Abstract: The present application discloses a single sided hydrophilic nonwoven fabric with high tensile strength and a CSR wrap made therefrom, wherein the single sided hydrophilic nonwoven fabric has a spunbonded/meltblown/spunbonded (SMS) structure consisting of a top spunbonded layer, a middle meltblown layer and a bottom spunbonded layer, wherein one side of the nonwoven fabric is hydrophilic, and the other side of the nonwoven fabric is hydrophobic.

Abstract: The present application discloses a positioning method for a movable platform, including: detecting, by a laser positioning system (LPS) mounted on the movable platform, a plurality of reflectors mounted on a target object, wherein the movable platform is moving; calculating in real-time, by the LPS, according to the current position information, relative positions of the plurality of reflectors with respect to the LPS; and obtaining, by the LPS, a relative position of the movable platform with respect to the target object based on the relative positions of the plurality of reflectors with respect to the LPS. The present application also discloses positioning system that performs the positioning method.

Abstract: A vision-LiDAR fusion method and system based on deep canonical correlation analysis are provided. The method comprises: collecting RGB images and point cloud data of a road surface synchronously; extracting features of the RGB images to obtain RGB features; performing coordinate system conversion and rasterization on the point cloud data in turn, and then extracting features to obtain point cloud features; inputting point cloud features and RGB features into a pre-established and well-trained fusion model at the same time, to output feature-enhanced fused point cloud features, wherein the fusion model fuses RGB features to point cloud features by using correlation analysis and in combination with a deep neural network; and inputting the fused point cloud features into a pre-established object detection network to achieve object detection. A similarity calculation matrix is utilized to fuse two different modal features.

Abstract: A vision-LiDAR fusion method and system based on deep canonical correlation analysis are provided. The method comprises: collecting RGB images and point cloud data of a road surface synchronously; extracting features of the RGB images to obtain RGB features; performing coordinate system conversion and rasterization on the point cloud data in turn, and then extracting features to obtain point cloud features; inputting point cloud features and RGB features into a pre-established and well-trained fusion model at the same time, to output feature-enhanced fused point cloud features, wherein the fusion model fuses RGB features to point cloud features by using correlation analysis and in combination with a deep neural network; and inputting the fused point cloud features into a pre-established object detection network to achieve object detection. A similarity calculation matrix is utilized to fuse two different modal features.

Abstract: The present application discloses a positioning method for a movable platform, including: detecting, by a laser positioning system (LPS) mounted on the movable platform, a plurality of reflectors mounted on a target object, wherein the movable platform is moving; calculating in real-time, by the LPS, according to the current position information, relative positions of the plurality of reflectors with respect to the LPS; and obtaining, by the LPS, a relative position of the movable platform with respect to the target object based on the relative positions of the plurality of reflectors with respect to the LPS. The present application also discloses positioning system that performs the positioning method.

Abstract: The present application discloses a positioning method for a movable platform, including: detecting, by a laser positioning system (LPS) mounted on the movable platform, a plurality of reflectors mounted on a target object, wherein the movable platform is moving; calculating in real-time, by the LPS, according to the current position information relative positions of the plurality of reflectors with respect to the LPS; and obtaining, by the LPS, a relative position of the movable platform with respect to the target object based on the relative positions of the plurality of reflectors with respect to the LPS. The present application also discloses positioning system that performs the positioning method.

Abstract: The present application discloses a positioning method for a movable platform, including: detecting, by a laser positioning system (LPS) mounted on the movable platform, a plurality of reflectors mounted on a target object, wherein the movable platform is moving; calculating in real-time, by the LPS, according to the current position information relative positions of the plurality of reflectors with respect to the LPS; and obtaining, by the LPS, a relative position of the movable platform with respect to the target object based on the relative positions of the plurality of reflectors with respect to the LPS. The present application also discloses positioning system that performs the positioning method.

Abstract: The present disclosure discloses methods and systems for testing the vehicle. In some embodiments, a method includes receiving, by an emulation server, a test task and a test scenario set required for executing the test task sent from a client; distributing, by the emulation server, each of the test scenarios to first emulation executors respectively, and sending the test task to each of the first emulation executors; acquiring, by the emulation server, a test result of the test task from each of the first emulation executors; and comparing, by the emulation server, the acquired test result with a preset test standard to generate feedback information of the test task, and sending the feedback information to the client.

Abstract: The present disclosure discloses embodiments of methods and apparatuses for indicating a vehicle moving state. In some embodiments, a method includes receiving a vehicle driving instruction; detecting a driving environment outside the vehicle; determining a driving strategy for executing the vehicle driving instruction in the driving environment; determining a driving track instructed by the driving strategy; and projecting the driving track on a road when the driving environment satisfies a preset condition. This implementation can clearly indicate the position that a vehicle is about to occupy, thereby improving the effect of interaction between the vehicle and other vehicles or pedestrians.

Abstract: The present invention discloses a vehicle control method and apparatus and a method and apparatus for acquiring a decision-making model. The vehicle control method, comprising: during travel of an unmanned vehicle, acquiring current external environment information and map information in real time; determining vehicle state information corresponding to the external environment information and map information acquired each time according to a decision-making model obtained by pre-training and reflecting correspondence relationship between the external environment information, map information and vehicle state information, and controlling a travel state of the unmanned vehicle according to the determined vehicle state information. Application of the solution of the present invention can improve security and reduce the workload.

Abstract: The present application discloses a method and an apparatus for generating a high precision map. According to an embodiment, the method comprises: acquiring three-dimensional (3D) laser point cloud data and information related to a grid map for generating the high precision map; determining position information of each piece of the 3D laser point data of the 3D laser point cloud data in the grid map; rendering each pixel point in the grid map, by using the reflection value of corresponding 3D laser point data of the 3D laser point cloud data, in order to generate each of grid images in the grid map; identifying, by using a machine learning algorithm, traffic information of each of the grid images in the grid map; clustering the traffic information of each of the grid images in the grid map to obtain the traffic information of the grid map; and loading the traffic information of the grid map into the grid map to generate the high precision map.

Abstract: The present invention discloses a method and apparatus of obtaining feature information of a simulated agent. The method further comprises: for each agent class, respectively obtaining feature information of real agents belonging to the class and freely participating in traffic activities, the number of real agents belonging to each class being greater than one; for each agent class, extracting representative feature information from feature information of each real agent belonging to the class, and taking the extracted feature information as feature information of simulated agents belonging to this class. The solution of the present invention may be applied to improve correctness of testing results of unmanned vehicles.

Abstract: Embodiments of real vehicle in-the-loop test systems and methods are disclosed. The system can include a sensor configured to acquire state information and location information of a real vehicle and environment information of an emulation test environment where the vehicle is located; an interaction module configured to acquire a test task in response to the instruction entered by a user, an emulation test environment of the test task comprises a test situation, a map, and an intelligent agent; a vehicle sensing module configured to acquire the state information, the location information, and the environment information from the sensor; and a test task control module configured to receive the test task from the interaction module, the state information and the location information from the vehicle sensing module and load them to the emulation test environment, control the real vehicle to execute the test task, and generate and send the test result.

Abstract: The present invention provides a local trajectory planning method and apparatus for a smart vehicle, pre-acquiring path planning information from a starting location to a destination; the method comprising: determining a target lane; sampling alternative curves from a current location of the smart vehicle to a target lane according to the path planning information; performing speed planning for the sampled alternative curves according to a current travel environment; selecting one of the alternative curves after the speed planning is performed as a target trajectory. Local trajectory planning of the smart vehicle is achieved through the present invention.

Abstract: Disclosed embodiments include a driverless vehicle control method, apparatus and system. In some embodiments, method comprises: receiving a vehicle control message of a driverless vehicle, the vehicle control message used to represent vehicle control characteristics of the driverless vehicle; converting the vehicle control message to a vehicle control protocol message, the vehicle control protocol message used to implement a general instruction description for the vehicle control message; generating a vehicle control command based on the vehicle control protocol message; and converting the vehicle control command to a vehicle control instruction corresponding to the vehicle control message, and sending the vehicle control instruction to the driverless vehicle. In this way, the vehicle control message can be described by using a general message instruction, achieving the objective of controlling the driverless vehicle by using a general message instruction.

Abstract: The present invention discloses a method and an apparatus of constructing a test scenario of an unmanned vehicle. The method comprises: obtaining a scenario attribute set by the user; respectively determining a map and an agent matching with the scenario attribute; generating a test scenario according to the determined map and agent. The solution of the present invention can be used to improve the efficiency of constructing the test scenario.

Abstract: The present application discloses a method and apparatus for detecting a vehicle contour based on point cloud data. The method includes: acquiring to-be-trained point cloud data; generating label data corresponding to points in the to-be-trained point cloud data in response to labeling on the points in the to-be-trained point cloud, the labeling used to indicate whether each of the points in the to-be-trained point cloud data is on a vehicle contour; training a fully convolutional neural network model based on the points in the to-be-trained point cloud data and the label data corresponding to the points in the to-be-trained point cloud data, to obtain a vehicle detection model; and acquiring to-be-detected point cloud data, and obtaining a detection result corresponding to each to-be-detected point in the to-be-detected point cloud data based on the vehicle detection model. The implementation may achieve an accurate detection of the vehicle contour.

Abstract: Disclosed embodiments include an unmanned vehicle, a method, apparatus and system for positioning an unmanned vehicle. In some embodiments, the method includes: acquiring first laser point cloud height value data matching a current position of the unmanned vehicle, the first laser point cloud height value data; converting the first laser point cloud height value data into laser point cloud projection data in a horizontal earth plane; determining a first matching probability of the laser point cloud projection data in a predetermined range of a laser point cloud height value map; and determining a position of the unmanned vehicle in the laser point cloud height value map based on the first matching probability. The embodiment implements an accurate positioning on the current position of the unmanned vehicle.

Abstract: Disclosed embodiments include a driverless vehicle, and a method, an apparatus and a system for positioning a driverless vehicle. In some embodiments, the method includes: acquiring first laser point cloud reflection value data matching a current position of the driverless vehicle; converting the first laser point cloud reflection value data into laser point cloud projection data in a horizontal earth plane; determining a first matching probability of the laser point cloud projection data in a predetermined range of a laser point cloud reflection value map by using a position of a predetermined prior positioning position in the laser point cloud reflection value map as an initial position; and determining a position of the driverless vehicle in the laser point cloud reflection value map based on the first matching probability. The implementation can accurately position the current position of the driverless vehicle.

Abstract: The present invention provides a method of building a smart vehicle control model, and a method and apparatus for controlling a smart vehicle, wherein the method of building a smart vehicle control model comprises: acquiring sample data which comprise corresponding steering wheel turning angles under driving environments; extracting vehicle state features and road condition features from the sample data; using the extracted features to train a neural network model to obtain the smart vehicle control model. The method of controlling smart vehicle comprises: extracting vehicle state features and road condition features of a vehicle to be controlled; inputting the extracted features into the smart vehicle control model to obtain a steering wheel turning angle; controlling the vehicle to be controlled using the steering wheel turning angle.