Abstract: A method for measuring a distance is provided. The method includes receiving a fine time measurement (FTM) message, extracting channel state information (CSI) from the FTM message, determining with a channel condition classifier a probability that the FTM message is multi-path based on the corresponding CSI, and discarding the FTM message in response to the probability that the FTM message is multi-path is above a threshold level of probability.

Abstract: A method includes calculating a first adaptive threshold based on uncertainty of a first parameter of a first collaborative perception message (CPM) from a first node and uncertainty of the first parameter of a second CPM from a second node, calculating a second adaptive threshold based on uncertainty of a second parameter of the first CPM and uncertainty of the second parameter of the second CPM, obtaining a first association matrix by filtering out one or more pairs whose score is greater than the first adaptive threshold, obtaining a second association matrix by filtering out one or more pairs whose score is greater than the second adaptive threshold, obtaining a fused association matrix based on the first association matrix and the second association matrix, and implementing a fusion algorithm on the fused associated matrix to obtain correspondence identification among objects.

Abstract: A method of matching objects in collaborative perception messages is provided. The method includes obtaining a first collaborative perception message (CPM) message from a first node, obtaining a second CPM from a second node, calculating an adaptive threshold based on uncertainty of the first CPM and uncertainty of the second CPM, calculating scores for pairs of objects, each of the pairs of objects including one object in the first CPM and one object in the second CPM, filtering out one or more pairs whose score is greater than the adaptive threshold to obtain a filtered matrix; and implementing a fusion algorithm on the filtered matrix to obtain correspondence identification among objects.

Abstract: A method for measuring a distance is provided. The method includes receiving a fine time measurement (FTM) message, extracting channel state information (CSI) from the FTM message, determining with a channel condition classifier a probability that the FTM message is multi-path based on the corresponding CSI, and discarding the FTM message in response to the probability that the FTM message is multi-path is above a threshold level of probability.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing systems, methods, and structures that advantageously are employed to determine the location and depth of underground fiber-optic facilities that may be carrying telecommunications traffic.

Abstract: A method of matching objects in collaborative perception messages is provided. The method includes obtaining a first collaborative perception message (CPM) message from a first node, obtaining a second CPM from a second node, calculating an adaptive threshold based on uncertainty of the first CPM and uncertainty of the second CPM, calculating scores for pairs of objects, each of the pairs of objects including one object in the first CPM and one object in the second CPM, filtering out one or more pairs whose score is greater than the adaptive threshold to obtain a filtered matrix; and implementing a fusion algorithm on the filtered matrix to obtain correspondence identification among objects.

Abstract: A method includes calculating a first adaptive threshold based on uncertainty of a first parameter of a first collaborative perception message (CPM) from a first node and uncertainty of the first parameter of a second CPM from a second node, calculating a second adaptive threshold based on uncertainty of a second parameter of the first CPM and uncertainty of the second parameter of the second CPM, obtaining a first association matrix by filtering out one or more pairs whose score is greater than the first adaptive threshold, obtaining a second association matrix by filtering out one or more pairs whose score is greater than the second adaptive threshold, obtaining a fused association matrix based on the first association matrix and the second association matrix, and implementing a fusion algorithm on the fused associated matrix to obtain correspondence identification among objects.

Abstract: A method for the non-destructive localization of underground optical fiber cables that utilizes distributed fiber optic sensing (DFOS) and time difference of arrival (TDOA) data associated with received signals of impulsive surface vibrations to localize the underground optical fiber cable by solving non-linear least square optimization problem(s). The method utilizes only TDOA information of received signals of impulsive surface vibrations to determine such localizations and does not require synchronization between a vibration source and DFOS receiver. The method may employ man-made impulsive vibrations to determine the localization. In operation the method involves 1) impulse-like vibration excitation and signal collection; 2) TDOA estimation of the received signals from DFOS; and 3) non-linear least square optimization applied to the TDOA information for underground optical fiber localization.

Abstract: A system for correcting HD map images is provided. The system is configured to: receive, from a plurality of vehicles, ground view image data of a map anomaly hotspot captured by the vehicles; convert the ground view image data to bird view image data using homography; remove outliers from the bird view image data; apply an error reduction algorithm; stitch together a sequence of consecutive images from the image data after error reduction to produce a high-definition image, wherein each of the consecutive images has been generated through converting ground view image data to bird view image data using homography, removing outliers from the bird view image data, and applying an error reduction algorithm; compare the stitched sequence of consecutive images to a high-definition image of the hotspot to determine revisions to the high-definition image; and provide revised high-definition image data for use in navigating near the map anomaly hotspot.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing systems, methods, and structures that advantageously are employed to determine the location and depth of underground fiber-optic facilities that may be carrying telecommunications traffic.

Abstract: Aspects of the present disclosure describe systems, methods and structures providing non-destructive localization of underground optical fiber cables utilizing distributed fiber optic sensing (DFOS) and time difference of arrival techniques.

Abstract: A system for correcting HD map images is provided. The system is configured to: receive, from a plurality of vehicles, ground view image data of a map anomaly hotspot captured by the vehicles; convert the ground view image data to bird view image data using homography; remove outliers from the bird view image data; apply an error reduction algorithm; stitch together a sequence of consecutive images from the image data after error reduction to produce a high-definition image, wherein each of the consecutive images has been generated through converting ground view image data to bird view image data using homography, removing outliers from the bird view image data, and applying an error reduction algorithm; compare the stitched sequence of consecutive images to a high-definition image of the hotspot to determine revisions to the high-definition image; and provide revised high-definition image data for use in navigating near the map anomaly hotspot.

Abstract: A system and method of operating a vehicle. The system includes a two-dimensional imager, a three-dimensional imager, and at least one processor. The two-dimensional imager obtains a two-dimensional image of an environment surrounding the vehicle, wherein the environment includes an object. The three-dimensional imager obtains a three-dimensional (3D) point cloud of the environment. The at least one processor identifies the object from the 2D image and assigns the identification of the object to a selected point of the 3D point cloud.

Abstract: A system and method of operating a vehicle. The system includes a two-dimensional imager, a three-dimensional imager, and at least one processor. The two-dimensional imager obtains a two-dimensional image of an environment surrounding the vehicle, wherein the environment includes an object. The three-dimensional imager obtains a three-dimensional (3D) point cloud of the environment. The at least one processor identifies the object from the 2D image and assigns the identification of the object to a selected point of the 3D point cloud.