Argo Ai Gmbh

Volkswagen Group of America Investments, LLC

James Hays

Fnu Ratnesh Kumar

Ersin Yumer

De Wang

Kevin Chen

Sean Foley

Ming-Fang Chang

Brett Browning

Michel Laverne

Ilan Biala

This document discloses system, method, and computer program product embodiments for detecting an object. For example, the method includes generating a plurality of cuboids by performing the following operations: defining a plurality of first cuboids each encompassing lidar data points that are plotted on a respective 3D graph of a plurality of 3D graphs; accumulating the lidar data points encompassed by the plurality of first cuboids; computing an extent using the accumulated lidar data points; and defining a second cuboid that has dimensions specified by the extent. The first cuboids and/or the second cuboid may be used to detect the object.

Systems and methods for producing amodal cuboids

Methods and systems are disclosed for correlating synthetic LiDAR data to a real-world domain for use in training an model for use by autonomous vehicle when operating in an environment. To do this, the system will obtain a data set of synthetic LiDAR data, along with images of a real-world environment. The system will transfer the synthetic LiDAR data to a two-dimensional representation, use the two-dimensional representation and the images to train a model that a vehicle can use to operate in a real-world environment.

Transferring synthetic lidar system data to real world domain for autonomous vehicle training applications

Systems and methods for operating an autonomous vehicle. The methods comprising: obtaining, by a computing device, loose-fit cuboids overlaid on 3D graphs so as to each encompass LiDAR data points associated with a given object; defining, by the computing device, an amodal cuboid based on the loose-fit cuboids; using, by the computing device, the amodal cuboid to train a machine learning algorithm to detect objects of a given class using sensor data generated by sensors of the autonomous vehicle or another vehicle; and causing, by the computing device, operations of the autonomous vehicle to be controlled using the machine learning algorithm.

Systems and methods for object detection. The methods comprise: obtaining, by a computing device, an image comprising a plurality of color layers superimposed on each other; generating at least one first additional layer using information contained in a road map (where the first additional layer includes ground height information, ground depth information, drivable geographical area information, map point distance-to-lane center information, lane direction information, or intersection information); generating a modified image by superimposing the first additional layer on the color layers; and causing, by the computing device, control of a vehicle's operation based on the object detection made using the modified image.

Systems and methods for monocular based object detection

A node is provided for capturing information about moving objects at an intersection. The node includes a plurality of first cameras that are positioned to capture first digital images of an intersection from different fields of view and a second camera positioned to capture second digital images in a field of view that is wider than that of each first camera. The node includes a processor that detects in the first and second digital images a set of objects of interest of the intersection, determines motion of each detected object of interest in the set from consecutive images of the first digital images or the second digital images. The node generates, for each object of interest of the set, augmented perception data that includes location data in the global coordinate system and the determined motion of each object of interest in the set.

Method and system for vehicle navigation using information from smart node

Methods and systems are disclosed for correlating synthetic LiDAR data to a real-world domain for use in training an autonomous vehicle in how to operate in an environment. To do this, the system will obtain a data set of synthetic LiDAR data, transfer the synthetic LiDAR data to a two-dimensional representation, use the two-dimensional representation to train a model of a real-world environment, and use the trained model of the real-world environment to train an autonomous vehicle.

Growing community of inventors

Decatur, GA, United States of America

James Hays