Abstract: Disclosed herein are systems and methods for managing traffic rules. In one embodiment, a method of managing traffic rules can comprise generating or updating a semantic map layer based in part on positioning data obtained from one or more edge devices and videos captured by the one or more edge devices. The method can also comprise generating or updating a traffic enforcement layer on top of the semantic map layer. A plurality of traffic rules can be saved as part of the traffic enforcement layer. The method can further comprise generating or updating a traffic insight layer based in part on traffic violations or traffic conditions determined by the one or more edge devices or the server. The traffic insight layer can adjust or provide a suggestion to adjust at least one of the traffic rules based on an impact analysis conducted by the traffic insight layer concerning the traffic rule.

Abstract: Disclosed herein are methods, devices, and systems for automatically detecting a school bus stop-arm violation. For example, one of the methods can comprise capturing videos of a vehicle using a plurality of cameras of a camera hub coupled to a school bus while the school bus is stopped and at least one stop-arm of the school bus is extended. The method can comprise inputting the videos to a vehicle detection deep learning model and to a vehicle tracker running on a control unit communicatively coupled to the camera hub to detect and track the vehicle as the vehicle passes the stopped school bus. The method can further comprise automatically detecting a license plate number of a license plate of the vehicle from portions of the videos using an automated license plate recognition (ALPR) deep learning model running on the control unit and generating an evidence package of the violation.

Abstract: Disclosed herein are methods and systems for automatically validating evidence of traffic violations. One instance of a method comprises receiving an evidence package comprising video frames showing a vehicle involved in a potential traffic violation. The video frames can be input into one or more deep learning models to obtain a plurality of classification results. The method can further comprise generating a score based in part on the classification results and evaluating the score against one or more thresholds to determine whether the evidence package is automatically approved, is automatically rejected, or requires further review.

Abstract: Disclosed herein are methods, devices, and systems for automatically detecting double parking violations. For example, one aspect of the disclosure concerns a method comprising determining a location of a road edge of a roadway from one or more video frames of a video captured by one or more video image sensors of an edge device; determining a layout of one or more lanes of the roadway, including a no-parking lane, based on the road edge; bounding the no-parking lane using a lane bounding polygon; bounding a vehicle detected from the one or more video frames using a vehicle bounding polygon; and detecting a potential double parking violation based in part on an overlap of at least part of the vehicle bounding polygon with at least part of the lane bounding polygon and a determination of whether the vehicle is static or moving.

Abstract: Disclosed herein are methods and devices for determining a pose and motion of a carrier vehicle. In one aspect, a method comprises capturing video(s) of an external environment surrounding the carrier vehicle using a camera of an edge device coupled to the carrier vehicle; determining a full vehicle pose of the carrier vehicle with respect to a keyframe of the video(s) based on visual odometry measurements made using the keyframe and a subsequent video frame captured after the keyframe; determining a vehicle position and motion of the carrier vehicle based on positioning data obtained from a positioning unit of the edge device; and providing the full vehicle pose with respect to the keyframe obtained from the visual odometry measurements and the vehicle position and motion determined from the positioning data to a filter running on the edge device to obtain a fused vehicle pose and motion of the carrier vehicle.

Abstract: Disclosed herein are systems, methods, and devices for detecting traffic lane violations. In one embodiment, a method for detecting a potential traffic violation is disclosed comprising bounding a vehicle detected from one or more video frames of a video in a vehicle bounding box. The vehicle can be detected and bounded using a first convolutional neural network. The method can also comprise bounding, using the one or more processors of the edge device, a plurality of lanes of a roadway detected from the one or more video frames in a plurality of polygons. The plurality of lanes can be detected and bounded using multiple heads of a multi-headed second convolutional neural network. The method can further comprise detecting a potential traffic violation based in part on an overlap of at least part of the vehicle bounding box and at least part of one of the polygons.

Abstract: Disclosed herein are systems, methods, and devices for detecting traffic lane violations. In one embodiment, a method for detecting a potential traffic violation is disclosed comprising bounding a vehicle detected from one or more video frames of a video in a vehicle bounding box. The vehicle can be detected and bounded using a first convolutional neural network. The method can also comprise bounding, using the one or more processors of the edge device, a plurality of lanes of a roadway detected from the one or more video frames in a plurality of polygons. The plurality of lanes can be detected and bounded using multiple heads of a multi-headed second convolutional neural network. The method can further comprise detecting a potential traffic violation based in part on an overlap of at least part of the vehicle bounding box and at least part of one of the polygons.

Abstract: Disclosed herein are methods and systems for automatically validating evidence of traffic violations. One instance of a method comprises receiving an evidence package comprising video frames showing a vehicle involved in a potential traffic violation. The video frames can be input into one or more deep learning models to obtain a plurality of classification results. The method can further comprise generating a score based in part on the classification results and evaluating the score against one or more thresholds to determine whether the evidence package is automatically approved, is automatically rejected, or requires further review.

Abstract: Disclosed herein are systems and apparatus for detecting a traffic violation. In one embodiment, a system for detecting a traffic violation comprises a context camera assembly and a license plate recognition (LPR) camera assembly. The context camera assembly can comprise a context camera housing containing a context camera, a context camera mount configured to mount the context camera housing to an interior of a carrier vehicle, and a context camera skirt coupled to and protruding outwardly from the context camera housing. The license LPR camera assembly can comprise an LPR camera housing containing one or more LPR cameras, an LPR camera mount configured to mount the LPR camera housing to the interior of the carrier vehicle, and one or more LPR camera skirts coupled to and protruding outwardly from the LPR camera housing.

Abstract: Disclosed herein are methods, systems, and apparatus for automated license plate recognition and methods for training a machine learning model to undertake automated license plate recognition. For example, a method can comprise dividing an image or video frame comprising a license plate into a plurality of image patches, determining a positional vector for each of the image patches, adding the positional vector to each of the image patches and inputting the image patches and their associated positional vectors to a text-adapted vision transformer. The text-adapted vision transformer can be configured to output a prediction concerning the license plate number of the license plate.

Abstract: Disclosed herein are methods and systems for managing traffic violation or enforcement data using a distributed ledger. The distributed ledger provides a transparent chain of custody/evidence related to all digital interactions with traffic violation or enforcement data. The distributed ledger can be audited for data accuracy and integrity by nodes making up the system each time one of the nodes interacts with the traffic violation or enforcement data. For example, a digital evidence package related to a traffic violation event can be generated by a node within the system and a package digest can be logged in the distributed ledger beginning with the creation of the digital evidence package and each time that the digital evidence package is processed, modified, or reviewed by nodes within the system.

Abstract: Disclosed herein are systems, methods, and devices for detecting traffic lane violations. In one embodiment, a method for detecting a potential traffic violation is disclosed comprising bounding a vehicle detected from one or more video frames of a video in a vehicle bounding box. The vehicle can be detected and bounded using a first convolutional neural network. The method can also comprise bounding, using the one or more processors of the edge device, a plurality of lanes of a roadway detected from the one or more video frames in a plurality of polygons. The plurality of lanes can be detected and bounded using multiple heads of a multi-headed second convolutional neural network. The method can further comprise detecting a potential traffic violation based in part on an overlap of at least part of the vehicle bounding box and at least part of one of the polygons.

Abstract: Disclosed herein are systems and methods for managing traffic rules. In one embodiment, a method of managing traffic rules can comprise generating or updating a semantic map layer based in part on positioning data obtained from one or more edge devices and videos captured by the one or more edge devices. The method can also comprise generating or updating a traffic enforcement layer on top of the semantic map layer. A plurality of traffic rules can be saved as part of the traffic enforcement layer. The method can further comprise generating or updating a traffic insight layer based in part on traffic violations or traffic conditions determined by the one or more edge devices or the server. The traffic insight layer can adjust or provide a suggestion to adjust at least one of the traffic rules based on an impact analysis conducted by the traffic insight layer concerning the traffic rule.

Abstract: Disclosed are systems and methods for detecting traffic violations using one or more mobile detection devices. Videos captured by one or more mobile detection devices can be processed on the mobile detection devices to extract data and information concerning a potential traffic violation involving a vehicle and a restricted road area. The mobile detection devices can transmit such data and information to a server configured to make a determination as to whether a traffic violation has occurred by comparing the data and information received from the mobile detection devices.

Abstract: Disclosed herein are methods and systems for managing traffic violation or enforcement data using a distributed ledger. The distributed ledger provides a transparent chain of custody/evidence related to all digital interactions with traffic violation or enforcement data. The distributed ledger can be audited for data accuracy and integrity by nodes making up the system each time one of the nodes interacts with the traffic violation or enforcement data. For example, a digital evidence package related to a traffic violation event can be generated by a node within the system and a package digest can be logged in the distributed ledger beginning with the creation of the digital evidence package and each time that the digital evidence package is processed, modified, or reviewed by nodes within the system.

Abstract: Disclosed herein are methods and systems for managing traffic violation or enforcement data using a distributed ledger. The distributed ledger provides a transparent chain of custody/evidence related to all digital interactions with traffic violation or enforcement data. The distributed ledger can be audited for data accuracy and integrity by nodes making up the system each time one of the nodes interacts with the traffic violation or enforcement data. For example, a digital evidence package related to a traffic violation event can be generated by a node within the system and a package digest can be logged in the distributed ledger beginning with the creation of the digital evidence package and each time that the digital evidence package is processed, modified, or reviewed by nodes within the system.

Abstract: Disclosed herein are systems and apparatus for detecting a traffic violation. In one embodiment, a system for detecting a traffic violation comprises a context camera assembly and a license plate recognition (LPR) camera assembly. The context camera assembly can comprise a context camera housing containing a context camera, a context camera mount configured to mount the context camera housing to an interior of a carrier vehicle, and a context camera skirt coupled to and protruding outwardly from the context camera housing. The license LPR camera assembly can comprise an LPR camera housing containing one or more LPR cameras, an LPR camera mount configured to mount the LPR camera housing to the interior of the carrier vehicle, and one or more LPR camera skirts coupled to and protruding outwardly from the LPR camera housing.

Abstract: Disclosed herein are systems, methods, and devices for detecting traffic lane violations. In one embodiment, a method for detecting a potential traffic violation is disclosed comprising bounding a vehicle detected from one or more video frames of a video in a vehicle bounding box. The vehicle can be detected and bounded using a first convolutional neural network. The method can also comprise bounding, using the one or more processors of the edge device, a plurality of lanes of a roadway detected from the one or more video frames in a plurality of polygons. The plurality of lanes can be detected and bounded using multiple heads of a multi-headed second convolutional neural network. The method can further comprise detecting a potential traffic violation based in part on an overlap of at least part of the vehicle bounding box and at least part of one of the polygons.

Abstract: Disclosed are systems and methods for detecting traffic violations using one or more mobile detection devices. Videos captured by one or more mobile detection devices can be processed on the mobile detection devices to extract data and information concerning a potential traffic violation involving a vehicle and a restricted road area. The mobile detection devices can transmit such data and information to a server configured to make a determination as to whether a traffic violation has occurred by comparing the data and information received from the mobile detection devices.

Abstract: Disclosed herein are methods and systems that allow users to report traffic violations and obtain information concerning reported violations using mobile devices. In one embodiment, a method comprises receiving, at a server, one or more images or videos captured by a mobile device of an offending vehicle committing a potential traffic violation and an extended metadata tag; storing the one or more images or videos and the extended metadata tag as unstructured content in a content lake; generating database tables on top of the content lake; receiving a request from another mobile device to view traffic violation content; querying the database tables and retrieving at least one of the one or more images or videos of the offending vehicle committing the potential traffic violation from the content lake; and generating a GUI to be displayed via the other mobile device showing the traffic violation content.