Baidu Online Network Technology (beijing) Co., Ltd.

International Business Machines Corporation

Beijing Baidu Netcom Science and Technology Co., Ltd.

Kunlunxin Technology (beijing) Company Limited

Baidu Online Network Technology Co., Ltd.

Yang Yan

Wei Qi

Liang Song

Xianglun Leng

Ruisuo Wang

Zhaoyan Jiang

Zhengze Qiu

Yong Wang

Yu Jiang

Hong Yang

Rong Song Shen

Ningyi Xu

Tao Yan

Xiao Dong Wang

Ming Liang Zu

Xiangfei Jia

Shuwei Guan

Provided are methods, systems, computer program products for scheduling jobs. The method may include receiving a request for allocating resources for a first job, a job comprising information regarding maximum amount of resources required by the job; determining a type of the first job; obtaining at least one backfill of the first job based on the determined type; allocating the maximum amount of resources from system resources to the first job; searching a second job in waiting jobs to be allocated resources, the second job being suitable to be allocated used resources by the first job from the maximum amount of resources allocated to the first job during the at least one backfill; allocating the resources unused by the first job from the maximum amount of resources allocated to the first job to the second job in response to the first job running to the at least one backfill.

Scheduling jobs

According to embodiments of the present disclosure, there is provided a data transmission apparatus. The data transmission apparatus includes a plurality of first ports, a plurality of second ports, and a plurality of data channels. The plurality of first ports are coupled to a processing unit. The plurality of second ports are coupled to a plurality of memories. The plurality of data channels are disposed among the first ports and the second ports to form an interleaving network having a plurality of layers, and configured to transmit data among the processing unit and the plurality of memories, such that each layer in the interleaving network includes at least one interleaving sub-network.

Data transmission apparatuses, data processing systems and methods

Example embodiments of the present disclosure provide a method, an apparatus, a device and a computer-readable storage medium for storage management. The method for storage management includes: obtaining an available channel mode of a plurality of channels in a memory of a data processing system, the available channel mode indicating availabilities of the plurality of channels, and each of the plurality of channels being associated with a set of addresses in the memory; obtaining a channel data-granularity of the plurality of channels, the channel data-granularity indicating a size of a data block that can be carried on each channel; obtaining a target address of data to be transmitted in the memory; and determining a translated address corresponding to the target address based on the available channel mode and the channel data-granularity.

Method, apparatus, device and computer-readable storage medium for storage management

The present disclosure provides a method and device for modelling a three-dimensional road model, and a storage medium. The method comprises: parsing two-dimensional road network data to establish a rudimentary road model; parsing panoramic image data to obtain three-dimensional attribute data of a traffic element; and combining the rudimentary model and the three-dimensional attribute data to obtain a three-dimensional road model. The method, device and storage medium can economically and efficiently acquire a three-dimensional road model based on a relatively easily obtained data source.

Method and device for modelling three-dimensional road model, and storage medium

A method and an apparatus for processing high-precision map data, a storage medium and a device. The method includes: performing at least twice collection on map data along a target trajectory so as to respectively acquire at least twice original map data; recognizing an object in the original map data respectively, and extracting an object feature; matching the object feature in each of the original map data; considering an object with the matched object feature as a reference object; and performing fusion processing on each of the original map data. An object in original map data is recognized and an object feature is extracted, then the object feature in each of the original map data is matched and the object obtained after the matching is considered as a reference object, and the reference object is utilized to perform fusion processing on the original map data. In this way, a problem of offset of an object in a map image is solved, ghosting of the map image is removed, and a definition of the map image is improved.

Method and apparatus for processing high-precision map data, storage medium and device

Embodiments of the present disclosure disclose a method and apparatus for urban road recognition based on a laser point cloud. The method comprises: constructing a corresponding road edge model according to the laser point cloud acquired by a laser sensor; determining a height of a mobile carrier provided with the laser sensor and constructing a corresponding road surface model based on the height and the laser point cloud; eliminating a road surface point cloud and a road edge point cloud in the laser point cloud according to the road edge model and the road surface model, segmenting a remaining laser point cloud using a point cloud segmentation algorithm, and recognizing an object corresponding to a segmenting result. By estimating the height of the mobile carrier according to the laser point cloud and constructing a corresponding road surface model using the height, the efficiency and accuracy of constructing the road surface model are improved, thereby improving the efficiency and accuracy of recognizing corresponding objects.

Method and apparatus for urban road recognition based on laser point cloud, storage medium, and device

Some embodiments of the present disclosure disclose a vehicular lane line data processing method, apparatus, storage medium, and device. The method includes: acquiring at least two consecutive original images of a vehicular lane line and positioning data of the original images; calculating, using a deep neural network model, a pixel confidence; determining an outline of the vehicular lane line and using the outline of the vehicular lane line as a candidate vehicular lane line; calculating a vehicular lane line confidence; filtering the candidate vehicular lane line; recognizing attribute information of the vehicular lane line; and determining map data of the vehicular lane line. The vehicular lane line data can be efficiently and precisely determined, the labor costs in high-precision map production is greatly reduced, and the mass production of high-precision maps can be achieved.

Vehicular lane line data processing method, apparatus, storage medium, and device

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Beijing, China

Yang Yan