Baidu USA LLC

Baidu.com Times Technology (beijing) Co. Ltd.

Peking University

Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Apollo Intelligent Driving Technology (beijing) Co., Ltd.

Edigene Biotechnology Inc.

Edigene Therapeutics (beijing) Inc.

Edigene Inc.

Edigene Therapeutics Inc.

Pengfei Yuan

Shiyu Song

Jia Wang

Li Yu

Runxin He

Yong Xiao

Wendong Ding

Wensheng Wei

Tong Wu

Liang Peng

Yuan Liu

Jie Huang

Yongyi Sun

Yan Feng

Wenjie Liu

Gaifan Li

Renlan Cai

Fan Yi

Xiaotao Li

Zongyi Yi

Liang Qu

Chunhui Wang

Yijun Yuan

Shiyou Zhu

Zhuo Zhou

Zexuan Yi

Lingling Yu

Yuexin Zhou

Yiou Chen

Hongmin Zhang

Yanxia Zhao

Nengyin Liu

Gangbin Tang

Zhongzheng Cao

Riguo Fang

Penghui Xia

Fucai Liang

The present application provides methods for editing RNA by introducing a deaminase-recruiting RNA in a host cell for deamination of an adenosine in a target RNA. The present application further provides deaminase-recruiting RNAs used in the RNA editing methods and compositions comprising the same.

Methods and compositions for editing RNAs

In one embodiment, a system and method for partitioning a region for point cloud registration of LIDAR poses of an autonomous driving vehicle (ADV) using a regional iterative closest point (ICP) algorithm is disclosed. The method determines the frame pair size of one or more pairs of related LIDAR poses of a region of an HD map to be constructed. If the frame pair size is greater than a threshold, the region is further divided into multiple clusters. The method may perform the ICP algorithm for each cluster. Inside a cluster, the ICP algorithm focuses on a partial subset of the decision variables and assumes the rest of the decision variables are fixed. To construct the HD map, the method may determine if the results of the ICP algorithms from the clusters converge. If the solutions converge, a solution to the point cloud registration for the region is found.

Coordinate gradient method for point cloud registration for autonomous vehicles

The present application discloses a high-precision mapping method and device, which relates to the field of autonomous driving. A specific implementation includes: acquiring global initial poses of multiple point clouds, where the point clouds are point clouds of a location for which a map is to be built and are collected by a lidar using a multi-circle collection mode; dividing the multiple point clouds into multiple spatial submap graphs according to a spatial distribution relationship of the multiple point clouds; optimizing, for each spatial submap graph, global initial poses of point clouds belonging to the spatial submap graph to acquire global poses of the point clouds in each spatial submap graph; and stitching the multiple spatial submap graphs together according to global poses of the point clouds in the multiple spatial submap graphs to acquire a base graph of the map to be built.

High-precision mapping method and device

A donor construct and a gene knockout method, as well as a system and kit for the gene knockout are provided. The donor construct is a linear donor DNA or can be cleaved in a cell to produce the linear donor DNA. The gene knockout method uses a marker gene contained in the donor construct to enrich cells in which a gene is knocked out, thereby improving the efficiency of generating the gene knockout by a sequence-specific nuclease.

Gene knockout method

In one embodiment, a system is disclosed for registration of point clouds for autonomous driving vehicles (ADV). The system receives a number of point clouds and corresponding poses from ADVs equipped with LIDAR sensors capturing point clouds of a navigable area to be mapped, where the point clouds correspond to a first coordinate system. The system partitions the point clouds and the corresponding poses into one or more loop partitions based on navigable loop information captured by the point clouds. For each of the loop partitions, the system applies an optimization model to point clouds corresponding to the loop partition to register the point clouds. They system merges the one or more loop partitions together using a pose graph algorithm, where the merged partitions of point clouds are utilized to perceive a driving environment surrounding the ADV.

Point clouds registration system for autonomous vehicles

In one embodiment, a system and method for point cloud registration of LIDAR poses of an autonomous driving vehicle (ADV) is disclosed. The method selects poses of the point clouds that possess higher confidence level during the data capture phase as fixed anchor poses. The fixed anchor points are used to estimate and optimize the poses of non-anchor poses during point cloud registration. The method may partition the points clouds into blocks to perform the ICP algorithm for each block in parallel by minimizing the cost function of the bundle adjustment equation updated with a regularity term. The regularity term may measure the difference between current estimates of the poses and previous or the initial estimates. The method may also minimize the bundle adjustment equation updated with a regularity term when solving the pose graph problem to merge the optimized poses from the blocks to make connections between the blocks.

Updated point cloud registration pipeline based on ADMM algorithm for autonomous vehicles

In one embodiment, a system identifies a road to be navigated by an ADV, the road being captured by one or more point clouds from one or more LIDAR sensors. The system extracts road marking information of the identified road from the point clouds, the road marking information describing one or more road markings of the identified road. The system partitions the road into one or more road partitions based on the road markings. The system generates a point cloud map based on the road partitions, where the point cloud map is utilized to perceive a driving environment surrounding the ADV.

Map partition system for autonomous vehicles

In one embodiment, a system receives a stream of frames of point clouds from one or more LIDAR sensors of an ADV and corresponding poses in real-time. The system extracts segment information for each frame of the stream based on geometric or spatial attributes of points in the frame, where the segment information includes one or more segments of at least a first frame corresponding to a first pose. The system registers the stream of frames based on the segment information. The system generates a first point cloud map for the stream of frames based on the frame registration.

Real-time map generation system for autonomous vehicles

Embodiments of the present disclosure provide a method and apparatus for evaluating data, a device, and a computer-readable storage medium in the field of autonomous driving. The method may include: generating, at least based on satellite navigation data related to a vehicle, a reference position and posture data set of the vehicle; generating, at least based on a part of the satellite navigation data, a test position and posture data set of the vehicle, the accuracy of the test position and posture data set being lower than that of the reference position and posture data set; obtaining a revised position and posture data set for the test position and posture data set; and evaluating, based on the reference position and posture data set and the revised position and posture data set, a correction performance for the test position and posture data set.

Method and apparatus for evaluating data, device, and computer-readable storage medium

The present disclosure discloses a method, an apparatus, an electronic device and a medium for acquiring map data, and relates to the field of intelligent transportation. A specific implementation solution is: determining a target areas of interest in a map, wherein the map is divided into at least two areas of interest; sending a data acquisition instruction to data acquisition terminals to acquire the map data in the target areas of interest; and receiving the map data fed back from the data acquisition terminal and updating the target areas of interest in the map according to the map data.

Method and apparatus for acquiring map data

In one embodiment, a system generates an occupancy grid map based on an initial frame of point clouds. The system receives one or more subsequent frames of the point clouds. For each of the subsequent frames, the system updates an occupancy grid map based on the subsequent frame. The system identifies one or more problematic voxels based on the update, the system determines whether the problematic voxels belong to a wall object, and in response to determining that the problematic voxels belong to a wall object, the system flags the problematic voxels as ghost effect voxels for the subsequent frame.

Point clouds ghosting effects detection system for autonomous driving vehicles

In one embodiment, a system receives a number of point clouds captured by one or more LIDAR sensors of an ADV and corresponding poses. The system receives a number of RGB images from one or more image capturing sensors of the ADV. The system synchronizes the RGB images with the point clouds to obtain RGB point clouds. The system extracts features from the RGB point clouds, the features including contextual and spatial information of the RGB point clouds. The system registers the RGB point clouds based on the extracted features and generates a point cloud map based on the registration of the RGB point clouds.

RGB point clouds based map generation system for autonomous vehicles

Growing community of inventors

Beijing, China

Pengfei Yuan