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

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

Baidu Online Network Technology Co., Ltd.

Fangfang Dong

Shiyu Song

Li Yu

Chen Yang

Jiming Mao

Shichun Yi

Qingrui Sun

Dun Luo

Junfei Zhang

Cheng Fang Wang

Hao Jia Li

Guowei Wan

Yao Zhou

Baoqiang Xu

Renlan Cai

Xiaolong Yang

Shengpan Xu

Jie Huang

Lixia Shen

Weixin Lu

Xiangyu Fu

Ruijie Hou

Jingsen Zheng

Weilin Peng

Hailong Tian

Embodiments of the present disclosure provide a method and an apparatus for generating a simulation scene. The method includes: acquiring scene parameters of a benchmark scene, where a dimensionality of the scene parameters of the benchmark scene is M; inputting the scene parameters of the benchmark scene into an encoder that is trained, and acquiring encoding parameters according to an output result of the encoder, where a dimensionality of the encoding parameters is N, and N<M; adjusting the encoding parameters to obtain adjusted encoding parameters, and inputting respectively the adjusted encoding parameters into a decoder that is trained; and generating a simulation scene according to the scene parameters of the reconstructed scene that are output by the decoder. According to the method, generating massive simulation scenes similar to the benchmark scene based on the benchmark scene can be achieved, which meets the diversity requirements for the simulation scenes.

Method and apparatus for generating simulation scene

Embodiments of a method, apparatus, device and computer readable storage medium for classifying driving scenario data includes: acquiring a first driving scenario data set from a crowdsourcing platform, driving scenario data in the first driving scenario data set having been classified; generating a driving scenario classification model at least based on the first driving scenario data set, for classifying driving scenario data collected by a collection entity; acquiring a rule for classifying the driving scenario data, the rule is generated based on a result of classifying the driving scenario data collected by the collection entity using the driving scenario classification model; updating the driving scenario classification model at least based on the rule.

Method, apparatus, device and medium for classifying driving scenario data

A method and an apparatus for generating a high precision map, and a storage medium for generating a high precision map. The method includes: performing a point cloud splicing process on target point cloud data to obtain a lidar pose corresponding to the target point cloud data; projecting the target point cloud data into a preset two-dimensional area based on the lidar pose to generate a map based on a reflection value and a height value; performing a self-positioning verification on the map based on the reflection value and the height value using the target point cloud data; and integrating, if a result of the self-positioning verification satisfies a preset condition, the map based on the reflection value and the height value into a reference map to generate the high precision map.

Method for generating a high precision map, apparatus and storage medium

Embodiments of the present disclosure disclose a method and apparatus for positioning an autonomous vehicle. The method includes: matching a current point cloud projected image of a first resolution with a map of the first resolution to generate a first histogram filter based on the matching result; determining at least two first response areas in the first histogram filter based on a probability value of an element in the first histogram filter; generating a second histogram filter based on a result of matching a current point cloud projected image of a second resolution with a map of the second resolution and the at least two first response areas, the first resolution being less than the second resolution; and calculating a weighted average of probability values of target elements in the second histogram filter to determine a positioning result of the autonomous vehicle in the map of the second resolution.

Method and apparatus for positioning autonomous vehicle

A stationary object detecting method, a stationary object apparatus, and an electronic device are disclosed in embodiments of the present disclosure, the method includes: obtaining point cloud data of a scene and feature information of each of data points in the point cloud data; performing a triangulation network connection on each of the data points in the point cloud data to generate a triangular network model, and taking a picture of the triangular network model using a preset camera to obtain an image; obtaining a first data point corresponding to each pixel point in the image, and obtaining a feature map of the point cloud data according to feature information of each first data point; inputting the feature map into a classification model to obtain data points corresponding to the stationary object in the point cloud data.

Stationary object detecting method, apparatus and electronic device

A method and an apparatus for extracting a lane line, a device, a computer readable-storage medium and a collection entity are provided. The method includes: obtaining a first group of lane lines of a road based on a first image generated from a point cloud collected by a laser radar; obtaining a second group of lane lines of the road based on a second image collected by a camera; and determining a lane line set of the road based on the first group of lane lines and the second group of lane lines.

Method and apparatus for extracting lane line and computer readable storage medium

Embodiments of the present disclosure provide a method and a device for acquiring a map, a device, and a computer readable storage medium. The method includes: acquiring a reference map of an acquisition area; dividing the acquisition area into a plurality of sub-areas based on the reference map; generating a plurality of acquisition tasks corresponding to the plurality of sub-areas, wherein each of the plurality of acquisition tasks is configured to acquire a map of each of the plurality of sub-areas; and assigning each of the plurality of acquisition tasks to an acquisition entity, to enable the acquisition entity to acquire the map of each of the plurality of sub-areas.

Method, device and apparatus for acquiring map, and computer readable storage medium

Provided are a method and an apparatus for acquiring a control amount for a performance test of an unmanned vehicle, including: acquiring coordinates of y key points on a trajectory of a simulated obstacle vehicle and speeds at the y key points, where y&#x2265;2; acquiring trajectory equations of the obstacle vehicle between every two adjacent key points according to the coordinates of the y key points, where the trajectory equation is an ndegree polynomial equation, n&#x2265;2; acquiring accelerations of the obstacle vehicle between every two adjacent key points according to speeds at the y key points and a trajectory curve corresponding to the trajectory equation; for each running duration, according to the accelerations, acquiring a dynamic attribute and a coordinate when the obstacle vehicle has run for the running duration according to trajectories corresponding to the trajectory equations.

Method and apparatus for acquiring control amount for performance test of unmanned vehicle

Embodiments of the present disclosure provide a method and an apparatus for generating a simulation scene. The method includes: acquiring scene parameters of a benchmark scene, where a dimensionality of the scene parameters of the benchmark scene is M; inputting the scene parameters of the benchmark scene into an encoder that is trained, and acquiring encoding parameters according to an output result of the encoder, where a dimensionality of the encoding parameters is N, and N&#x3c;M; adjusting the encoding parameters to obtain adjusted encoding parameters, and inputting respectively the adjusted encoding parameters into a decoder that is trained; and generating a simulation scene according to the scene parameters of the reconstructed scene that are output by the decoder. According to the method, generating massive simulation scenes similar to the benchmark scene based on the benchmark scene can be achieved, which meets the diversity requirements for the simulation scenes.

A method and an apparatus for processing information are provided. A method may include: sending a preset data acquisition request to a target data transmission end included in at least two preset data transmission ends; receiving feedback information from the target data transmission end, and determining whether the feedback information includes the acquired data; determining, in response to determining that the feedback information does not include the acquired data, whether the feedback information includes first fault information for indicating that the target data transmission end malfunctions; and selecting, in response to determining that the feedback information includes the first fault information, a data transmission end other than the target data transmission end from the at least two data transmission ends as a new target data transmission end.

Method and apparatus for processing information

The present disclosure provides an integrated positioning method and system. The method comprises: receiving IMU data, and solving for the IMU data in a first Kalman filter to obtain a system state variable of an autonomous vehicle; receiving and buffering measurement data, and regarding the measurement data as current measurement data; performing measurement updating: obtaining the system state variable at a measurement time of current measurement data, and solving in the second Kalman filter to obtain an updated system state variable; if measurement data after the measurement time of the measurement data is already buffered, regarding the already-buffered measurement data after the measurement time as current measurement data, and performing the measurement update. The present disclosure can solve the problem about measurement information disorder caused by inconsistency of time delay in solving between different sensors, and improve real time and accuracy of the positioning.

Integrated positioning method and system

The present disclosure provides a method and a device for positioning, an apparatus and a computer readable storage medium. The method includes acquiring a visual feature map of a geographic area located by a movable object. The visual feature map includes a first set of visual features. The method further includes acquiring at least one image captured by a camera coupled to the movable object during a motion of the movable object within the geographic area. The method further includes extracting a second set of visual features corresponding to the first set of visual features from the at least one image. In addition, the method further includes determining at least one pose of the movable object during the motion at least based on the visual feature map and the second set of visual features.

Method and device for positioning

In a specific implementation of the method a reflectance map is constructed based on a position and an Euler angle, obtained through a global optimization and used for constructing a reflectance map, of a center of a laser radar corresponding to each frame laser point cloud used for constructing the reflectance map collected in each collection region. This implementation implements the level-by-level pose optimization of laser point clouds used for constructing a reflectance map that are collected in each collection region in an excessively large region, to obtain an accurate position and Euler angle, used for constructing the reflectance map, of a laser radar center corresponding to each frame laser point cloud used for constructing the reflectance map.

Method and apparatus for constructing reflectance map

The present disclosure provides a laser point cloud positioning method and system. The method comprises: converting laser point cloud reflection value data and height value data matched with a current location of an autonomous vehicle into laser point cloud projection data in a ground plane; assigning a weight for a reflection value matching probability and a height value matching probability of the laser point cloud projection data and a laser point cloud two-dimensional grid map, and determining a matching probability of the laser point cloud projection data and the laser point cloud two-dimensional grid map; determining a location of the autonomous vehicle in the laser point cloud two-dimensional grid map based on a matching probability of the laser point cloud projection data and the laser point cloud two-dimensional grid map. The present disclosure is employed to solve the problem in the prior art that when the laser point cloud matches with the map, an undesirable matching effect is achieved by individually considering the reflection value matching or height value matching, or superimposing the two simply. The present disclosure can improve the laser point cloud positioning precision, and enhance robustness of the positioning system.

Laser point cloud positioning method and system

Disclosed embodiments include a method and a device for generating position information of a target object. In some embodiments, the method comprises: obtaining target point cloud data of the target object, acquired by a target scanner at a target position, and position information of the target scanner, and obtaining reference point cloud data of the target object, acquired by a reference scanner at a reference position, and position information and an Euler angle of the reference scanner; obtaining an Euler angle of the target scanner based on the target point cloud data, the reference point cloud data, and the Euler angle of the reference scanner; and generating the position information of the target object based on the target point cloud data, the position information and the Euler angle of the target scanner, the reference point cloud data, and the position information and the Euler angle of the reference scanner.

Growing community of inventors

Beijing, China

Fangfang Dong