Abstract: Techniques for detecting erroneous LIDAR data are disclosed herein. One embodiment receives LIDAR point-cloud data pertaining to a robot's environment; receives image data and generates segmented optical-flow data therefrom; fuses, in a 2D grid, a plurality of objects including LIDAR points and optical-flow pixels; executes a hash function that generates, for the plurality of objects, a 1D hash table and an associated index; performs one or more queries using the 1D hash table and the associated index to measure the extent of spatial correspondence between the LIDAR points and the optical-flow pixels; identifies the LIDAR point-cloud data as erroneous, when the extent of spatial correspondence fails to satisfy one or more predetermined criteria; and identifies the LIDAR point-cloud data as valid and controls operation of the robot based, at least in part, on the LIDAR point-cloud data, when the extent of spatial correspondence satisfies the one or more predetermined criteria.

Abstract: Systems and methods for providing driving recommendations are disclosed herein. One embodiment receives, at an ego vehicle, first vehicle data and first encoded information from one or more other vehicles; constructs, from the first vehicle data, graph data representing how the ego vehicle and the one or more other vehicles are spatially related; inputs the graph data, the first vehicle data, second vehicle data pertaining to the ego vehicle, and the first encoded information to a graph convolutional network that outputs second encoded information; inputs the second encoded information and previously stored encoded information to a recurrent neural network that outputs a set of parameters to a mixture model; predicts acceleration of the one or more other vehicles using the mixture model; and generates a driving recommendation for the ego vehicle based, at least in part, on the predicted acceleration of the one or more other vehicles.

Abstract: Systems and methods for providing driving recommendations are disclosed herein. One embodiment receives, at an ego vehicle, first vehicle data and first encoded information from one or more other vehicles; constructs, from the first vehicle data, graph data representing how the ego vehicle and the one or more other vehicles are spatially related; inputs the graph data, the first vehicle data, second vehicle data pertaining to the ego vehicle, and the first encoded information to a graph convolutional network that outputs second encoded information; inputs the second encoded information and previously stored encoded information to a recurrent neural network that outputs a set of parameters to a mixture model; predicts acceleration of the one or more other vehicles using the mixture model; and generates a driving recommendation for the ego vehicle based, at least in part, on the predicted acceleration of the one or more other vehicles.

Abstract: A system or method for providing recommended items for presentation to users and/or consideration by vehicle systems may be executable to: retrieve social network data associated with a user; extract a keyword and/or a key phrase from the social network data; and determine a preference model that includes a frequency analysis associated with the keyword and/or the key phrase and indicating how often the keyword and/or the key phrase are observed in the social network data. The system may further receive a request for content data; search a content database to retrieve one or more content items based on the frequency analysis associated with the preference model; generate one or more recommended items for the user or for a vehicle system from the one or more content items; and provide the one or more recommended items for presentation to the user or for processing by the vehicle system.

Abstract: The disclosure includes a system and method for providing recommended items for presentation to users and/or consideration by vehicle systems.

Abstract: A system and method for determining a network is disclosed. The system comprises a requesting module and a determination module. The requesting module sends a request from a vehicle for network data to a connectivity server. The request includes vehicle data describing a current location of the vehicle and a destination for the vehicle. The requested network data is associated with the vehicle data. The determination module receives the network data relating to one or more networks. The determination module caches the network data and determines which network to connect to from the one or more networks based at least in part on the network data.

Abstract: The self-healing system comprises a self-healing processor and an error mitigation system. The self-healing processor includes a code block associated with the operation of a portion of digital logic. The self-healing processor also includes a dynamic signature analysis circuit. The processor executes the code block. The dynamic signature analysis circuit creates a dynamic signature representing the operation of the portion of digital logic associated with the code block. The error mitigation system receives the dynamic signature from the dynamic signature analysis circuit. The error mitigation system compares the dynamic signature to a static signature to determine if the signatures match. If the signatures do not match, then the digital logic associated with the code block has an error. The error mitigation system retries execution of the code block. The error mitigation system stores log information describing the above events.

Abstract: A system and method for managing event control schedules is disclosed. The system includes a retrieval module, an estimation module, a plan module and a scheduling module. The retrieval module retrieves mobile computer system journey context data and user profile data for one or more users of a mobile computer system. The estimation module estimates future journey data associated with one or more future trips based at least in part on the mobile computer system journey context data and the user profile data. The plan module generates one or more provisioning plans based at least in part on the estimated future journey data and determining a preferred provisioning plan from the one or more provisioning plans. The scheduling module generates a provisioning schedule for the vehicle.

Abstract: A system and method for managing event control schedules is disclosed. The system includes a retrieval module, an estimation module, an optimization module, a plan module and a scheduling module. The retrieval module retrieves mobile computer system journey context data and user profile data for one or more users of a mobile computer system. The estimation module estimates future journey data associated with one or more future trips based at least in part on the mobile computer system journey context data and user profile data. The optimization module determines one or more journey provisioning data parameters for the mobile computer system based at least in part on the estimated future journey data. The plan module generates one or more provisioning plans based at least in part on the one or more estimated future journey data and determining a preferred provisioning plan from the one or more provisioning plans. The scheduling module generates a provisioning schedule for the vehicle.

Abstract: A system and method for determining a network is disclosed. The system comprises a requesting module and a determination module. The requesting module sends a request from a vehicle for network data to a connectivity server. The request includes vehicle data describing a current location of the vehicle and a destination for the vehicle. The requested network data is associated with the vehicle data. The determination module receives the network data relating to one or more networks. The determination module caches the network data and determines which network to connect to from the one or more networks based at least in part on the network data.

Abstract: Historic vehicle state information specifying a plurality of performance values associated with the plurality of subsystems of the vehicle at a plurality of time points, including a current time point is stored at the vehicle. Historic neighbor vehicle state information specifying a plurality of performance values associated with a plurality of subsystems of a vehicle at a plurality of time points, including a current time point is received from a neighbor vehicle proximate to the vehicle. A forward-looking model is generated based on vehicle state information. An performance value associated with a subsystem of the plurality of subsystems of the vehicle is determined based on the historic vehicle state information, the historic neighbor vehicle state information, and the forward-looking model. A recommendation to a driver of the vehicle is provided based on the optimized performance value.

Abstract: An energy transfer system comprises a transmitter array, an energy transfer controller, a receiver array, a charging module. The transmitter array is embedded in a roadway and the energy transfer controller is coupled to the transmitter array. The receiver array and the charging module are part of a mobile vehicle. The transmitter array and the receiver array each include a plurality of coils. The energy transfer controller estimates a likely trajectory of the mobile vehicle and energizes individual coils of the transmitter array using this position estimate. The energy transfer controller varies the resonant circuit component values of the transmitter during the transfer cycle such as resonant coupling capacitance values. The charging module also varies the resonant circuit component values of the coils in the receiver array to match the transfer array for transfer of energy from the transmitter array to the receiver array. The present invention also includes a method for energy transfer.

Abstract: The self-healing system comprises a self-healing processor and an error mitigation system. The self-healing processor includes a code block associated with the operation of a portion of digital logic. The self-healing processor also includes a dynamic signature analysis circuit. The processor executes the code block. The dynamic signature analysis circuit creates a dynamic signature representing the operation of the portion of digital logic associated with the code block. The error mitigation system receives the dynamic signature from the dynamic signature analysis circuit. The error mitigation system compares the dynamic signature to a static signature to determine if the signatures match. If the signatures do not match, then the digital logic associated with the code block has an error. The error mitigation system retries execution of the code block. The error mitigation system stores log information describing the above events.

Abstract: A system, method, and computer readable medium for a business to securely distribute potentially large collections of document folders (180) to recipients (140). The system comprises a sending device (160) for sending the document folders (180) and temporarily storing the document folders (180) on a network (120) of interconnected computers (170), a destination computer (170) for accumulating the sent document folders (180) destined for a particular recipient (140), and a receiving device (130) for receiving the accumulated document folders (180) from the destination device (170).

Abstract: The group interaction system comprises a plurality of vehicle navigation systems that are capable of communicating with one another, displaying the location of other vehicle navigation systems in a group, and receiving selection of certain vehicle navigation systems in the group and selection of an application for interaction among the selected vehicle navigation systems. A group of vehicle navigation systems to interact with is established. One or more of other vehicle navigation systems to interact with the vehicle navigation system. An application is also selected on the vehicle navigation system for interaction with the selected other vehicle navigation systems. In response, the vehicle navigation system runs the selected application with respect to the selected other navigation systems.

Abstract: The group interaction system comprises a plurality of vehicle navigation systems that are capable of communicating with one another, displaying the location of other vehicle navigation systems in a group, and receiving selection of certain vehicle navigation systems in the group and selection of an application for interaction among the selected vehicle navigation systems. A group of vehicle navigation systems to interact with is established. One or more of other vehicle navigation systems to interact with the vehicle navigation system. An application is also selected on the vehicle navigation system for interaction with the selected other vehicle navigation systems. In response, the vehicle navigation system runs the selected application with respect to the selected other navigation systems.

Abstract: A radiographic imaging system (100) comprises an X-ray tube (110), a sensor plate (120), and a graphics engine (130). The tube (110) and the sensor plate (120) rotate synchronously about a patient (150) and expose a stereoscopic pair of images which are transmitted to the graphics engine (130). The graphics engine (130) determines (312) the geometry of the system (100). If (314) the pair of images are toed-in relative to each other, the graphics engine (130) converts (316) the images into a parallel geometry. Likewise, the graphics engine (130) also processes (320) the images for keystone distortion, if necessary. Simply flipping the images in the stereo pair distorts the depth of objects in the stereoscopic image. Instead of simply flipping the images, it is desirable to “go behind” the screen (412A) and look at the image from the back.

Abstract: Cameras (10, 12) produce first and second images (14, 16) of an object (18) from different viewpoints. An image partitioning module (54) partitions the images (14, 16) into a plurality of vertically striped regions (70), such that each region (70) of the first image (14) corresponds to a region (70) of the second image (16). A region alignment module (55) vertically shifts a region (70) of the first image (14) in a direction calculated to vertically align a portion (30) of the region (70) with a substantially matching portion (32) of the corresponding region (70). A disparity calculation module (62) calculates at least one disparity value between at least a portion (30) of the vertically shifted region (70) and at least a portion (32) of the corresponding region (70).

Abstract: A computer-implemented method for representing a stereoscopic image (204) in a portable document format encoded file (112) includes receiving at least two component images (110A-B) combinable to form the stereoscopic image (204); encoding the component images (110A-B) in a portable document format; associating each of the component images (110A-B) with a stereoscopic indicator (302); and storing the component images (110A-B) and associated stereoscopic indicators (302) in the encoded file (112). A computer implemented method for displaying a stereoscopic image (204) represented in a portable document format encoded file (112) includes identifying in the encoded file (112) at least two component images (110A-B) combinable to create the stereoscopic image (204); combining the component images (110A-B) to create the stereoscopic image (204); and displaying the stereoscopic image (204) on a stereoscopic display device (106).

Abstract: In generation of combined image data from first and second scans of an image, artifacts caused by motion between the first and second scans are reduced. Digital pixel data for the first and second scans are stored. A translational second scan is generated using the first and second scans, the translational second scan having new digital pixel data which compensates for motion artifacts between the first and second scans. The combined image data is generated by combining the digital pixel data of the first scan with the new digital pixel data of the translational second scan, with the translational second scan being weighted before it is summed with the digital pixel data of the first scan.