Bayerische Motoren Werke Aktiengesellschaft

Nvidia Corporation

Abb Schweiz Ag

Wanli Jiang

Qianshan Li

Maximilian Doemling

Sebastian Granzow

Tao Xu

Tomas Xu

Bill Lv

Jianpeng Li

Shuhan Lv

Hongshan Xu

Rick Li

Ivan Xu

Minwoo Park

Carlos Martinez

Sangmin Oh

Junghyun Kwon

Carsten Isert

Maciej Orman

Gavin Xu

Yichun Shen

Siyi Li

Jacek Plesnar

Apparatuses, systems, and techniques to perform non-maximum suppression (NMS) in parallel to remove redundant bounding boxes. In at least one embodiment, two or more parallel circuits to perform two or more portions of a NMS algorithm in parallel to remove one or more redundant bounding boxes corresponding to one or more objects within one or more digital images.

Performing non-maximum suppression in parallel

A method and an apparatus optimizes scan data obtained by sensors on vehicle, and corrects trajectory for a vehicle/robot based on the optimized scan data. The method for optimizing the scan data obtained by scanning environment elements, includes: step of obtaining the scan data, including obtaining at least two frames of scan data respectively corresponding to different timings; step of cluster processing, based on the characteristic of the data points, including classifying the plurality of data points in each frame of the scan data into one or more clusters; step of establishing correspondence, among the at least two frames of scan data, including searching and obtaining at least one set of clusters having correspondence; step of optimizing clusters, among the at least two frames of scan data, including conducting calculation to each set of the at least one set of clusters having correspondence, to obtain optimized clusters respectively corresponding to each set of the at least one set of clusters having correspondence; and step of optimizing the scan data, including accumulating all optimized clusters to obtain an optimized scan date for the at least two frames of scan data.

Method and apparatus for optimizing scan data and method and apparatus for correcting trajectory

A method for segmentation of a point cloud includes receiving a first frame of point cloud from a sensor; segmenting the first frame of point cloud to obtain a first set of point clusters representing a segmentation result for the first frame of point cloud; receiving a second frame of point cloud from the sensor; mapping the first set of point clusters to the second frame of point cloud; determining points within the second frame of point cloud which do not belong to the mapped first set of point clusters; segmenting the points within the second frame of point cloud which do not belong to the mapped first set of point clusters to obtain a second set of point clusters; and generating a segmentation result for the second frame of point cloud by combining the first set of point clusters and the second set of point clusters.

Incremental segmentation of point cloud

A method for determining driving strategy of a vehicle includes obtaining localization information of the vehicle, a driving route of the vehicle, and a current driving velocity of the vehicle. The method further includes detecting and interpreting a current status of a traffic light. The method further includes recognizing a duration of the current status of the traffic light. The method further includes detecting a relevant stop line. The method further includes obtaining a distance between the stop line and the vehicle. The method further includes determining whether to change the current driving velocity according to the current status of the traffic light, the duration of the current status of the traffic light, the distance between the traffic stop line and the vehicle, and the current driving velocity. A data processing device for determining driving strategy of a vehicle is also provided.

Method and apparatus for determining driving strategy of a vehicle

A method and apparatus for selecting a map from a plurality of maps having different resolutions for a moving object includes dynamically selecting an appropriate map from maps having different levels of details according to environment information, such that a minimum amount of required map is loaded, thereby effectively improving the data processing efficiency of vehicles.

Methods and apparatus for selecting a map for a moving object, system, and vehicle/robot

A computer-implemented method for representing environmental elements includes receiving scan data comprising at least a point cloud representing at least an environmental element from a sensor, segmenting the point cloud into point clusters, and partitioning the point clusters into hierarchical grids. The method also includes establishing a Gaussian distribution for points in each cell of each of the hierarchical grids, and constructing a Gaussian Mixture Model based on the Gaussian distribution for representing the environmental element.

Method and apparatus for representing environmental elements, system, and vehicle/robot

A method for configuring a sensor, which is adapted to be installed on a moving object, includes obtaining localization information of the sensor from a localization device, obtaining characteristic information and localization information of at least one element from a digital map, and choosing one or more elements of the at least one element in the digital map. The method also includes configuring the sensor according to the characteristic information and/or the localization information of the one or more chosen elements.

Method, device and system for configuration of a sensor on a moving object

A method and an apparatus for uncertainty modeling of a point cloud and a non-transient storage medium are provided. The method finds the neighboring points for each point; fits a local quadratic surface for each point; and generates a Gaussian Model (GM) for representing the uncertainty modeling of the local surface at each point. The GM is constructed in a piece-wise manner on the underlying surface of the point cloud, which will accurately model the surface uncertainty.

Method and apparatus for uncertainty modeling of point cloud

A method and an apparatus are provided for representing a map element and for locating a vehicle/robot based thereupon. The method for representing a map element includes: generating a Gaussian Mixture Model for the map element; generating a signature for identifying the map element, wherein the signature includes properties of the map element; and generating a Signatured Gaussian Mixture Model for representing the map element, wherein the Signatured Gaussian Mixture Model includes the Gaussian Mixture Model and the signature.

Method and apparatus for representing a map element and method and apparatus for locating a vehicle/robot

A portable apparatus for controlling a robot and a method therefor. The portable apparatus includes: an orientation sensor adapted for measuring orientation of the portable apparatus; an HMI device adapted for detecting two-dimensional manual motion relative to the HMI device; and a processing unit adapted for receiving a first signal representing the measured orientation of the portable apparatus and a second signal representing the detected two-dimensional manual motion relative to the HMI device and controlling a part of the robot to move in a direction in consideration of the measured orientation of the portable apparatus and the detected two-dimensional manual motion relative to the HMI device. By having the portable apparatus and the method therefor as explained herein, the two-dimensional manual movement on touch panel is integrated with orientation of the portable apparatus and an integration of these is mapped by the robot, which makes it possible to define a path in three dimensional space for jogging/teaching robot's movements in three dimension.

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Shanghai, China

Wanli Jiang