Abstract: An autonomous vehicle, system and method of operating an autonomous vehicle. The system includes a communication module and a processor. The communication module sends a first set of Global Positioning Satellite (GPS) data over a first communication channel and a second set of GPS data over a second communication channel. The second set of GPS data is an authenticated data set. The processor operates the autonomous vehicle using the first set of GPS data, and compares the first set of GPS data to the second set of GPS data to verify the integrity of the first set of GPS data. A first value for a vehicle parameter based on the first set of GPS data is compared to a second value for the vehicle parameter based on data from a vehicle-based sensor. The first set of GPS data is rational when the difference is less than a selected threshold.

Abstract: An autonomous vehicle, system and method of operating an autonomous vehicle. The system includes a communication module and a processor. The communication module sends a first set of Global Positioning Satellite (GPS) data over a first communication channel and a second set of GPS data over a second communication channel. The second set of GPS data is an authenticated data set. The processor operates the autonomous vehicle using the first set of GPS data, and compares the first set of GPS data to the second set of GPS data to verify the integrity of the first set of GPS data. A first value for a vehicle parameter based on the first set of GPS data is compared to a second value for the vehicle parameter based on data from a vehicle-based sensor. The first set of GPS data is rational when the difference is less than a selected threshold.

Abstract: In some embodiments, systems, methods, and devices disclosed herein are directed to RAM-based computer systems, methods, and devices that offer improved performance over conventional computer systems. In some embodiments, the RAM-based computer systems, devices, and methods described herein can function without reliance on conventional storage devices and thus are not subject to the bottleneck in processing speed associated with conventional storage devices. Rather, in some embodiments, the RAM-based computer systems, devices, and methods described herein include and/or utilize a processor and RAM, wherein the RAM is used for mass data storage, without reliance on a conventional hard drive, solid state drive, or any other peripheral storage device.

Abstract: In some embodiments, systems, methods, and devices disclosed herein are directed to RAM-based computer systems, methods, and devices that offer improved performance over conventional computer systems. In some embodiments, the RAM-based computer systems, devices, and methods described herein can function without reliance on conventional storage devices and thus are not subject to the bottleneck in processing speed associated with conventional storage devices. Rather, in some embodiments, the RAM-based computer systems, devices, and methods described herein include and/or utilize a processor and RAM, wherein the RAM is used for mass data storage, without reliance on a conventional hard drive, solid state drive, or any other peripheral storage device.

Abstract: An image processing device includes circuitry to acquire a range image, recognize an external situation indicating a situation of an imaging area corresponding to the range image, recognize a recognition target to be tracked from the range image by a recognition method based on the recognized external situation, remove the recognition target from the range image to generate a new range image, and detect an object in the new range image from which the recognition target has been removed.

Abstract: A processing apparatus includes a distance image acquirer, a moving-object detector, a background recognizer. The distance image acquirer acquires a distance image containing distance information of each pixel. The moving-object detector detects a moving object from the distance image. The background recognizer generates a background model in which a stationary object recognized as background of the moving object is modeled, from the distance image acquired by the distance image acquirer. The moving-object detector changes a method for detecting the moving object, based on a relative positional relation between the moving object and the background model.

Abstract: In some embodiments, systems, methods, and devices disclosed herein are directed to RAM-based computer systems, methods, and devices that offer improved performance over conventional computer systems. In some embodiments, the RAM-based computer systems, devices, and methods described herein can function without reliance on conventional storage devices and thus are not subject to the bottleneck in processing speed associated with conventional storage devices. Rather, in some embodiments, the RAM-based computer systems, devices, and methods described herein include and/or utilize a processor and RAM, wherein the RAM is used for mass data storage, without reliance on a conventional hard drive, solid state drive, or any other peripheral storage device.

Abstract: An image processing device includes a range image acquiring unit configured to acquire a range image; a situation recognizer configured to recognize an external situation indicating a situation of an imaging area corresponding to the range image; and a target recognizer configured to recognize a recognition target from the range image by a recognition method based on a recognition result of the situation recognizer.

Abstract: Disclosed is an image feature extraction method including a step of defining a combination of at least two kinds of solid angles along at least two directions, of an input spherical image; a step of determining respective values of the combination of the at least two kinds of solid angles, so that surface areas of spherical crowns of spherical segments, which are obtained by dividing the spherical image by the respective values of the combination of the at least two kinds solid angles, have a same value; and a step of generating, by utilizing the respective values of the combination of the at least two kinds of solid angles, an image feature template so as to conduct image feature extraction.

Abstract: A method and a system for analyzing a target in a stereo image by displaying the stereo image using a cascade structure are disclosed. The method includes for the input stereo image, generating, based on a first relevant feature, rule or model of the stereo image, at least a first first-level structure map, each of the first first-level structure maps being generated based on an individual tolerance level of the first relevant feature, rule or model, and each of the first first-level structure maps including the target at an individual first division level; and at least partly integrating the first first-level structure maps and analyzing the target in the stereo image, to obtain a structure map of a first-level target analysis result including the target.

Abstract: A processing apparatus includes a distance image acquirer, a moving-object detector, a background recognizer. The distance image acquirer acquires a distance image containing distance information of each pixel. The moving-object detector detects a moving object from the distance image. The background recognizer generates a background model in which a stationary object recognized as background of the moving object is modeled, from the distance image acquired by the distance image acquirer. The moving-object detector changes a method for detecting the moving object, based on a relative positional relation between the moving object and the back-ground model.

Abstract: Disclosed is an object detection method used to detect an object in an image pair corresponding to a current frame. The image pair includes an original image of the current frame and a disparity map of the same current frame. The original image of the current frame includes at least one of a grayscale image and a color image of the current frame. The object detection method comprises steps of obtaining a first detection object detected in the disparity map of the current frame; acquiring an original detection object detected in the original image of the current frame; correcting, based on the original detection object, the first detection object so as to obtain a second detection object; and outputting the second detection object.

Abstract: A method and an apparatus are used for detecting a road partition, the method comprising: a step of obtaining a disparity top view having a road area; and a step of detecting parallel lines as the road partitions from the disparity top view.

Abstract: Disclosed is an object tracking method. The method includes steps of obtaining a first boundary region of a waiting-for-recognition object in the disparity map related to the current frame; calculating a probability of each valid pixel in the first boundary region so as to get a pixel probability map of the waiting-for-recognition object; obtaining historic tracking data of each tracked object, which includes identifier information of the tracked object and a pixel probability map related to each of one or more prior frame related disparity maps prior to the disparity map related to the current frame; determining identifier information of the waiting-for-recognition object, and updating the pixel probability map of the waiting-for-recognition object; and updating, based on the updated pixel probability map of the waiting-for-recognition object, the first boundary region of the waiting-for-recognition object, so as to get a second boundary region of the waiting-for-recognition object.

Abstract: Disclosed is an image feature extraction method including a step of defining a combination of at least two kinds of solid angles along at least two directions, of an input spherical image; a step of determining respective values of the combination of the at least two kinds of solid angles, so that surface areas of spherical crowns of spherical segments, which are obtained by dividing the spherical image by the respective values of the combination of the at least two kinds solid angles, have a same value; and a step of generating, by utilizing the respective values of the combination of the at least two kinds of solid angles, an image feature template so as to conduct image feature extraction.

Abstract: A method and a device are disclosed for detecting a drivable region of a road, the method comprising the steps of: deriving a disparity map from a gray-scale map including the road and detecting the road from the disparity map; removing a part with a height above the road greater than a predetermined height threshold from the disparity map so as to generate a sub-disparity map; converting the sub-disparity map into a U-disparity map; detecting the drivable region from the U-disparity map; and converting the drivable region detected from the U-disparity map into the drivable region within the gray-scale map.

Abstract: Disclosed is an object detection method used to detect an object in an image pair corresponding to a current frame. The image pair includes an original image of the current frame and a disparity map of the same current frame. The original image of the current frame includes at least one of a grayscale image and a color image of the current frame. The object detection method comprises steps of obtaining a first detection object detected in the disparity map of the current frame; acquiring an original detection object detected in the original image of the current frame; correcting, based on the original detection object, the first detection object so as to obtain a second detection object; and outputting the second detection object.

Abstract: A method and a system for analyzing a target in a stereo image by displaying the stereo image using a cascade structure are disclosed. The method includes for the input stereo image, generating, based on a first relevant feature, rule or model of the stereo image, at least a first first-level structure map, each of the first first-level structure maps being generated based on an individual tolerance level of the first relevant feature, rule or model, and each of the first first-level structure maps including the target at an individual first division level; and at least partly integrating the first first-level structure maps and analyzing the target in the stereo image, to obtain a structure map of a first-level target analysis result including the target.

Abstract: Disclosed is an object tracking method. The method includes steps of obtaining a first boundary region of a waiting-for-recognition object in the disparity map related to the current frame; calculating a probability of each valid pixel in the first boundary region so as to get a pixel probability map of the waiting-for-recognition object; obtaining historic tracking data of each tracked object, which includes identifier information of the tracked object and a pixel probability map related to each of one or more prior frame related disparity maps prior to the disparity map related to the current frame; determining identifier information of the waiting-for-recognition object, and updating the pixel probability map of the waiting-for-recognition object; and updating, based on the updated pixel probability map of the waiting-for-recognition object, the first boundary region of the waiting-for-recognition object, so as to get a second boundary region of the waiting-for-recognition object.

Abstract: Disclosed are a method and a system for detecting an object on a road. The method comprises a step of simultaneously capturing two depth maps, and then calculating a disparity map; a step of obtaining, based on the disparity map, a V-disparity image by adopting a V-disparity algorithm; a step of detecting an oblique line in the V-disparity image, and then removing points in the disparity map, corresponding to the oblique line so as to acquire a sub-disparity map excluding the road; a step of detecting plural vertical lines in the V-disparity image, and then extracting, for each of the plural vertical lines, points corresponding to this vertical line from the sub-disparity map as an object sub-disparity map corresponding to this vertical line; and a step of merging any two rectangular areas of the object sub-disparity maps approaching each other, into a rectangular object area.