Verizon Connect Development Limited

Verizon Connect Ireland Limited

Verizon Patent and Licensing Inc.

Stefano Caprasecca

Luca Bravi

Matteo Simoncini

Leonardo Taccari

Francesco Sambo

Leonardo Sarti

Douglas Coimbra De Andrade

Tommaso Mugnai

Luca Kubin

Tommaso Bianconcini

Fabio Schoen

Simone Magistri

Samuele Salti

Alessandro Lori

Andrea Benericetti

David Di Lorenzo

Francesco De Felice

Gabriele Bianchi

Salvatore Impellizzieri

A device may receive video data identifying videos associated with one or more unsafe driving events by a driver of a vehicle, and may process the video data, with a machine learning model, to determine classifications for the videos. The device may assign tags to the videos based on the classifications, and may calculate event severity scores based on the classifications. The device may calculate tag scores based on the tags assigned to the videos, and may calculate time-to-contact scores, box cross scores, day/night scores, weather scores, and road condition scores based on the video data. The device may calculate video risk scores for the videos based on the event severity scores, the tag scores, the time-to-contact scores, the box cross scores, the day/night scores, the weather scores, and the road condition scores, and may provide one or more of the video risk scores for display.

Systems and methods for determining video similarity, risk scores, and textual descriptions

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 system may determine, for each frame of a plurality of frames of image data associated with a vehicle, a probability that each frame of the plurality of frames that includes an image of a stop sign is relevant to the vehicle. The system may determine a longest sequence of consecutive frames of the plurality of frames for which the probability satisfies a probability threshold. The system may determine a maximum probability associated with a frame included in the longest sequence of consecutive frames. The system may determine a time window based on a time associated with the frame associated with the maximum probability. The system may determine location data and sensor data for the vehicle based on the time window. The system may determine an occurrence of a stop sign violation based on the location data and the sensor data.

System for automatic stop sign violation identification

A device may receive a first image. The device may process the first image to identify an object in the first image and a location of the object within the first image. The device may extract a second image from the first image based on the location of the object within the first image. The device may process the second image to determine at least one of a coarse-grained viewpoint estimate or a fine-grained viewpoint estimate associated with the object. The device may determine an object viewpoint associated with the second vehicle based on the at least one of the coarse-grained viewpoint estimate or the fine-grained viewpoint estimate. The device may perform one or more actions based on the object viewpoint.

Systems and methods for utilizing a deep learning model to determine vehicle viewpoint estimations

A violation detection platform may obtain image data, location data, and sensor data associated with a vehicle. The violation detection platform may determine a probability that a frame of the image data includes an image of a stop sign. The violation detection platform may determine that the probability satisfies a probability threshold. The violation detection platform may identify location data and sensor data associated with the frame of the image data based on the probability satisfying the probability threshold. The violation detection platform may determine an occurrence of a type of a stop sign violation based on the probability, the location data, and the sensor data. The violation detection platform may perform one or more actions based on determining the occurrence of the type of the stop sign violation.

A device may generate a shortest path tree based on a passenger starting point identified in driving data. The device may generate a graph with a first layer and a second layer that correspond to the shortest path tree and may add paths from nodes in the first layer to corresponding nodes in the second layer. The device may identify a first shortest path that starts from a driver starting node in the first layer and ends at a passenger end node in the second layer and a second shortest path that starts from the passenger end node and ends at a driver end node in the second layer. The device may calculate a shared cost associated with the driver and the passenger sharing a ride based on the first shortest path and the second shortest path. The device may generate a recommendation based on the shared cost.

Systems and methods for utilizing machine learning models to determine suggested ride sharing of vehicles

A rating platform can identify a point of interest (POI) among a first plurality of POIs included in a stop cluster. The rating platform can generate a plurality of unweighted user scores for the POI. Respective unweighted user scores, of the plurality of unweighted user scores is associated, can be associated with respective users of a plurality of users. The rating platform can generate a plurality of weighted user scores for the POI based on the plurality of unweighted user scores and respective weights assigned to respective users of the plurality of users. The rating platform can generate a POI score for the POI based on the plurality of weighted user scores. The rating platform can transmit, based on the POI score, an instruction to display, on the client device, information associated with the POI.

Objective generation of a point of interest score based on quantities of user stops

A device receives location data that identifies geographic locations of a vehicle at corresponding times, and speed data that identifies respective speeds of the vehicle at the set of corresponding times. The device identifies, based on the location data, speed limit data that identifies respective speed limits at the geographic locations. The device determines, based on the respective speeds and the respective speed limits, a set of respective relative speed values associated with the vehicle. The device identifies respective relative speed values, of the set of respective relative speed values, that exceed a threshold. The device determines, based on the respective relative speed values, a univocal speed indicator that represents an overall degree to which the respective relative speed values exceed the threshold, during a time interval that includes the corresponding times. The device performs a set of actions based on the univocal speed indicator.

Systems and methods for improved driver safety analytics

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.

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Florence, Italy

Stefano Caprasecca