Abstract: Techniques for maintaining and synchronizing data is a processing pipeline data between multiple processing units to improve a system latency are described herein. For example, the techniques may include determining, in response to an invocation of vision processing on first vision data stored in a first memory range in a first memory associated with a central processing unit (CPU), that second vision data stored in a second memory range in a second memory associated with a graphic processing unit (GPU) is a modified copy of the first vision data. The second vision data may be obtained using a non-blocking operation from the second memory range. The first vision data stored in the first memory range may be replaced with the second vision data obtained from the second memory range. The vision processing may then be performed using the second vision data stored in the first memory.

Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.

Abstract: Techniques for maintaining and synchronizing data is a processing pipeline data between multiple processing units to improve a system latency are described herein. For example, the techniques may include determining, in response to an invocation of vision processing on first vision data stored in a first memory range in a first memory associated with a central processing unit (CPU), that second vision data stored in a second memory range in a second memory associated with a graphic processing unit (GPU) is a modified copy of the first vision data. The second vision data may be obtained using a non-blocking operation from the second memory range. The first vision data stored in the first memory range may be replaced with the second vision data obtained from the second memory range. The vision processing may then be performed using the second vision data stored in the first memory.

Abstract: Techniques for maintaining and synchronizing data is a processing pipeline data between multiple processing units to improve a system latency are described herein. For example, the techniques may include determining, in response to an invocation of vision processing on first vision data stored in a first memory range in a first memory associated with a central processing unit (CPU), that second vision data stored in a second memory range in a second memory associated with a graphic processing unit (GPU) is a modified copy of the first vision data. The second vision data may be obtained using a non-blocking operation from the second memory range. The first vision data stored in the first memory range may be replaced with the second vision data obtained from the second memory range. The vision processing may then be performed using the second vision data stored in the first memory.

Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.

Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.

Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.

Abstract: This invention presents a method and an apparatus for auto-programming for general purposes as well as a new kind of operating system that uses a general-purpose learning engine to learn any open-ended practical tasks or applications. Experimental systems of the method are applied to vision, audition, and natural language understanding.

Abstract: This invention includes a new type of neural network that is able to automatically and incrementally generate an internal hierarchy without a need to handcraft a static hierarchy of network areas and a static number of levels and the static number of neurons in each network area or level. This capability is achieved by enabling each neuron to have its own dynamic inhibitory zone using neuron-specific inhibitory connections.

Abstract: The Developmental Network incorporates a Turing Machine that injects teaching instructions directly into the skull-closed network. The Developmental Network can also autonomously learn directly from the natural world without the need for a human to encode its input and output. The neural network so configured can be used as a controller for robotic and other computer control applications where the neural network is organized into plural X-Y-Z areas receiving signals from sensors and providing signals to effectors.

Abstract: The Developmental Network incorporates a Turing Machine that injects teaching instructions directly into the skull-closed network. The Developmental Network can also autonomously learn directly from the natural world without the need for a human to encode its input and output. The neural network so configured can be used as a controller for robotic and other computer control applications where the neural network is organized into plural X-Y-Z areas receiving signals from sensors and providing signals to effectors.