A method is provided. The method includes: obtaining first environmental information and second environmental information, where the first environmental information and the second environmental information are acquired by different sensors; determining, based on the first environmental information, information about a first lane of a first traffic object in the first environmental information, and determining; and determining whether the first traffic object and the second traffic object have an association relationship.

The present disclosure provides a method of processing an image, a device, and a medium. The method of processing the image includes: performing an image processing on an original image to obtain a component image for brightness of the original image; determining at least one of the original image and the component image as an image to be processed; classifying a pixel in the image to be processed, so as to obtain a classification result; processing the image to be processed according to the classification result, so as to obtain a target image; and determining an image quality of the original image according to the target image.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for monitoring events using a Virtual Inductive Loop system. In some implementations, image data is obtained from cameras. A region depicted in the obtained image data is identified, the region comprising lines spaced by a distance that satisfies a distance threshold. For each line included in the region: an object depicted crossing the line is determined whether to satisfy a height criteria indicating that the line is activated. In response to determining that an object depicted crossing the line satisfies the height criteria, an event is determined to have likely occurred using data indicating (i) which lines of the lines were activated and (ii) an order in which each of the lines were activated. In response to determining that an event likely occurred, actions are performed using at least some of the data.

Example aspects of the present disclosure describe a scene generator for simulating scenes in an environment. For example, snapshots of simulated traffic scenes can be generated by sampling a joint probability distribution trained on real-world traffic scenes. In some implementations, samples of the joint probability distribution can be obtained by sampling a plurality of factorized probability distributions for a plurality of objects for sequential insertion into the scene.

Example aspects of the present disclosure describe a scene generator for simulating scenes in an environment. For example, snapshots of simulated traffic scenes can be generated by sampling a joint probability distribution trained on real-world traffic scenes. In some implementations, samples of the joint probability distribution can be obtained by sampling a plurality of factorized probability distributions for a plurality of objects for sequential insertion into the scene.

A method, apparatus and computer program product are provided to determine lane status confidence indicators of lane status predictions such as closures and/or shifting. Lane statuses and corresponding confidence indicators are determined based on probe data, such as probe data collected from vehicle and/or mobile devices traveling along a road segment. Probe data may be partitioned into clusters and compared to partitioned subsets of the probe data. Cluster stability for the segment and corresponding lane status confidence indicators can be determined based on the comparison. Accordingly, determinations of whether to transmit predicted lane statuses to another system, service, and/or user device may be made.

A license plate detection and recognition system receives training data comprising images of license plates. The system prepares ground truth data from the training data based predefined parameters. The system trains a first machine learning algorithm based on the ground truth data to generate a license plate detection model. The license plate detection model is configured to detect one or more regions in the images. The one or more regions contains a candidate for a license plate. The LPDR system generates a bounding box for each region. The LPDR system trains a second machine learning algorithm based on the ground truth data and the license plate detection model to generate a license plate recognition model. The license plate recognition model generates a sequence of alphanumeric characters with a level of recognition confidence for the sequence.

A system for detecting a pedestrian at a crosswalk and preventing an accident on the basis of artificial intelligence, the system comprising: a camera device installed on a mast arm installed at the top of a traffic light pole installed at a crosswalk to capture pedestrians and the vicinity of the crosswalk; a pedestrian detection module for detecting a pedestrian in an image captured by the camera device through a pre-installed image analysis solution; a color detection module for detecting a current blinking color of a traffic light consisting of a pedestrian traffic light and a driving traffic light; and a warning signal output module for outputting an unauthorized crossing warning message to the pedestrian waiting at the crosswalk according to a detection result of the color detection module and a detection result of the pedestrian detection module.

A travel support system includes a server configured to support the travel of a vehicle. The server comprises a recognition unit configured to recognize an obstacle on a travel path of the vehicle, an obtainment unit configured to obtain, upon detecting an approaching vehicle which is approaching the obstacle, a blind spot region which occurs due to the obstacle recognized by the recognition unit, and a notification unit configured to notify the approaching vehicle of information of the blind spot region obtained by the obtainment unit. The server is arranged in an apparatus other than the approaching vehicle.

An image capturing method and apparatus, an electronic photography device and a computer-readable storage medium are provided. The image capturing method includes: in a snapshot mode, performing image acquisition at a first frame rate and buffering an acquired first captured image; in a live mode, acquiring a second captured image at a second frame rate, where the second frame rate is less than the first frame rate; and processing the buffered first captured image and the second captured image at the second frame rate.