Systems, methods, devices and computer readable media for determining a geographical location of a license plate are described herein. A first image of a license plate is acquired by a first image acquisition device of a camera unit and a second image of the license plate is acquired by a second image acquisition device of the camera unit. A three-dimensional position of the license plate relative to the camera unit is determined based on stereoscopic image processing of the first image and the second image. A geographical location of the camera unit is obtained. A geographical location of the license plate is determined from the three-dimensional position of the license plate relative to the camera unit and the geographical location of the camera unit. Other systems, methods, devices and computer readable media for detecting a license plate and identifying a license plate are described herein.

A first 2D recording of a specified reference view of an object is captured by a camera and, starting from the first 2D recording, a user's starting location relative to the object is ascertained by a computer vision module. Starting from the origin of a coordinate system as the starting location of the camera, one or more specified and/or settable relative positions in the vicinity of the object and/or in the object are determined as one or more locations for the respective perspective of the camera for taking at least one second 2D recording. The respective location in an object view on a display of the camera is displayed by a respective first augmented reality marker on the ground and/or on the object. The alignment of the camera with regard to angle and rotation with the perspective corresponding to the respective location is performed in this case by second augmented reality markers as auxiliary elements.

Handling personally identifiable information (PII) in data streams is provided. Processed sensor data is received, from a plurality of vehicles including sensors capturing raw sensor data, the raw sensor data including captured PII and non-PII. The processed sensor data includes simulated PII created based on the captured PII and one or more layers of the captured PII corresponding to the simulated PII. A request is received from a client device for a portion of the processed sensor data. Access keys corresponding to the request are identified. A result is constructed according to the access keys using the processed sensor data. The constructed result is sent to the client device responsive to the request.

A controlled intersection employs video analytics to identify incoming vehicles coupled with autonomous driving capabilities in the vehicle to selectively provide intervention for collision avoidance. A camera image of an approaching vehicle is used to identify a range and speed, and to compute whether intervention is appropriate based on a detected distance and speed from the intersection. A vehicle approaching a stop signal (e.g. “red light”) at an unsafe rate of speed triggers an invocation of on-board autonomous systems in the vehicle that provide appropriate warnings and ultimately, forced braking if warnings go unheeded. A registration system maintains a local grouping of vehicles in proximity to an intersection for minimizing latency in vehicle identification for commencing intervention. In this manner, on-board vehicle collision avoidance systems collaborate with complementary traffic control logic at a controlled intersection for preventing inadvertent or intentional disregard of a red signal.

A license plate number recognition method includes: extracting license plate number features of an image to be recognized including a license plate number, through a pre-trained convolutional neural network; extracting an intermediate convolution result during extracting the license plate number features, and extracting a first verification feature and/or a second verification feature according to the intermediate convolution result; verifying whether the license plate number features are correct according to the first and/or second verification features; if correct, outputting a predicted license plate number result according to the license plate number features. During the feature extraction process of the license plate number features, an intermediate feature is extracted as a verification feature to verify whether the extracted license plate number features are correct, and only when the verification is passed, outputting the license plate number result, which reduces the output error rate of the license plate number recognition result.

A system for checking an operating state of a component on a vehicle, in particular on a commercial vehicle, comprising a detection device which detects information in the form of a data set, in particular an image data set, in a defined region on the vehicle, the defined region comprising the component to be checked, and an evaluation device to which the data set is provided, wherein the evaluation device is configured to determine the operating state of the component on the basis of the detected data set, wherein the detection device is arranged outside the vehicle.

A system and method of automated logistical vehicle registration and validation for remote monitoring. Cameras are deployed at the gate to a trucking logistics facility to record detailed information (e.g., license plate, identification info) on vehicles. Alternatively, vehicle and drive identification credentials can be provided by a mobile application from the driver. The cameras are connected to a data center hosting the automated logistical vehicle registration and validation system. Cameras are placed around the entry gate of a facility that takes snapshots of vehicles “checking in”. The system takes this info with assistance from artificial algorithms to read data and images (e.g., photos) to compare and verify the info. Recommendations are then sent to a guard to approve entry of the vehicle.

An apparatus and a method correct a level of a headlamp, in which a sight of the driver may be sufficiently secured while an influence on a sight of a driver of a preceding vehicle is minimized by specifying a level of a cutoff line of the headlamp using a transverse length and a longitudinal length of a number plate frame image. The specified level is compared with a level of a target line calculated based on a change of an inter-vehicle distance. The method includes determining whether it is necessary to perform a correction of raising or lowering the level of the headlamp, and a correcting the level of the headlamp such that the cutoff line of the headlamp follows the level of the target line at the corresponding inter-vehicle distance.

In some aspects, an edge appliance is placed in an active mode and causes a software agent that is based on a machine learning algorithm to engage in a conversation to take an order from a customer that is located at an order post. The edge appliance provides, using a communication interface, audio data that includes the conversation, to a communications system of a restaurant. The edge appliance provides, using the communication interface, a content of a cart associated with the order to a point-of-sale terminal of the restaurant. If the edge appliance determines, using the communication interface, that a microphone of the communication system is receiving audio input from an employee, the edge appliance automatically transitions the edge appliance from the active mode to an override mode, enabling the employee to receive a remainder of the order from the customer.

A violation inspection system based on visual sensing of a self-driving vehicle and a method thereof are disclosed. The violation inspection system receives traffic images captured by the self-driving vehicle, identifies image objects by artificial intelligence, continuously detects a movement status of each of the image objects, to generate image object dynamics; when the image object matches one of a trigger object factor and an association object factor, the image object and the image object dynamics thereof are inputted to an AI-based situation recognition model, to output a current situation, and when the current situation matches a violation situation template, the image objects corresponding to the trigger object factor and the association object factor corresponding to the violation situation template, and a violation item are outputted, so as to achieve the technical effect of improving accuracy and diversity in violation behavior determination.