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 and methods for alleviating traffic congestion within an enforcement zone. In some embodiments, a system for alleviating traffic congestion within an enforcement zone comprises a server and a plurality of edge devices coupled to carrier vehicles deployed within the enforcement zone. The edge devices can be configured to capture videos of vehicles that may be subject to the traffic congestion pricing policy and to determine their locations, license plate numbers, and certain vehicle attributes using certain sensors on the edge devices and deep learning models running on the edge devices. The edge devices can also be configured to transmit evidence packages to the server concerning such vehicles. The server can be configured to evaluate the evidence packages received from the edge devices and prepare final evidence packages concerning the vehicles for further review and processing.

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 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: Methods, systems and apparatus, including computer programs encoded on computer storage medium, for processing parts using machine tool workstations. The machine tool workstations are grouped into multiple groups, where each group includes workstations of the same type. For each of the multiple groups, data representing parts to be processed by the type of workstations included in the group are transmitted to a combinatorial optimization problem solver. Data representing a sequence of ordered subsets of the multiple parts that produce a reduced setup time for the workstations in the group are received from the solver. An average delay time associated with the generated sequence of ordered subsets of the collection of parts is computed. If the average delay time is less than the predetermined threshold, parts are released according to the generated sequence of ordered subsets of the collection of parts to the group for processing.

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 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: A display device includes a display panel, a first light source, and second light source. The first and second light sources are respectively aligned with a first spot and second spot in a direction perpendicular to a substrate. The first light source irradiates the first and second spots with luminance of a first value and second value, respectively. The first light source irradiates a midpoint between the first and second spots with a luminance value that is half the addition of the first and second values.

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: Platform-agnostic digital assistant methods and apparatuses are provided. The apparatuses and methods include a processor configured by a memory to receive user data from a storage, wherein the storage includes informational contents, establish a communication channel with an entity, utilize the communication channel to exchange a plurality of information related at least to a user and a language component between the processor and the entity, extract at least one user datum from the plurality of information by analyzing the second portion of the plurality of information, modify the informational contents of the storage based on at least one of the user datum and the plurality of information, generate a task datum as a function of the modified informational contents and transmit the task datum to the entity.

Abstract: An apparatus for automatically generating a profile evaluation is disclosed. The apparatus comprises at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to extract a user profile from a user. The memory then instructs the processor to generate a verified user profile as a function of the user profile. The memory instructs the processor to identify at least one evaluation factor associated with the verified user profile. The memory additionally instructs the processor to generate a profile evaluation as a function of the at least one evaluation factor. The memory instructs the processor to display the profile evaluation using a display device.

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 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 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: Platform-agnostic digital assistant methods and apparatuses are provided. The apparatuses and methods include a processor configured by a memory to receive user data from a storage containing informational contents. The processor is configured to establish a communication channel with an entity and utilize the communication channel to receive first information related to a user. The processor is configured to extract at least one user datum from the first information and modify the informational contents of the storage based on at least one of the user datum and the first information. The processor is further configured to initiate an action related to the user based on at least one of the first information, the user datum, and the informational contents.