A method including rendering graphics for an application using graphics processing units (GPUs). Responsibility for rendering of geometry is divided between GPUs based on screen regions, each GPU having a corresponding division of the responsibility which is known. First pieces of geometry are rendered at the GPUs during a rendering phase of a previous image frame. Statistics are generated for the rendering of the previous image frame. Second pieces of geometry of a current image frame are assigned based on the statistics to the GPUs for geometry testing. Geometry testing at a current image frame on the second pieces of geometry is performed to generate information regarding each piece of geometry and its relation to each screen region, the geometry testing performed at each of the GPUs based on the assigning. The information generated for the second pieces of geometry is used when rendering the geometry at the GPUs.

Method and apparatus for generating point cloud encoder, method and apparatus for generating point cloud data, electronic device and computer storage medium are provided. The method for generating point cloud encoder includes: first point cloud data and second point cloud data of an object are acquired; a first probability distribution of a global feature of the first point cloud data is determined based on a first encoder; a second probability distribution of a global feature of the second point cloud data is determined based on a second encoder; a weight of the first encoder is regulated based on a first difference between the first probability distribution and the second probability distribution to obtain a target weight of the first encoder; and a point cloud encoder is generated according to the first encoder and the target weight.

Disclosed is a vehicle fuel economy evaluation method based on data analysis. The method combines data processing and a fuel model with an enhanced learning mechanism to predict fuel consumption, analyze driving behavior and output an improvement suggestion. With continuous enhanced learning and long-term dynamic improvement, the model will be able to predict economic fuel consumption in an increasingly accurate way, along with specific and intuitive driving behavior suggestions to help drivers to drive economically.

Disclosed is a vehicle fuel economy evaluation method based on data analysis. The method combines data processing and a fuel model with an enhanced learning mechanism to predict fuel consumption, analyze driving behavior and output an improvement suggestion. With continuous enhanced learning and long-term dynamic improvement, the model will be able to predict economic fuel consumption in an increasingly accurate way, along with specific and intuitive driving behavior suggestions to help drivers to drive economically.

A virtual traffic line generation apparatus and a method thereof are provided. The virtual traffic line generation apparatus includes a controller that determines reliability of a traffic line detected for each frame and generates a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected and a storage storing the reliability of the traffic line for each frame.

A virtual traffic line generation apparatus and a method thereof are provided. The virtual traffic line generation apparatus includes a controller that determines reliability of a traffic line detected for each frame and generates a virtual traffic line based on a traffic line with the highest reliability among traffic lines detected in a previous frame when the traffic line is not detected and a storage storing the reliability of the traffic line for each frame.

A method and device for event displaying, a computer-readable storage medium, and an electronic device. The method comprises: acquiring parameter information of multiple users in a preset area (S110); determining, on the basis of parameter information, the probabilities of events to be displayed being triggered (S120); and determining, on the basis of the probabilities, a duration for displaying said events in the preset area (S130). The method overcomes the problem of data resource wastage in the prior art in which a greater number of invalid events are displayed when displaying events without taking into consideration parameter information of users.

A method and device for event displaying, a computer-readable storage medium, and an electronic device. The method comprises: acquiring parameter information of multiple users in a preset area (S110); determining, on the basis of parameter information, the probabilities of events to be displayed being triggered (S120); and determining, on the basis of the probabilities, a duration for displaying said events in the preset area (S130). The method overcomes the problem of data resource wastage in the prior art in which a greater number of invalid events are displayed when displaying events without taking into consideration parameter information of users.

Methods, systems, and apparatuses are provided to automatically detect objects within images. For example, an image capture device may capture an image, and may apply a trained neural network to the image to generate an object value and a class value for each of a plurality of portions of the image. Further, the image capture device may determine, for each of the plurality of image portions, a confidence value based on the object value and the class value corresponding to each image portion. The image capture device may also detect an object within at least one image portion based on the confidence values. Further, the image capture device may output a bounding box corresponding to the at least one image portion. The bounding box defines an area of the image that includes one or more objects.

Methods, systems, and apparatuses are provided to automatically detect objects within images. For example, an image capture device may capture an image, and may apply a trained neural network to the image to generate an object value and a class value for each of a plurality of portions of the image. Further, the image capture device may determine, for each of the plurality of image portions, a confidence value based on the object value and the class value corresponding to each image portion. The image capture device may also detect an object within at least one image portion based on the confidence values. Further, the image capture device may output a bounding box corresponding to the at least one image portion. The bounding box defines an area of the image that includes one or more objects.