Argo Ai Gmbh

Ford Global Technolgoies, LLC

Jason Ziglar

Xiaoyan Hu

Michael Happold

Lingyuan Wang

G Peter K Carr

Basel Alghanem

Arsenii Saranin

Benjamin D Ballard

Satyen Rajpal

Systems and methods for object detection. Object detection may be used to control autonomous vehicle(s). For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of the autonomous vehicle; and using, by the computing device. The LiDAR dataset and image(s) are used to detect an object that is in proximity to the autonomous vehicle. The object is detected by: computing a distribution of object detections that each point of the LiDAR dataset is likely to be in; creating a plurality of segments of LiDAR data points using the distribution of object detections; and detecting the object in a point cloud defined by the LiDAR dataset based on the plurality of segments of LiDAR data points. The object detection may be used to facilitate at least one autonomous driving operation.

Systems and methods for camera-LiDAR fused object detection with local variation segmentation

Systems/methods for object detection. The methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of the autonomous vehicle; and using, by a computing device, the LiDAR dataset and at least one image to detect an object that is in proximity to the autonomous vehicle. The object is detected by: generating a pruned LiDAR dataset by reducing a total number of points contained in the LiDAR dataset; and detecting the object in a point cloud defined by the pruned LiDAR dataset. The object detection may be used by the computing device to facilitate at least one autonomous driving operation.

Systems and methods for camera-LiDAR fused object detection with point pruning

Systems and methods for object detection. The methods include, by a computing device: obtaining a plurality of intensity values denoting at least a difference in a first location of at least one object in a first image and a second location of the at least one object in a second image; converting the intensity values to 3D position values; inputting the 3D position values into a classifier algorithm to obtain classifications for data points of a 3D point cloud (each of the classifications comprising a foreground classification or a background classification); and using the classifications to detect at least one object which is located in a foreground or a background.

Systems and methods for object detection using stereovision information

Devices, methods, and systems may obtain at least one point cloud, segment points in the at least one point cloud into a plurality of segments, train a neural network using known segments and a first loss function to generate a first trained neural network, train the first trained neural network using outlier segments and a second loss function to generate a second trained neural network, and train an extended isolation forest by applying an extended isolation algorithm to features of the known segments and features of the outlier segments to generate an anomaly score for each segment.

System and method for hybrid LiDAR segmentation with outlier detection

Systems and methods for designing a robotic system architecture are disclosed. The methods include generating a model that defines one or more requirements for a robotic device for a mapping between a software graph and a hardware graph. The model is used for allocating a plurality of computational tasks in a computational path included in the software graph to a plurality of hardware components of the robotic device to yield a robotic system architecture. The methods also include using the robotic system architecture to configure the robotic device to be capable of performing functions corresponding to the software graph, where the robotic system architecture is optimized to meet one or more latency requirements.

Method and system for designing a robotic system architecture with optimized system latency

Systems and methods for object detection. The methods comprise, by a computing device: obtaining a plurality of intensity values denoting at least a difference in a first location of at least one object in a first image and a second location of the at least one object in a second image; converting the intensity values to 3D position values; inputting the 3D position values into a classifier algorithm to obtain classifications for data points of a 3D point cloud (each of the classifications comprising a foreground classification or a background classification); and using the classifications to detect at least one object which is located in a foreground or a background.

Systems and methods for operating a vehicle. The methods comprise, by a processor: obtaining a pair of stereo images captured by a stereo camera; processing the pair of stereo images to generate a disparity map comprising a plurality of pixels defined by intensity values; converting each intensity value to a 3D position in a map (each 3D position defining a location of a data point in a point cloud); performing a hierarchical decision tree classification to determine a classification for each data point in the point cloud (the classification being a foreground classification or a background classification); and using the classifications to facilitate autonomous control of the vehicle.

Systems and methods for designing a robotic system architecture are disclosed. The methods include defining a software graph including a first plurality of nodes, and a first plurality of edges representative of data flow between the first plurality of tasks, and defining a hardware graph including a second plurality of nodes, and a second plurality of edges. The methods may include mapping the software graph to the hardware graph, modeling a latency associated with a computational path included in the software graph for the mapping between the software graph and the hardware graph, allocating a plurality of computational tasks in the computational path to a plurality of the hardware components to yield a robotic system architecture using the latency, and using the robotic system architecture to configure the robotic device to be capable of performing functions corresponding to the software graph.

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Pittsburgh, PA, United States of America

Jason Ziglar