Abstract: An example computer system includes memory hardware configured to store a machine learning model and historical driving data vector inputs, and processor hardware configured to execute instructions including training the machine learning model with the historical driving data vector inputs to generate a reconstructed driving data output, wherein the reconstructed driving data output includes at least one reconstruction error score indicative of a likelihood that a driving data input corresponds to a desired driving behavior, obtaining a current driving data input, and supplying the current driving data input to the machine learning model to generate a reconstruction error score based on the current driving data input. The instructions may include determining a driving score according to the reconstruction error score, identifying at least one driver notification according to the determined driving score, and transmitting the identified at least one driver notification to a computing device or display.

Abstract: A system for setting parameters within a first vehicle for a first user of the first vehicle is provided and includes a memory, transceiver, and a processing module. The memory stores normalized parameters, which are specific to the first user. Each of the normalized parameters is a parameter of the vehicle. The transceiver receives the normalized parameters from a mobile device of the first user, a server in a cloud-based network, or a portable memory device. The processing module: determines whether the first user is authorized for parameter translation services; if the first user is authorized for parameter translation services, translates the normalized parameters to resultant parameters; generates parameter recommendations based on the resultant parameters; presents the parameter recommendations to the first user; and adjusts current parameters of the first vehicle based on the resultant parameters and a received response from the first user with regards to the parameter recommendations.

Abstract: A central latency system includes a communication device configured to exchange data with an external device. The data includes current latency data, current contextual data associated with the latency data, or a combination thereof. The system further includes a controller configured to aggregate latency data in response to acquiring the current latency data, generate a latency characterization information based on the aggregated latency data, and transmit the latency characterization information to the external device.

Abstract: A roadside computing (RSC) system associated with a roadway obtains, a position of a connected road user. The RSC system is configured to identify at least one road user based on sensor data from one or more roadside sensors, determine a position of the at least one road user identified based on the sensor data, track by the RSC system, the position of the road user traveling on the roadway, determine a predicted trajectory of the road user based on the tracked position of the road user and a trajectory prediction model, and transmit information related to the predicted trajectory to the computing device associated with the connected road user.

Abstract: Systems and methods for concurrent reading and writing in shared, persistent byte-addressable non-volatile memory is described herein. One method includes in response to initiating a write sequence to one or more memory elements, checking an identifier memory element to determine whether a write sequence is in progress. In addition, the method includes updating an ingress counter. The method also includes adding process identification associated with a writer node to the identifier memory element. Next, a write operation is performed. After the write operation, an egress counter is incremented and the identifier memory element is reset to an expected value.

Abstract: Examples relate to ordering updates for nonvolatile memory accesses. In some examples, a first update that is propagated from a write-through processor cache of a processor is received by a write ordering buffer, where the first update is associated with a first epoch. The first update is stored in a first buffer entry of the write ordering buffer. At this stage, a second update that is propagated from the write-through processor cache is received, where the second update is associated with a second epoch. A second buffer entry of the write ordering buffer is allocated to store the second update. The first buffer entry and the second buffer entry can then be evicted to non-volatile memory in epoch order.

Abstract: Exemplary embodiments herein describe programming models and frameworks for providing parallel and resilient tasks. Tasks are created in accordance with predetermined structures. Defined tasks are stored as data objects in a shared pool of memory that is made up of disaggregated memory communicatively coupled via a high performance interconnect that supports atomic operations as descried herein. Heterogeneous compute nodes are configured to execute tasks stored in the shared memory. When compute nodes fail, they do not impact the shared memory, the tasks or other data stored in the shared memory, or the other non-failing compute nodes. The non-failing compute nodes can take on the responsibility of executing tasks owned by other compute nodes, including tasks of a compute node that fails, without needing a centralized manager or schedule to re-assign those tasks. Task processing can therefore be performed in parallel and without impact from node failures.

Abstract: Systems and methods for concurrent reading and writing in shared, persistent byte-addressable non-volatile memory is described herein. One method includes in response to initiating a write sequence to one or more memory elements, checking an identifier memory element to determine whether a write sequence is in progress. In addition, the method includes updating an ingress counter. The method also includes adding process identification associated with a writer node to the identifier memory element. Next, a write operation is performed. After the write operation, an egress counter is incremented and the identifier memory element is reset to an expected value.

Abstract: A central latency system includes a communication device configured to exchange data with an external device. The data includes current latency data, current contextual data associated with the latency data, or a combination thereof. The system further includes a controller configured to aggregate latency data in response to acquiring the current latency data, generate a latency characterization information based on the aggregated latency data, and transmit the latency characterization information to the external device.

Abstract: A transformation on raw data is applied to produce transformed data, where the transformation includes at least one selected from among a summary of the raw data or a transform of the raw data between different domains. In response to a query to access data, the query is processed using the transformed data.

Abstract: A vehicle application enabling system is provided and includes a latency characterization and prediction module and an application enable module. The latency characterization and prediction module includes: an estimate sample module configured to collect and store obtained latency estimates, where the obtained latency estimates are associated with transmission of signals in a network for one or more vehicle applications; a sample characterization module configured to characterized the obtained latency estimates; a trend module configured to determine a trend based on the characterized obtained latency estimates; a point projection module configured to generate projected latency estimates based on the trend; and a projection characterization module configured to characterize the projected latency estimates. The application enable module is configured to determine whether to enable the one or more vehicle applications based on the characterization of the projected latency estimates.

Abstract: A vehicle application enabling system is provided and includes a memory and initialization, latency evaluation, and application enable modules. The initialization module: receives a maximum network latency; sets a percentage of occurrences that the maximum network latency is not satisfied, a maximum false positive rate, and a maximum deviation value; and calculates a weighting factor based on the percentage of occurrences, maximum false positive rate and maximum deviation value. The latency evaluation module implements a latency evaluation algorithm, which includes: comparing one or more latency estimates to the maximum network latency to provide one or more samples; updating confusion matrix statistics based on the one or more samples; updating a probability threshold based on the maximum false positive rate; updating weighted observations based on the weighting factor; and determining a predicted decision based on the probability threshold.

Abstract: A travel route selection system is provided and includes historical latency data and travel route modules. The historical latency data module includes data and characterization modules. The data module collects historical latency data. The historical latency data are associated with transmission of signals in a network for one or more vehicle applications of a vehicle. The characterization module: obtains characteristic distributions of the historical latency data at selected locations along travel routes; based on the characteristic distributions, (i) calculates metrics along the travel routes for the vehicle, or (ii) combines the characteristic distributions to obtain an overall distribution for each of the travel routes; and based on the metrics or the overall distribution, performs a statistical process to predict latencies of the signals along each of the travel routes. The travel route module selects one of the travel routes based on the predicted latencies of the signals.

Abstract: An automatic external incident detection and reporting system for a vehicle includes at least one of a plurality of cameras and a plurality of proximity sensors, an incident determination unit, a remote vehicle or object position determination unit, and a communication unit. The incident determination unit is configured to receive signals from the at least one of the cameras and proximity sensors, detect whether an external incident has occurred, and determine a type of incident. The remote vehicle or object position determination unit is configured to receive signals from the at least one of the cameras and proximity sensors and determine an incident location. The communication unit is configured to transmit at least the incident location and the type of incident to remote vehicles, to a local access point, or to share the incident location and the type of incident through a crowd-sourcing manner if the external incident has occurred.

Abstract: Exemplary embodiments herein describe programming models and frameworks for providing parallel and resilient tasks. Tasks are created in accordance with predetermined structures. Defined tasks are stored as data objects in a shared pool of memory that is made up of disaggregated memory communicatively coupled via a high performance interconnect that supports atomic operations as descried herein. Heterogeneous compute nodes are configured to execute tasks stored in the shared memory. When compute nodes fail, they do not impact the shared memory, the tasks or other data stored in the shared memory, or the other non-failing compute nodes. The non-failing compute nodes can take on the responsibility of executing tasks owned by other compute nodes, including tasks of a compute node that fails, without needing a centralized manager or schedule to re-assign those tasks. Task processing can therefore be performed in parallel and without impact from node failures.

Abstract: An automatic traffic incident detection and reporting system for a vehicle may include at least one of a plurality of cameras and a plurality of proximity sensors, an incident determination unit, a remote vehicle position determination unit, and a communication unit. The incident determination unit is configured to receive signals from the at least one of the plurality of cameras and the plurality of proximity sensors, detect whether incident involving at least one remote vehicle has occurred, and categorize the incident. The remote vehicle position determination unit is configured to receive signals from the at least one of the plurality of cameras and the plurality of proximity sensors and determine a location of the incident. The communication unit is configured to transmit data related to the incident to at least one of a cloud server and an emergency service provider.

Abstract: An automatic external incident detection and reporting system for a vehicle includes at least one of a plurality of cameras and a plurality of proximity sensors, an incident determination unit, a remote vehicle or object position determination unit, and a communication unit. The incident determination unit is configured to receive signals from the at least one of the cameras and proximity sensors, detect whether an external incident has occurred, and determine a type of incident. The remote vehicle or object position determination unit is configured to receive signals from the at least one of the cameras and proximity sensors and determine an incident location. The communication unit is configured to transmit at least the incident location and the type of incident to remote vehicles, to a local access point, or to share the incident location and the type of incident through a crowd-sourcing manner if the external incident has occurred.

Abstract: An automatic traffic incident detection and reporting system for a vehicle may include at least one of a plurality of cameras and a plurality of proximity sensors, an incident determination unit, a remote vehicle position determination unit, and a communication unit. The incident determination unit is configured to receive signals from the at least one of the plurality of cameras and the plurality of proximity sensors, detect whether incident involving at least one remote vehicle has occurred, and categorize the incident. The remote vehicle position determination unit is configured to receive signals from the at least one of the plurality of cameras and the plurality of proximity sensors and determine a location of the incident. The communication unit is configured to transmit data related to the incident to at least one of a cloud server and an emergency service provider.

Abstract: A system for setting parameters within a first vehicle for a first user of the first vehicle is provided and includes a memory, transceiver, and a processing module. The memory stores normalized parameters, which are specific to the first user. Each of the normalized parameters is a parameter of the vehicle. The transceiver receives the normalized parameters from a mobile device of the first user, a server in a cloud-based network, or a portable memory device. The processing module: determines whether the first user is authorized for parameter translation services; if the first user is authorized for parameter translation services, translates the normalized parameters to resultant parameters; generates parameter recommendations based on the resultant parameters; presents the parameter recommendations to the first user; and adjusts current parameters of the first vehicle based on the resultant parameters and a received response from the first user with regards to the parameter recommendations.

Abstract: Determining cache value currency using persistent markers is disclosed herein. In one example, a cache entry is retrieved from a local cache memory device. The cache entry includes a key, a value to be used by the computing device, and a marker flag to determine whether the cache entry is current. The local cache memory device also includes a marker location that indicates a location of a marker in a shared persistent fabric-attached memory (FAM). Using a marker location, the marker is retrieved from the shared persistent FAM. From the marker and the marker flag, it is determined whether the cache entry is current. The shared FAM pool is connected to the local cache memory devices of multiple computing devices.