Abstract: A computer that includes a processor and a memory, the memory including instructions executable by the processor to generate background pixels and object pixels and generate background pixel ray data based on the background pixels and object ray pixel data based on the object pixels. The background pixel ray data can be input to a first neural network to generate background neural radiance fields (NeRFs) and the object pixel ray data can be input to a second neural network to generate object NeRFs. An output image can be rendered based on the background NeRFs and the object NeRFs.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein are systems, methods, and computer program products for object detection. The methods comprise performing the following operations by a computing device: obtaining an image that comprises a plurality of layers superimposed on each other; identifying a center point of a robot on a map; selecting a portion of the map contained in a geometric shape overlaid on the map so as to have a center set to the center point of the robot; obtaining map information associated with the selected portion of the map; generating at least one additional layer using the map information; superimposing the at least one additional layer onto the image to generate a modified image; and performing an object detection algorithm to detect at least one object in the modified image.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods by which an autonomous vehicle or related system may determine when a high definition (HD) map is out of date are disclosed. As the vehicle moves in an area, it captures sensor data representing perceived features of the area. A processor will input the sensor data along with HD map data for the area into a neural network to generate an embedding that identifies differences between features in the map data and corresponding features in the sensor data. The system may convert the sensor data into a birds-eye view or ego view before doing this. The network will identify any differences that exceed a threshold. The network will report the features for which the differences that exceed the threshold as features of the HD map that require updating.

Abstract: A system for navigation of a vehicle that includes a vehicle computer vision system to receive digital images of an environment along a path. The vision system has a vision range and includes a processor and programming instructions. The processor detects in the digital images a first set of objects of interest (OOIs) and determines motion of each OOI in the first set of OOIs. The system includes a communication device that receives augmented perception data associated with a node along a portion of the path. The received perception data identifies motion of each OOI of a second set of OOIs detected within a vision range of the node. The system includes a navigation controller that uses a fusion of the first set of OOIs and the second set of OOIs to control motion of the vehicle along the path.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method and kit to produce activated autologous platelet rich and platelet poor plasma (AAPRPP) and methods of use to treat pain. The method to produce AAPRPP generally comprising (1) obtaining whole blood from patient; (2) centrifuging whole blood in a collection tube; (3) extracting platelet rich and platelet poor plasma mixture from collection tube; and (4) transferring platelet rich and platelet poor plasma mixture to an activation tube containing at least one platelet aggregator and at least one platelet activator to obtain a resulting platelet concentration. The method to treat pain generally comprising producing AAPRPP and extracting contents of activation tube into a standard syringe then injecting AAPRPP at or near the nerve group responsible for the affected anatomical area.

Abstract: The invention provides gas purification systems for the recovery and liquefaction of low boiling point organic and inorganic gases, such as methane, propane, CO2, NH3, and chlorofluorocarbons. Many such gases are in the effluent gas of industrial processes and the invention can increase the sustainability and economics of such industrial processes. In a preferred system of the invention, low boiling point gases are adsorbed with a heated activated carbon fiber material maintained at an adsorption temperature during an adsorption cycle. During a low boiling point desorption cycle the activated carbon fiber is heated to a desorption temperature to create a desorption gas stream with concentrated low boiling point gases. The desorption gas stream is actively compressed and/or cooled to condense and liquefy the low boiling point gases, which can then be collected, stored, re-used, sold, etc.

Abstract: The invention provides gas purification methods and systems for the recovery and liquefaction of low boiling point organic and inorganic gases, such as methane, propane, CO2, NH3, and chlorofluorocarbons. Many such gases are in the effluent gas of industrial processes and the invention can increase the sustainability and economics of such industrial processes. In a preferred method of the invention, low boiling point gases are adsorbed with a heated activated carbon fiber material maintained at an adsorption temperature during an adsorption cycle. During a low boiling point desorption cycle the activated carbon fiber is heated to a desorption temperature to create a desorption gas stream with concentrated low boiling point gases. The desorption gas stream is actively compressed and/or cooled to condense and liquefy the low boiling point gases, which can then be collected, stored, re-used, sold, etc.

Abstract: In one embodiment, a processor-readable medium stores code representing instructions that when executed cause a processor to receive a first signal at a mobile device, the first signal including a mobile instant message having inline image data, the inline image data defining an image. The processor-readable medium stores code further representing instructions that when executed cause the processor to receive an edit command associated with the image and send a second signal, the second signal including a mobile instant message having a directive based at least in part on the edit command.

Abstract: A preferred embodiment steady state tracking desorption system achieves steady tracking of either a fixed sorbate output set point, or a set point that changes over time. The system includes an electrically heated thermal adsorption/desorption device A temperature sensor senses the temperature of an adsorbent material within the adsorption/desorption device. A sorbate sensor senses a sorbate level from an outlet of the adsorption/desorption device. A power sensor senses the power supplied by the desorption device. A controller interprets levels sensed by the temperature sensor, the sorbate sensor and the power sensor and provides a signal to achieve steady set point tracking of a sorbate level from the outlet of the adsorption/desorption device.