Jr

Luca Noce

Alessio Sanfratello

Giovanni Pini

Paolo Raiconi

Enrico Civitelli

Simone Magistri

A device may receive video data and corresponding GPS data and IMU data associated with a vehicle, and may remove video frames from the video data to generate modified video data. The device may select objects and image regions of video frames of the modified video data, and may determine a current speed and a current turn angle of the vehicle based on the GPS data, the IMU data, and the modified video data. The device may mask the objects of the video frames of the modified video data to learn first features, and may mask the image regions of the video frames of the modified video data to learn second features. The device may generate a trained neural network model based on the current speed, the current turn angle, the first features, and the second features, and may implement the trained neural network model in the vehicle.

Systems and methods for training a driving agent based on real-world driving data

A device may obtain video data associated with a driving event involving a first vehicle. The device may determine a vanishing point associated with the video data and may construct a cone of impact of the first vehicle based on the vanishing point. The device may detect a second vehicle within the cone of impact and may analyze the subset of video frames to determine a distance between the first vehicle and the second vehicle. The device may determine a speed of the first vehicle during a time period associated with a subset of video frames. The device may determine a headway score, representative of a severity associated with the first vehicle being within a proximity threshold of the second vehicle during the time period, based on the distance and the speed. The device may determine an occurrence of a tailgating event based on the headway score.

Systems and methods for detecting vehicle tailgating

A device may receive accelerometer data and video data for a vehicle and may identify bounding boxes and object classes for objects near the vehicle. The device may identify tracks for the objects and may filter out tracks that are not associated with vehicles or vulnerable road users to generate one or more tracks or an indication of no tracks. The device may generate a collision cone identifying a drivable area of the vehicle to identify objects more likely to be involved in a collision and may filter out tracks from the one or more tracks, based on the bounding boxes, and to generate a subset of tracks or another indication of no tracks. The device may determine scores for the subset of tracks and may identify a track of the subset of tracks with a highest score. The device may perform actions based on the identified track.

Systems and methods for utilizing models to detect dangerous tracks for vehicles

In some implementations, an adverse environment detection system may receive an image of a road scene associated with a vehicle. The adverse environment detection system may determine a set of features associated with the image based on providing the image to an initial portion of a model. The adverse environment detection system may determine a first condition associated with the image based on providing the set of features to a first processing layer of the model, a second condition associated with the image based on providing the set of features to a second processing layer of the model, and a third condition associated with the image based on providing the set of features to a third processing layer of the model. The first processing layer, the second processing layer, and the third processing layer may process the set of features in parallel.

Systems and methods for utilizing machine learning for vehicle detection of adverse conditions

A device may receive schedule data identifying schedules of appointments for drivers of vehicles and may receive location data identifying geographical locations of the vehicles. The device may receive traffic data identifying traffic conditions, and may process the received data, with a first model, to determine status data identifying estimated statuses of the appointments. The device may process the received data, with a second model, to generate sets of isochrones for destinations of the appointments, and may calculate, based on the sets of isochrones and the location data, estimated travel time data identifying estimated travel times for the appointments. The device may process the status data, the estimated travel time data, and the schedule data, with a third model, to calculate estimated time of arrival data identifying estimated times of arrival for the appointments, and may perform actions based on the estimated time of arrival data.

Systems and methods for utilizing models to determine real time estimated times of arrival for scheduled appointments

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 device can receive, from a client device, a request to modify a schedule that includes information identifying the set of deliveries that the fleet of vehicles is to perform. The device can generate a set of modified schedules using information included in the request and one or more routing techniques. The device can determine, for each modified schedule, of the set of modified schedules, one or more scores for ranking the set of modified schedules, such as a projected delivery time score, an operational cost score, and/or an overall score. The device can provide the set of modified schedules that have been scored to the client device. The device can receive a modified schedule that has been selected by the client device. The device can deploy the selected modified schedule to instruct the fleet of vehicles to perform an updated set of deliveries associated with the selected modified schedule.

Logistics management platform for modifying schedules in real-time

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 method for finding an approximation to the all-pairs shortest travel path between a number of predetermined nodes, comprising clustering nodes of an original road network into a plurality of subgroups so that the number of nodes in each subgroup is no greater than a first predetermined threshold and the maximum distance between any two nodes of a subgroup is no greater than a second predetermined threshold; adding information of intra group shortest paths for all pairs in a same subgroup to a newly created higher level road network; adding information of inter group shortest paths for the plurality of subgroups to the same road network; and searching the same road network for the shortest travel path. In those cases in which the path returned is not exact, the path represents one in the original map, even if not necessarily the best one.

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Florence, Italy

Tommaso Bianconcini