Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, streaming and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework that automatically and dynamically determines and prioritizes, and updates tasks at a scale incapable of being performed without machine learning or modern technology. The framework provides systems and methods for the management of a work flow whereby tasks, subtasks and the behavior and interactions of entities performing those operations are monitored to determine which tasks are in progress, which are completed, which are safe and which are to be rescheduled.

Abstract: 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.

Abstract: A device may receive an image. The device may utilize an episodic memory to determine a first classification associated with the image. The device may utilize a semantic memory to determine a second classification associated with the image. The device may determine an accuracy associated with utilizing the semantic memory to determine the second classification. The device may determine that the image is associated with the first classification when the accuracy fails to satisfy a threshold accuracy. The device may determine that the image is associated with the second classification when the accuracy satisfies the threshold accuracy.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present invention relates to a robust method for optical recognition of markers in an outdoor environment. In this context, the present invention provides a method for optical recognition of optical markers comprising the steps of: acquiring an image; identifying regions of contiguous colours in the image by flood filling; extracting data and parameters of the contiguous regions; and detecting an optical marker by means of a convex hull algorithm and prediction of position of squares based on the data and parameters extracted from the contiguous Thus, the method of the present invention allows identification of markers, such as chequerboards and targets, unequivocally and with enough robustness as regards partial occlusions and variations of illumination.