Verizon Connect Development Limited

Verizon Connect Ireland Limited

Verizon Patent and Licensing Inc.

Verizon Connect Telo Inc.

Verizon Connect Development Limted

Luca Bravi

Leonardo Taccari

Francesco Sambo

Matteo Simoncini

Samuele Salti

Alessandro Lori

Leonardo Sarti

Stefano Caprasecca

Douglas Coimbra De Andrade

Luca Kubin

Tommaso Mugnai

Andrea Benericetti

Jason Mathew Koch

Peter Mitchell

Tommaso Bianconcini

Fabio Schoen

Aurel Pjetri

Simone Magistri

Alessandro Giannini

Wan Luo

Yaoyang Qian

John Molamphy

Alex Quintero Garcia

Andrew Harbourne-Thomas

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 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

In some implementations, a device may receive video data associated with video frames that depict an environment of a vehicle. The device may identify an object depicted in the video frames, wherein an object detection model indicates bounding boxes associated with the object. The device may determine, based on the bounding boxes, a configuration of the bounding boxes within the video frames. The device may determine, based on the configuration of the bounding boxes, that the object is a vulnerable road user (VRU) that is in the environment. The device may determine a trajectory of the VRU based on a change in the configuration between video frames of a set of the video frames. The device may determine, based on the trajectory, a probability of a collision between the VRU and the vehicle. The device may perform, based on the probability, an action associated with the vehicle.

Systems and methods for detecting a vulnerable road user in an environment of a vehicle

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 receive geographical data identifying a geographical area, and may receive, from vehicle devices of vehicles, first vehicle data identifying engine off conditions, locations during engine off conditions, and durations of the engine off conditions. The device may divide, based on the geographical data, the geographical area into clusters with particular dimensions, and may process data identifying the clusters and the first vehicle data, with a machine learning model, to determine parking data identifying public parking spaces in the geographical area. The device may receive, from a set of the vehicle devices associated with vehicles parked in the public parking spaces, vehicle data identifying engine on conditions and locations during the engine on conditions, and may identify available public parking spaces based on the second vehicle data and the parking data. The device may perform one or more actions based on data identifying the available public parking spaces.

Systems and methods for utilizing a machine learning model to identify public parking spaces and for providing notifications of available public parking spaces

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 method and system for identifying recurrent stops of a vehicle fleet having a plurality of vehicles. The method comprises retrieving historical GPS tracks of the vehicle fleet over a period of time; detecting stops made by the vehicle fleet along travelled routes that are associated with the historical GPS tracks; constructing a coverage area that covers the travelled routes; discretizing the coverage area into a plurality of cells; determining whether a cell is a recurrent stop based on a fleet stay period associated with that cell; and classifying a determined recurrent stop into a plurality of categories.

System and method for detecting and classifying recurrent stops of a vehicle fleet

A method and system for classifying a vehicle based on low frequency GPS tracks. The method and system comprise retrieving a low frequency GPS track having a sampling interval of at least 20 seconds; deriving additional data from the low frequency GPS track, the additional data including interval speed and instantaneous acceleration of the vehicle; extracting a plurality of data sets from the low frequency GPS track and the additional data; generating a plurality of features from the extracted data sets; and providing the plurality of generated features to a classifier that classifies the vehicle into a predetermined class.

System and method for determining a vehicle classification from GPS tracks

A device receives location information and work order information associated with multiple vehicles, and groups the location information into engine off information, idling information, and journey information. The device combines, based on the journey information, the engine off information and the idling information to generate vehicle stop information associated with the plurality of vehicles, and matches corresponding work order information with the vehicle stop information to generate matched information. The device extracts stop-wise features, points of interest features, stop cluster features, and sequential features from the vehicle stop information, and utilizes the stop-wise features, the points of interest features, the stop cluster features, and the sequential features with a model to determine work order stops and non-work order stops for the multiple vehicles. The device provides information associated with the work order stops and the non-work order stops for the multiple vehicles.

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Scandicci, Italy

Luca Bravi