Abstract: Devices, computer-readable media, and methods for making a cache admission decision regarding a video chunk are described. For instance, a processing system including at least one processor may obtain a request for a first chunk of a first video, determine that the first chunk is not stored in a cache, and apply, in response to the determining that the first chunk is not stored in the cache, a classifier to predict whether the first chunk will be re-requested within a time horizon, where the classifier is trained in accordance with a set of features associated with a plurality of chunks of a plurality of videos. When it is predicted via the classifier that the first chunk will be re-requested within the time horizon, the processing system may store the first chunk in the cache.

Abstract: A network function virtualization platform for providing network functions for traffic flow of a network is disclosed. The platform may be added to a Function as a Service (FaaS) network infrastructure. A worker node includes a core executing network functions, a scheduler, and an agent. A first network function includes code for executing the network function and a runtime. An ingress module receives network traffic flow and separates packets for performance of the first network function. A controller is coupled to the ingress module and the agent. The controller controls the ingress module to route the separated packets to the worker node. The scheduler schedules execution of the first network function on the packets. The agent assigns execution of the first network function to the core of the worker node.

Abstract: A system for creating a 3D volumetric scene includes a first visual sensor positioned onboard a first vehicle to obtain first visual images, first motion sensors positioned onboard the first vehicle to obtain first motion data, a first computer processor positioned onboard the first vehicle and adapted to generate a first scene point cloud, a second visual sensor positioned onboard a second vehicle to obtain second visual images, second motion sensors positioned onboard the second vehicle to obtain second motion data, and a second computer processor positioned onboard the second vehicle and adapted to generate a second scene point cloud, the first and second computer processors further adapted to send the first and second scene point clouds to a third computer processor, and the third computer processor located within an edge/cloud infrastructure and adapted to create a stitched point cloud.

Abstract: A vehicle is described, and includes an on-board controller, an extra-vehicle communication system, a GPS sensor, a spatial monitoring system, and a navigation system that employs an on-vehicle navigation map. Operation includes capturing a 3D sensor representation of a field of view and an associated GPS location, executing a feature extraction routine, executing a semantic segmentation of the extracted features, executing a simultaneous location and mapping (SLAM) of the extracted features, executing a context extraction from the simultaneous location and mapping of the extracted features, and updating the on-vehicle navigation map based thereon. A parsimonious map representation is generated based upon the updated on-vehicle navigation map, and is communicated to a second, off-board controller. The second controller executes a sparse map stitching to update a base navigation map based upon the parsimonious map representation. The on-vehicle navigation map is updated based upon the off-board navigation map.

Abstract: Devices, computer-readable media, and methods for making a cache admission decision regarding a video chunk are described. For instance, a processing system including at least one processor may obtain a request for a first chunk of a first video, determine that the first chunk is not stored in a cache, and apply, in response to the determining that the first chunk is not stored in the cache, a classifier to predict whether the first chunk will be re-requested within a time horizon, where the classifier is trained in accordance with a set of features associated with a plurality of chunks of a plurality of videos. When it is predicted via the classifier that the first chunk will be re-requested within the time horizon, the processing system may store the first chunk in the cache.

Abstract: Aspects of the subject disclosure may include, for example, allocating, by a processing system including a processor, a first subset of resources to a first plurality of applications and a second subset of the resources to a second plurality of applications, wherein the allocating is based on respective statuses associated with the first plurality of applications and the second plurality of applications, and assigning, by the processing system, a respective bitrate to each application of the first plurality of applications, wherein the assigning of the respective bitrate to each application of the first plurality of applications is based on: a first threshold associated with a re-buffering of content, and a second threshold associated with the statuses. Other embodiments are disclosed.

Abstract: A vehicle is described, and includes an on-board controller, an extra-vehicle communication system, a GPS sensor, a spatial monitoring system, and a navigation system that employs an on-vehicle navigation map. Operation includes capturing a 3D sensor representation of a field of view and an associated GPS location, executing a feature extraction routine, executing a semantic segmentation of the extracted features, executing a simultaneous location and mapping (SLAM) of the extracted features, executing a context extraction from the simultaneous location and mapping of the extracted features, and updating the on-vehicle navigation map based thereon. A parsimonious map representation is generated based upon the updated on-vehicle navigation map, and is communicated to a second, off-board controller. The second controller executes a sparse map stitching to update a base navigation map based upon the parsimonious map representation. The on-vehicle navigation map is updated based upon the off-board navigation map.

Abstract: Aspects of the subject disclosure may include, for example, allocating, by a processing system including a processor, a first subset of resources to a first plurality of applications and a second subset of the resources to a second plurality of applications, wherein the allocating is based on respective statuses associated with the first plurality of applications and the second plurality of applications, and assigning, by the processing system, a respective bitrate to each application of the first plurality of applications, wherein the assigning of the respective bitrate to each application of the first plurality of applications is based on: a first threshold associated with a re-buffering of content, and a second threshold associated with the statuses. Other embodiments are disclosed.

Abstract: Systems and method are provided for controlling an autonomous vehicle. In one embodiment, a method includes: receiving sensor data from a sensor of the vehicle; determining a three dimensional point cloud map segment from the sensor data; determining a vehicle pose associated with the three-dimensional point cloud map segment; determining a pose difference based on the vehicle pose, another vehicle pose, and a two-step process, wherein the two-step process includes computing a coarse-granularity pose difference, and computing a fine-granularity pose difference; aligning the three dimensional point cloud map segment with another three dimensional point cloud map segment associated with the other vehicle pose based on the pose difference; and controlling the vehicle based on the aligned three dimensional point cloud map segments.

Abstract: Systems and method are provided for controlling an autonomous vehicle. In one embodiment, a method includes: receiving sensor data from a sensor of the vehicle; determining a three dimensional point cloud map segment from the sensor data; determining a vehicle pose associated with the three-dimensional point cloud map segment; determining a pose difference based on the vehicle pose, another vehicle pose, and a two-step process, wherein the two-step process includes computing a coarse-granularity pose difference, and computing a fine-granularity pose difference; aligning the three dimensional point cloud map segment with another three dimensional point cloud map segment associated with the other vehicle pose based on the pose difference; and controlling the vehicle based on the aligned three dimensional point cloud map segments.

Abstract: A system and method is taught for vehicles controlled by automated driving systems, particularly those configured to automatically control vehicle steering, acceleration, and braking during a drive cycle without human intervention. In particular, the present disclosure teaches a system and method for generation situational awareness and path planning data and transmitting this information via vehicle to vehicle communications where one vehicle has an obstructed view to objects not within an obstructed view of a second vehicle.

Abstract: A system and method is taught for vehicles controlled by automated driving systems, particularly those configured to automatically control vehicle steering, acceleration, and braking during a drive cycle without human intervention. In particular, the present disclosure teaches a system and method for generation situational awareness and path planning data and transmitting this information via vehicle to vehicle communications where one vehicle has an obstructed view to objects not within an obstructed view of a second vehicle.

Abstract: A method is intended for computing online channel loss rate and collision loss rate of at least one communication link established between nodes (N1, N2) of a network (WN) using a random access MAC protocol. This method comprises the steps of i) dividing time in probing windows and transmitting a chosen number S of probe packets during each probing window from a transmitter node (N1) to a receiver node (N2) linked therebetween, ii) measuring a packet loss rate from probe packets lost on this communication link during a probing window, iii) scanning each probing window with smaller sliding windows, each having a size Wk smaller than S, to identify the sliding window during which only channel losses occur, and then for computing a channel loss rate on this communication link from this identified sliding window, and iv) computing a collision loss rate on this communication link by subtracting the computed channel loss rate from the measured packet loss rate.

Abstract: A method is intended for computing online a feasible rates region in a network using a random access MAC protocol and comprising nodes having links there between. This method comprises the steps of i) determining, for each link, a primary extreme point corresponding to a maximum output rate when this link transmits alone at a maximum input rate, and ii) determining secondary extreme points by combining these primary extreme points with a chosen interference model, these primary and secondary extreme points defining a boundary of a feasible rates region.

Abstract: A method is intended for computing online channel loss rate and collision loss rate of at least one communication link established between nodes (N1, N2) of a network (WN) using a random access MAC protocol. This method comprises the steps of i) dividing time in probing windows and transmitting a chosen number S of probe packets during each probing window from a transmitter node (N1) to a receiver node (N2) linked therebetween, ii) measuring a packet loss rate from probe packets lost on this communication link during a probing window, iii) scanning each probing window with smaller sliding windows, each having a size Wk smaller than S, to identify the sliding window during which only channel losses occur, and then for computing a channel loss rate on this communication link from this identified sliding window, and iv) computing a collision loss rate on this communication link by subtracting the computed channel loss rate from the measured packet loss rate.

Abstract: A method is intended for computing online channel loss rate and collision loss rate of at least one communication link established between nodes of a network using a random access MAC protocol. This method comprises the steps of i) dividing time in probing windows and transmitting a chosen number S of probe packets during each probing window from a transmitter node to a receiver node linked therebetween, ii) measuring a packet loss rate from probe packets lost on this communication link during a probing window, iii) scanning each probing window with smaller sliding windows, each having a size Wk smaller than S, to identify the sliding window during which only channel losses occur, and then for computing a channel loss rate on this communication link from this identified sliding window, and iv) computing a collision loss rate on this communication link by subtracting the computed channel loss rate from the measured packet loss rate.

Abstract: A method is intended for computing online channel loss rate and collision loss rate of at least one communication link established between nodes of a network using a random access MAC protocol. This method comprises the steps of i) dividing time in probing windows and transmitting a chosen number S of probe packets during each probing window from a transmitter node to a receiver node linked therebetween, ii) measuring a packet loss rate from probe packets lost on this communication link during a probing window, iii) scanning each probing window with smaller sliding windows, each having a size Wk smaller than S, to identify the sliding window during which only channel losses occur, and then for computing a channel loss rate on this communication link from this identified sliding window, and iv) computing a collision loss rate on this communication link by subtracting the computed channel loss rate from the measured packet loss rate.

Abstract: A method is intended for computing online a feasible rates region in a network using a random access MAC protocol and comprising nodes having links there between. This method comprises the steps of i) determining, for each link, a primary extreme point corresponding to a maximum output rate when this link transmits alone at a maximum input rate, and ii) determining secondary extreme points by combining these primary extreme points with a chosen interference model, these primary and secondary extreme points defining a boundary of a feasible rates region.