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

Beijing Baidu Netcom Science and Technology Co., Ltd.

Petrochina Company Limited

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

Han Gao

Tian Xia

Xun Sun

Guo Qiang Li

Ming Wang

Qi Cheng Li

Yuqiang Zhai

Yilin Gui

Li Wang

Yali Wang

Libo Wang

Tong Liu

Yan Jiang

Ji Wan

Sihan Wang

Xianming Xu

Yulong Wang

Guizhi Wang

Fuling Huang

Xiuhui Wang

Buwei Yu

Hongling Chu

Shijia Chen

Xuemei Han

Kecun Ma

Yuanyuan Cao

Guo Li

Weihong Guan

Yunguang Han

Enhao Sun

Hongliang Huo

Fengrong Wang

Ming Li

Ruhai Lin

Legang Feng

Embodiments of the present provide a cross-camera obstacle tracking method, system and medium. The method includes: obtaining obstacle tracking results of images captured by at least two cameras, wherein each obstacle tracking result comprises results after identifying and tracking at least one obstacle in the image captured by each camera, and each obstacle is labeled with a local identifier for each camera; in response to a triggering condition of a main camera in the cameras to fusion of cross-camera obstacles being satisfied, establishing a mapping relation between local identifiers of the obstacles in the cameras and global identifiers according to the obstacle tracking result of each of the cameras; determining similarities of obstacles according to the obstacle tracking result of each of the cameras, and performing fusion of the global identifiers on the same obstacle according to the similarities; and obtaining final obstacle tracking results labeled with the global identifiers.

Cross-camera obstacle tracking method, system and medium

A method, device, apparatus and a computer-readable storage medium for detecting a height of an obstacle are provided. The method can include: acquiring observation data of a plurality of reference obstacles from a frame; according to the observation data of each of the reference obstacles, fitting a function F: Z=F(ymax), wherein the observation data of a reference obstacle comprises a longitudinal coordinate of a bottom of the reference obstacle in the frame, and a distance between the reference obstacle and a camera capturing the frame; and determining a distance between the obstacle to be detected and the camera, according to the longitudinal coordinate of the bottom of the obstacle in the frame and the function F; and determining an evaluation value of the height of the obstacle to be detected according to the distance between the obstacle to be detected and the camera.

Method, device, apparatus and storage medium for detecting a height of an obstacle

The present invention provides a low viscosity poly-&#x3b1;-olefin lubricating oil and a synthesis method thereof. The method comprises: (1) the &#x3b1;-olefin raw material is subjected to dehydration treatment so that the water content in the raw material is &#x2264;10 ppm; (2) a reaction of the dehydration treated &#x3b1;-olefin raw material is carried out in the presence of a complex catalyst and gaseous BFto obtain a reaction product, wherein the pressure of the gaseous BFis 0.01 to 1 MPa; (3) the reaction product obtained in step (2) is sequentially subjected to flash distillation, gas stripping, centrifugation, and washing treatment to obtain an intermediate product; (4) the intermediate product obtained in step (3) is subjected to distillation under reduced pressure to separate the unreacted &#x3b1;-olefin raw material and &#x3b1;-olefin dimers, and the remaining heavy fractions are subjected to hydrogenation saturation treatment followed by fractionation and cutting-off.

Low viscosity poly-a-olefin lubricating oil and synthesis method thereof

A method and device of multi-focal sensing of an obstacle. A method includes acquiring detection results of obstacles at multiple moments by utilizing a camera with long focus lens and a camera with short focus lens; performing a target tracking to the acquired detection results, to obtain at least two tracking sequences, wherein each tracking sequence includes detection results acquired at the multiple moments for a same obstacle; and matching two random tracking sequences of the at least two tracking sequences, and combining the two random tracking sequences into a combined tracking sequence, if the matching is successful.

Method and device of multi-focal sensing of an obstacle and non-volatile computer-readable storage medium

Embodiments of the present disclosure provide a method and an apparatus for recognizing a traffic light, a device, a storage medium, and a vehicle. The method may include: obtaining semantic information of one or more traffic lights provided at an intersection, and recognizing states of the one or more traffic lights in an image captured by a camera; determining traffic lights representing same semantics from the one or more traffic lights based on the semantic information, and determining a uniform state corresponding to the traffic lights representing the same semantics according to states of the traffic lights representing the same semantics and preset confidences of respective traffic light states; and determining indication information of the traffic lights representing the same semantics according to the uniform state.

Method for recognizing traffic light, device, and vehicle

The method comprises: obtaining a first image collected by a long-focus camera, a second image collected by a short-focus camera, and positioning information and travelling direction of the autonomous vehicle collected by a positioning sensor at a target moment; according to the positioning information and traveling direction of the autonomous vehicle at the target moment, obtaining, from a high-precision map server, location information of traffic lights within a range of preset distance threshold ahead in the traveling direction of the autonomous vehicle at the target moment; recognizing the state of traffic lights at the target moment, according to the location information of the traffic lights at the target moment, and one image of the first image and second image.

Traffic light state recognizing method and apparatus, computer device and readable medium

A method, device, apparatus and a computer-readable storage medium for detecting a height of an obstacle are provided. The method can include: acquiring observation data of a plurality of reference obstacles from a frame; for each reference obstacle, determining a first evaluation value of the height of the obstacle to be detected, according to the observation data of the reference obstacle, an observed height of the obstacle to be detected in a frame, an actual height of the reference obstacle, and a distance between a bottom of the obstacle to be detected in the frame and an optical axis of a camera capturing the frame; and determining a second evaluation value of the height of the obstacle to be detected by using a plurality of first evaluation values for the plurality of reference obstacles.

Embodiments of a method and system for sensing an obstacle, a computer device, and a computer-readable storage medium are provided. The method can include: capturing continuously, by first and second cameras adjacently arranged on a motor vehicle, obstacles around the motor vehicle, to obtain at least a first obstacle image and a second obstacle image respectively; associating the first obstacle image with the second obstacle image; determining whether the first obstacle image and the second obstacle image comprise the same obstacle. By means of some of the disclosed methods and systems, an obstacle can be real-time sensed by a motor vehicle in a large field of view, such as, a range of 360&#xb0; around the vehicle.

Method and system for sensing an obstacle, and storage medium

The disclosure discloses a method and apparatus for identifying a traffic light. An embodiment of the method comprises: zooming a to-be-processed image acquired by an image acquisition device by at least one preset ratio to obtain at least one zoomed image; inputting the at least one zoomed image into a pre-trained convolutional neural network to obtain location information and category information of a traffic light corresponding to each zoomed image of the at least one zoomed image, wherein the convolutional neural network is used for retrieving location information and category information of a traffic light displayed in an image; and analyzing the obtained location information and category information to generate at least one candidate traffic light identification result, and fusing the generated candidate traffic light identification result to generate a traffic light identification result corresponding to the to-be-processed image. The embodiment improves the accuracy in identifying a traffic light.

Method and apparatus for identifying traffic light

Embodiments of the present disclosure provide a method and an apparatus for recognizing an object, a device, a vehicle and a medium. The method includes: selecting a target camera matched with a target object from at least two cameras according to position information of the target object in a map and photographing parameters of the at least two cameras, in which, the at least two cameras has at least two focal lengths; controlling the target camera to photograph an image of the target object; and obtaining the image photographed by the target camera, and performing recognition on the image to recognize the target object.

Method and apparatus for recognizing object, device, vehicle and medium

Embodiments of the present disclosure disclose a method and apparatus for detecting a braking behavior of a front vehicle of an autonomous vehicle. A specific implementation of the method comprises: extracting a vehicle image from a vehicle area in an image acquired by the image acquisition device; converting a color space of the vehicle image to generate a first vehicle image; setting a pixel value of a pixel point in the first vehicle image meeting any condition in a preset condition group to a first preset value to generate a second vehicle image; analyzing the second vehicle image to determine a candidate vehicle light area group; and detecting, based on the candidate vehicle light area group, whether a vehicle indicated by the vehicle image is braking, and generating a detection result. The implementation improves the detection efficiency for the braking behavior of the front vehicle.

Method and apparatus for detecting braking behavior of front vehicle of autonomous vehicle

The present disclosure provides a multiple targets-tracking method and apparatus, a device and a storage medium, wherein the method comprises: obtaining a to-be-processed current image, inputting the current image into a convolutional neural network model obtained by pre-training, and obtaining a target detection result; respectively extracting feature vectors of each detected target from a pre-selected convolutional layer; respectively calculating a similarity between the feature vectors of each target in the current image and feature vectors of each target of previous images, completing association of the same target between different image frames according to calculation results, and allocating a tracking serial number. The solution of the present disclosure may be applied to meet requirements for real-time processing.

Multiple targets&#x2014;tracking method and apparatus, device and storage medium

Growing community of inventors

Beijing, China

Han Gao