Verizon Connect Development Limited

Verizon Patent and Licensing Inc.

Verizon Connect Development Limted

Douglas Coimbra De Andrade

Leonardo Taccari

Francesco Sambo

Matteo Simoncini

Luca Bravi

Stefano Caprasecca

Fabio Schoen

Luca Kubin

Aurel Pjetri

Leonardo Sarti

Andrea Benericetti

Samuele Salti

Tommaso Mugnai

Mourya C Darivemula

Aaroon Thowfiq Shahul Hameed

Vipul Shyam Javeri

Giovanni Morini

Nishitha Reddy Nalla

Andrea Basile

Simone Magistri

Ianemmanuel P Crueldad

Jin Soo Cho

Jeyanth Paul John Britto

Vidhya Seran

Tommaso Bianconcini

Jerry Gamble, Jr

Alex Quintero Garcia

Niccolo Bellaccini

In some implementations, a video system may capture, from a camera mounted to a vehicle, a video of a portion of a road on which the vehicle is traveling. The video system may detect, in the video, a driving lane associated with the road on which the vehicle is traveling. The video system may detect, in the video, multiple other vehicles within the driving lane. The video system may determine, for each of the multiple other vehicles within the driving lane, a bounding box that substantially surrounds an image of the other vehicle, resulting in a plurality of bounding boxes. The video system may determine a region in the video corresponding to an area to be driven by the vehicle based on the plurality of bounding boxes.

Systems and methods for detecting a driving area in a video

A device may receive a plurality of narratives associated with a plurality of scenes and an image identifying a scene not included in the plurality of scenes, and may process the image, with a classifier model, to detect a plurality of features in the image. The device may replace keywords in the plurality of narratives, with tags, to generate a plurality of sentences, and may group similar sentences of the plurality of sentences, based on a defined measure of dissimilarity, into clusters of templates. The device may select a candidate template from each of the clusters to generate a set of candidate templates, and may select a template from the set of candidate templates. The device may populate tags of the template with the plurality of features detected in the image to generate an image caption, and may provide the image and the image caption for display.

Systems and methods for utilizing a neural network model and templates to generate an image caption

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 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 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

In some implementations, a device may receive an image that depicts an environment associated with a vehicle. The device may partition the image into a plurality of subsections. The device may analyze the plurality of subsections to determine respective subsection information, wherein subsection information, for an individual subsection, indicates: a probability score that the subsection includes a line segment associated with an object class, a position of a representative point of the line segment, and a direction of the line segment. The device may identify, based on the respective subsection information of the plurality of subsections, a line associated with the object class that is associated with a set of subsections of the plurality of subsections. The device may perform one or more actions based on identifying the line associated with the object class.

Systems and methods for detecting objects in an image of an environment

In some implementations, a device may receive image data and sensor data associated with the image data. The image data may depict a series of scenes that are captured over a time period and that are associated with an occurrence of an event. The device may determine a time of interest, within the time period, at which a value of the sensor data satisfies one or more criteria. The device may extract a portion of the image data based on the time of interest. The device may extract, from the portion of the image data, a slice of image data depicting a first set of scenes and a slice of image data depicting a second set of scenes. The device may train, based on the slices of image data, a model to predict a probability of an occurrence of an event.

Systems and methods for training event prediction models for camera-based warning systems

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 vehicle device may receive audio data and other vehicle data associated with a vehicle and may transform the audio data to transformed audio data in a frequency domain. The vehicle device may segment the transformed audio data into a plurality of audio segments and may process the plurality of audio segments, with different feature extraction techniques, to extract a plurality of feature vectors. The vehicle device may merge the plurality of feature vectors into a merged feature vector and may create an audio signature for the audio data based on the merged feature vector. The vehicle device may process the audio signature and the other vehicle data, with a model, to determine a classification of the audio signature and may perform one or more actions based on the classification of the audio signature.

Systems and methods for utilizing models to predict hazardous driving conditions based on audio data

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 include one or more processors configured to receive sensor data from an external sensor associated with an autonomous driving zone; generate a map of the autonomous driving zone based on the sensor data; determine that a vehicle is entering the autonomous driving zone; and transmit a signal to the vehicle based on the vehicle entering the autonomous driving zone. The signal may cause the vehicle to begin operating in a full autonomous mode. The one or more processors may be configured further to determine an objective associated with the vehicle; receive first data obtained by one or more first sensors associated with the vehicle and second data obtained by the external sensor; determine a route based on the objective, the first data, and the second data; and transmit information identifying the route to the vehicle. The route may allow the vehicle to satisfy the objective.

Systems and methods for externally assisted self-driving

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.

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Florence, Italy

Douglas Coimbra De Andrade