Boe Technology Group Co., Ltd.

Beijing Boe Optoelectronics Technology Co., Ltd.

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

Baidu USA LLC

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

Beijing Baidu Netcom Science and Technology Co., Ltd.

Tsinghua University

Hefei Xinsheng Optoelectronics Technology Co., Ltd.

Beijing University of Technology

Capitalbio Corporation

Telenav Gmbh

Beijing University of Posts and Telecommunications

Beijing Boe Sensor Technology Co., Ltd.

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

Beijing Geekplus Technology Co. Ltd.

Fan Yang

Jianshe Xue

Bin Gao

Shuang Zhang

Xiaoxing Zhu

Xu Chen

Zhenyu Xie

Fan Zhu

Jingjing Xue

Junping Wang

Gang Wang

Chengfa Wang

Seung Moo Rim

Dongchao Gao

Wei Liu

Hao Zhang

Jing Cheng

Seongyeol Yoo

Wei Wu

Qi Chen

Xuehui Zhang

Woobong Lee

Bin Cai

Huafang Gao

Taohe Zhu

Dang Zhang

Yuan Tong

Junfeng Dai

Yichen Zhou

Yiyi Zhang

Yue Wang

Zheng Chen

Kai Liu

Kun Wang

Yu Yang

Qiang Wang

Dong Wang

Jie Zhang

Li Li

Yu Zhu

Wei Yang

Wei Qin

Di Zhang

Heecheol Kim

Bo Wu

Xin Xu

Xing Xing Wang

Xin Fang

Jing Yang

Yan Chen

Yujie Gao

He Wang

Qiping Zhang

Zhen Bo Zhu

Yongyi Sun

Shuo Yang

Min Wei

Zhifeng Zhang

Lin Huang

Chunnan Feng

Liang Guo

Yingnan Liu

Yaling Kang

Zuchuan Shi

Yunnan Jin

Hongyi Li

Haoyang Zhang

Bo Huang

Peng Luo

Tieyong Zuo

Fan Zhou

Peina Wang

Yajie Li

Shuyong He

Jinshu Wang

Meiling Zhou

Yongli Zhao

Langlang Sun

Yeshun Xu

Xiaochu Zhang

Lijun Ju

Zungan Ling

Sihui Bao

Caixia Li

ZiZheng Liu

He Liu

Tao Guo

Yan Tang

Guohao Gong

Yanhua Liu

Gengxin Lan

Guangyan Sun

Tieyi Zhang

Xiaosong Yu

Jiajia Peng

Jin-Shu Wang

Li-Ran Dong

Mei-Ling Zhou

Tie-Yong Zuo

An antenna includes a first substrate and a second substrate oppositely arranged; the first substrate includes: a first dielectric substrate having a first surface and a second surface oppositely arranged; a reference electrode layer on the first surface; at least one first radiation part on the second surface and having an orthographic projection on the first dielectric substrate at least partially overlapping that of the reference electrode layer; and at least one feeding structure on the second surface, electrically connected to the first radiation part, and having an orthographic projection on the first dielectric substrate at least partially overlapping that of the reference electrode layer; the second substrate includes: a second dielectric substrate opposite to the second surface; and at least one second radiation part on the second dielectric substrate, and each having an orthographic projection on the first surface within that of one first radiation part.

Antenna and communication system

A transferring robot is disclosed in the disclosure. The transferring robot includes a movable chassis configured to move along a path among adjacent warehouse storage containers; a temporary storage shelf provided on the movable chassis, the temporary storage shelf being configured to store a target case; a fetching assembly provided on the movable chassis, the fetching assembly being configured to extend or retract horizontally relative to the movable chassis, so as to realize fetching and placing of the target case between the storage containers and the temporary storage shelf, where a direction along which the fetching assembly extends or retracts horizontally is perpendicular to a moving direction. A warehousing and logistics system includes the foregoing transferring robot. The disclosure further discloses a warehousing system and an item transferring method.

Transfer robot, warehouse logistics system and article transferring method

The present disclosure provides a display substrate, a manufacturing method of the display substrate and a display apparatus. The display substrate includes a base substrate; an alignment layer, on the base substrate; a first electrode, located between the base substrate and the alignment layer and being in contact with the alignment layer; and a pattern layer, located between the base substrate and the alignment layer and being in contact with the alignment layer. An orthographic projection of the pattern layer on the base substrate does not overlap an orthographic projection of the first electrode on the base substrate, and a ratio of a contact angle of the pattern layer to a contact angle of the first electrode is greater than or equal to 7/12 and less than 3/2.

Display substrate, manufacturing method thereof and display apparatus

Embodiments of the present disclosure provide a display substrate having a display region and a frame region. The display substrate includes a plurality of display pixels and a plurality of dummy pixels arranged in an array; the display pixels are distributed in the display region; the dummy pixels are distributed in the frame region. Each display pixel includes a first pixel electrode and a first common electrode; and a first voltage difference exists between the first pixel electrode and the first common electrode. Each dummy pixel includes a second pixel electrode and a second common electrode; and a second voltage difference exists between the second pixel electrode and the second common electrode. A difference between the first voltage difference and the second voltage difference is less than a set value. Embodiments of the present disclosure further provides a display panel and a display apparatus.

Display substrate, display panel and display apparatus

An autonomous vehicle includes: a plurality of side laser radars respectively provided on a plurality of side portions of the autonomous vehicle, each of the plurality of side laser radars being partially embedded in a corresponding side portion of the plurality of side portions; and a top laser radar provided on a top portion of the autonomous vehicle and configured to obtain environmental information around the autonomous vehicle together with the plurality of side laser radars. By providing the plurality of laser radars, a sensing blind spot of the autonomous vehicle may be avoided.

Autonomous vehicle

The present disclosure provides a method and system for processing an obstacle detection result of an ultrasonic sensor array. The method comprises: obtaining obstacle coordinates collected by ultrasonic sensors in an ultrasonic sensor array; inputting the obstacle coordinates collected by ultrasonic sensors in the ultrasonic sensor array into a pre-trained neural network model, to obtain true or false labels for the obstacle coordinates collected by the ultrasonic sensors output by the pre-trained neural network model; process the obstacle coordinates collected by the ultrasonic sensors in the ultrasonic sensor array according to the true or false labels. The present disclosure improves the accuracy in the obstacle detection of the ultrasonic sensor array, avoids detection error and omission and improves the driving safety.

Method and system for processing obstacle detection result of ultrasonic sensor array

The present disclosure provides an ultrasonic radar array, an obstacle detection method and system. The method includes: obtaining obstacle information collected by ultrasonic radars in an ultrasonic radar array in an obstacle scenario; judging false detection and missed detection for the obstacle information collected by ultrasonic radars according a preset rule; processing the obstacle information collected by the ultrasonic radars according to the judgement result; determining a position of the obstacle according to the processed obstacle information collected by the ultrasonic radars.

Ultrasonic radar array, and obstacle detection method and system

A method, apparatus, and system for filtering a point cloud generated by a LIDAR device in an autonomous vehicle is disclosed. A point cloud comprising a plurality of points is generated based on outputs of the LIDAR device. The point cloud is filtered to remove a first set of points in the plurality of points that correspond to noise based on one or more of: point intensity measurements, distances between points, or a combination thereof. Perception data is generated based on the filtered point cloud. Operations of the autonomous vehicle are controlled based on the perception data.

Neighbor-based point cloud filter system

A method, apparatus, and system for determining whether all extrinsic matrices are accurate is disclosed. A plurality of post-LIDAR fusion point clouds that are based on simultaneous outputs from a plurality of LIDAR devices installed at one ADV are obtained. The obtained plurality of point clouds are filtered to obtain a first set of points comprising all points in the plurality of point clouds that fall within a region of interest. Each point in the first set of points corresponds to one coordinate value on the axis in the up-down direction. A distribution of the first plurality of coordinate values is obtained. A quantity of peaks in the distribution of the first plurality of coordinate values is determined. Whether all extrinsic matrices associated with the plurality of LIDAR devices are accurate is determined based on the quantity of peaks in the distribution of the first plurality of coordinate values.

Detector for point cloud fusion

According to various embodiment, described herein are methods and systems for reliably detecting malfunctions in a variety of software or hardware components in an autonomous driving vehicle (ADV). In one embodiment, a redundant system can be provided on an independent computing device in an ADV to check for malfunctions in a number of software or hardware components. When no malfunction occurs in the ADV, an autonomous driving system (ADS) in the ADV operates to drive the ADV, while the redundant system can monitor the ADS in a standby mode. In the event of a malfunction, the redundant system can take over the control of the ADV, and take appropriate actions based on a severity level of the malfunction.

Fail-safe handling system for autonomous driving vehicle

A method, including: acquiring a point cloud frame including point cloud data of a pedestrian; projecting the point cloud data of the pedestrian to a ground coordinate system to obtain projection point data of the pedestrian; determining a direction of a connection line between the two shoulders of the pedestrian, based on a location distribution of the projection point data; extracting a point cloud of a stable region from the point cloud data of the pedestrian based on the direction of the connection line between the two shoulders of the pedestrian, a form change range of the stable region when the pedestrian moves being smaller than form change ranges of other regions of the pedestrian; and determining movement information of the pedestrian based on a coordinate of a center point of the point cloud of the stable region in a plurality of consecutive point cloud frames.

Method and apparatus for detecting pedestrian

A method, apparatus, and system for estimating a moving speed of a detected pedestrian at an autonomous driving vehicle (ADV) is disclosed. A pedestrian is detected in a plurality of frames of point clouds generated by a LIDAR device installed at an autonomous driving vehicle (ADV). In each of at least two of the plurality of frames of point clouds, a minimum bounding box enclosing points corresponding to the pedestrian excluding points corresponding to limbs of the pedestrian is generated. A moving speed of the pedestrian is estimated based at least in part on the minimum bounding boxes across the at least two of the plurality of frames of point clouds. A trajectory for the ADV is planned based at least on the moving speed of the pedestrian. Thereafter, control signals are generated to drive the ADV based on the planned trajectory.

Partial point cloud-based pedestrians' velocity estimation method

A method and apparatus for calibrating a camera are provided. A specific embodiment of the method includes: acquiring an image-point cloud sequence, the image-point cloud sequence including at least one group of an initial image and point cloud data collected at a same time, the initial image being collected by a camera provided on an autonomous vehicle and the point cloud data being collected by a radar provided on the autonomous vehicle; determining target point cloud data of initial images corresponding to groups of image-point cloud in the image-point cloud sequence; and matching the target point cloud data with the corresponding initial images in the image-point cloud sequence to determine a correction parameter of the camera.

Method and apparatus for calibrating camera

A method and apparatus for controlling a vehicle is disclosed. The method may include: determining center points of at least two frames of point clouds collected for an identified obstacle during travelling of the vehicle; performing curve fitting based on the determined center points to obtain a fitted curve; determining a moving velocity of the obstacle based on the fitted curve; predicting whether the vehicle is to be collided with the obstacle when the vehicle continues travelling at a current velocity, based on the moving velocity of the obstacle, the traveling velocity of the vehicle, and a distance between the obstacle and the vehicle; and sending control information to the vehicle, in response to predicting that the vehicle is to be collided with the obstacle when the vehicle continues travelling at the current velocity, the control information being used to control the vehicle to avoid collision with the obstacle.

Method and apparatus for controlling vehicle

A method, apparatus, and system for filtering obstacle candidates determined based on outputs of a LIDAR device in an autonomous vehicle is disclosed. A point cloud comprising a plurality of points is generated based on outputs of the LIDAR device. One or more obstacle candidates are determined based on the point cloud. The one or more obstacle candidates are filtered to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates. One or more recognized obstacles comprising the obstacle candidates that have not been removed are determined. Operations of an autonomous vehicle are controlled based on the recognized obstacles.

Point cloud feature-based obstacle filter system

Growing community of inventors

Beijing, China

Fan Yang