Verizon Connect Development Limited

Verizon Patent and Licensing Inc.

Verizon Connect Ireland Limited

Verizon Connect Telo Inc.

Leonardo Sarti

Leonardo Taccari

Francesco Sambo

Matteo Simoncini

Luca Bravi

Samuele Salti

Alessandro Lori

Tommaso Bianconcini

Stefano Caprasecca

Andrea Benericetti

Douglas Coimbra De Andrade

Fabio Schoen

Luca Kubin

Jason Mathew Koch

Tommaso Innocenti

Nicola Tommasi

Valerio Arena

Vidhya Seran

Peter Mitchell

Aurel Pjetri

Jerry Gamble, Jr

Wan Luo

Yaoyang Qian

John Molamphy

Enrico Civitelli

Simone Magistri

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 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 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 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 can receive a request for a schedule that assigns a fleet of vehicles to a set of deliveries. The device can determine that a parameter is not included in the request that is needed to generate a new schedule or that is needed to generate an existing schedule. The device can obtain the parameter using a historical user request, a historical schedule, or a scheduling template. The device can generate or obtain the schedule based on information included in the request and the obtained parameter. The device can provide the schedule to a user device and/or to one or more devices associated with the fleet of vehicles carrying out the set of deliveries. The device can modify the schedule based on a trigger. The device can provide the modified schedule to the user device and/or to the one or more devices associated with the fleet of vehicles.

Scheduling and management of deliveries via a virtual agent

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 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 maneuver classification platform may obtain video data and telematic data that are associated with a driving event involving a vehicle. The maneuver classification platform may analyze the video data to identify a path of the vehicle along a roadway during the driving event, and based on markings of a plurality of roadways. The maneuver classification platform may analyze the video data to determine a type of a vehicle maneuver performed by the vehicle during the driving event. The maneuver classification platform may determine a maneuver score of the driving event based on the type of the vehicle maneuver, the path of the vehicle, and the telematic data. The maneuver classification platform may send a message associated with the maneuver score and a vehicle identifier of the vehicle to a client device to permit the client device to use the maneuver score.

Systems and methods for detecting and classifying an unsafe maneuver of a vehicle

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 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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Sesto Fiorentino, Italy

Leonardo Sarti