Various embodiments for systems and methods of evaluating perception systems for autonomous vehicles using a quality temporal logic are disclosed herein.

Various embodiments for systems and methods of evaluating perception systems for autonomous vehicles using a quality temporal logic are disclosed herein.

The subject disclosure relates to techniques for detecting an object. A process of the disclosed technology can include steps for receiving three-dimensional (3D) Light Detection and Ranging (LiDAR) data of the object at a first time, generating a first point cloud based on the 3D LiDAR data at the first time, receiving 3D LiDAR data of the object at a second time, generating a second point cloud based on the 3D LiDAR data at the second time, aggregating the first point cloud and the second point cloud to form an aggregated point cloud, and placing a bounding box around the aggregated point cloud. Systems and machine-readable media are also provided.

Exemplary positioning method and apparatus, a device, a system, a medium and a self-driving vehicle are provided. The positioning method includes determining, according to the current frame of point cloud data collected by a to-be-positioned terminal in a travelling environment, the current key point in the current frame of point cloud data and a point cloud distribution feature of the current key point; selecting, according to point cloud distribution features associated with reference key points in a global positioning map and the point cloud distribution feature of the current key point, a target key point matching the current key point from the reference key points; and determining, according to reference pose data associated with the target key point, the current pose data of the to-be-positioned terminal.

A system and method for performing visual localization is disclosed. In aspects, the system implements methods to generate a global point cloud, the global point cloud representing a plurality of point clouds. The global point cloud can be mapped to a prior map information to locate a position of an autonomous vehicle, the prior map information representing pre-built geographic maps. The position of the autonomous vehicle can be estimated based on applying sensor information obtained from sensors and software of the autonomous vehicle to the mapped global point cloud.

According to an embodiment of the present disclosure, a method for learning an exercise posture of a user is disclosed. The method includes: checking joint feature point information which is constructed based on a joint of the user; learning a ready posture learning model by learning the joint feature point information corresponding to a ready posture of the user; and learning an exercise posture learning model by learning the joint feature point information corresponding to an exercise posture of the user.

A multi-camera vehicle vision system and method. In one embodiment a map is generated about a moving vehicle. Frames of image data are provided with a series of cameras extending along a surface of the vehicle. The image data frames are processed to identify an object of interest. An object of interest is classified among a set of object types and location of an identified object of interest is determined. Object type and location information is provided to a control unit spaced apart from the cameras via a data link. Road map data is generated to illustrate changes in position of the moving vehicle along a roadway based on data other than the image data provided by the cameras. A display of the road map data is generated with the object type and location information overlaid on the road map data to indicate object location relative to the vehicle.

A multi-camera vehicle vision system and method. In one embodiment a map is generated about a moving vehicle. Frames of image data are provided with a series of cameras extending along a surface of the vehicle. The image data frames are processed to identify an object of interest. An object of interest is classified among a set of object types and location of an identified object of interest is determined. Object type and location information is provided to a control unit spaced apart from the cameras via a data link. Road map data is generated to illustrate changes in position of the moving vehicle along a roadway based on data other than the image data provided by the cameras. A display of the road map data is generated with the object type and location information overlaid on the road map data to indicate object location relative to the vehicle.

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.

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.