One or more devices may obtain traffic data, such as video from intersection cameras, point cloud data from Light Detection and Ranging (or “LiDAR”) sensors, and so on. Metrics may be calculated from the traffic data. For each frame, the metrics may be analyzed to detect whether a near miss/collision occurs between each object in the frame (such as motorized or non-motorized vehicles, pedestrians, and so on) and each of the other objects in the frame. These metrics may be analyzed to evaluate whether or not a group of conditions are met. If the group of conditions are met, a near miss/collision may be detected. This may be recorded in the metrics for the objects involved. In some implementations, one or more indicators may be added to the traffic data and/or to one or more visualizations generated using the metrics, the traffic data, the structured data, and so on.

Control system and method for managing transport of vehicles in a warehouse. A network of cameras provide coverage over the route way network by capturing images and sending image data to a central control unit which processes the images and generates signals to control the movement of robot slaves. The control system also includes a calibration mechanism to calibrate a map of the network of routes and an obstruction matrix function. The robot slaves include a safety override mechanism to control the robot slaves autonomously and independently in case of detecting an obstacle or an unexpected hazard in a path of its movement along a route of the warehouse network.

Control system and method for managing transport of vehicles in a warehouse. A network of cameras provide coverage over the route way network by capturing images and sending image data to a central control unit which processes the images and generates signals to control the movement of robot slaves. The control system also includes a calibration mechanism to calibrate a map of the network of routes and an obstruction matrix function. The robot slaves include a safety override mechanism to control the robot slaves autonomously and independently in case of detecting an obstacle or an unexpected hazard in a path of its movement along a route of the warehouse network.

Provided are an urban digital twin platform system and a moving object information analysis and management method therefor. Through the urban digital twin platform system and the moving object information analysis and management method, a moving object such as a vehicle or a pedestrian may be detected from multimodal sensor data, data on the moving object may be generated, and a situation may be quickly determined by deriving complex actions of the moving object. The urban digital twin platform system includes a multimodal sensor data input and objectification module configured to detect a moving object and to generate objectification data, a multimodal sensor data analysis module configured to classify basic actions of the moving object, to classify complex actions of the moving object, and to generate moving object information, and an urban space data server configured to store the objectification data and the moving object information.

Provided are an urban digital twin platform system and a moving object information analysis and management method therefor. Through the urban digital twin platform system and the moving object information analysis and management method, a moving object such as a vehicle or a pedestrian may be detected from multimodal sensor data, data on the moving object may be generated, and a situation may be quickly determined by deriving complex actions of the moving object. The urban digital twin platform system includes a multimodal sensor data input and objectification module configured to detect a moving object and to generate objectification data, a multimodal sensor data analysis module configured to classify basic actions of the moving object, to classify complex actions of the moving object, and to generate moving object information, and an urban space data server configured to store the objectification data and the moving object information.

A vehicle parking management method, comprising: on at least one of a vehicle and a mobile device, acquiring current parking place information and a plurality of additional information of a parking lot, the current parking place information at least comprising a location of an unoccupied parking place, each additional information in the plurality of additional information being configured for identifying associated data; on the basis of the current parking place information and the additional information, generating a parking place distribution image, the parking place distribution image at least indicating the unoccupied parking place and the additional information; presenting the parking place distribution image; and in response to that an operation targeting the unoccupied parking place or the additional information is detected, generating a navigation indication related to a target unoccupied parking place.

A vehicle parking management method, comprising: on at least one of a vehicle and a mobile device, acquiring current parking place information and a plurality of additional information of a parking lot, the current parking place information at least comprising a location of an unoccupied parking place, each additional information in the plurality of additional information being configured for identifying associated data; on the basis of the current parking place information and the additional information, generating a parking place distribution image, the parking place distribution image at least indicating the unoccupied parking place and the additional information; presenting the parking place distribution image; and in response to that an operation targeting the unoccupied parking place or the additional information is detected, generating a navigation indication related to a target unoccupied parking place.

The present disclosure provides a method and apparatus for detecting a traffic anomaly, relates to the field of artificial intelligence and specifically to computer vision and deep learning technologies, and can be applied to video analysis scenarios. A specific implementation comprises: acquiring at least two frames of consecutive traffic images; identifying respectively a position of a target vehicle from the at least two frames of consecutive traffic images to obtain a position information set; determining a direction of travel and speed of the target vehicle according to the position information set; and comparing the direction of travel and speed of the target vehicle with a pre-generated vehicle vector field to determine whether the target vehicle is abnormal.

The present disclosure provides a method and apparatus for detecting a traffic anomaly, relates to the field of artificial intelligence and specifically to computer vision and deep learning technologies, and can be applied to video analysis scenarios. A specific implementation comprises: acquiring at least two frames of consecutive traffic images; identifying respectively a position of a target vehicle from the at least two frames of consecutive traffic images to obtain a position information set; determining a direction of travel and speed of the target vehicle according to the position information set; and comparing the direction of travel and speed of the target vehicle with a pre-generated vehicle vector field to determine whether the target vehicle is abnormal.

For tracking a target, multiple smart devices are self-organized into an ad hoc network, once a target is detected. A typical target is a motor vehicle or other moving objects. To add a device to the ad hoc network, a device within the system reaches out to a device that is not in the ad hoc network, and solicits the latter for images and data gathered in the past, as well as for future gathering of images and data. The usefulness of a device to the ad hoc network in view of target tracking is calculated, so that devices are added to or removed from the ad hoc network. The network might track multiple targets. In addition to detecting and tracking targets in “real world” scenes, targets in video games, or in virtual worlds, or in the Metaverse, are also contemplated.