Verizon Connect Development Limited

Luca Kubin

Leonardo Taccari

Francesco Sambo

Luca Bravi

Matteo Simoncini

Douglas Coimbra De Andrade

Stefano Caprasecca

Leonardo Sarti

Tommaso Bianconcini

Andrea Benericetti

Fabio Schoen

Tommaso Innocenti

Simone Magistri

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 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 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.

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Collecchio, Italy

Luca Kubin