Abstract: A digital key sharing system includes an electronic circuit and a first server computer. The electronic circuit is configured to store a plurality of digital keys, receive a plurality of key sharing requests, and generate a plurality of features in response to the plurality of key sharing requests. The first server computer is in wireless communication with the electronic circuit and is configured to generate an alert signal in response to finding one or more anomalies in the plurality of features to initiate a notification to a primary user device associated with the electronic circuit, and send a suspend notification signal to the electronic circuit in response to the finding of the one or more anomalies in the plurality of features. The electronic circuit is further configured to suspend use of the plurality of digital keys in response to reception of the suspend notification signal.

Abstract: A digital key sharing system includes an electronic circuit and a first server computer. The electronic circuit is configured to store a plurality of digital keys, receive a plurality of key sharing requests, and generate a plurality of features in response to the plurality of key sharing requests. The first server computer is in wireless communication with the electronic circuit and is configured to generate an alert signal in response to finding one or more anomalies in the plurality of features to initiate a notification to a primary user device associated with the electronic circuit, and send a suspend notification signal to the electronic circuit in response to the finding of the one or more anomalies in the plurality of features. The electronic circuit is further configured to suspend use of the plurality of digital keys in response to reception of the suspend notification signal.

Abstract: A vehicle, operating system of a vehicle and a method of operating a vehicle is disclosed. A local electronic control unit is operated at the vehicle in order to control the vehicle. A backup electronic control unit is operated at a remote computing platform for control of the vehicle. A control of the vehicle is transferred from the local electronic control unit to the backup electronic control unit upon occurrence of a fault at the local electronic control unit.

Abstract: A method for vehicle tracking and localization includes receiving, by a controller, odometry data from a sensor of the first vehicle; geospatial data from a Global Positioning System (GPS) device of the first vehicle; inertial data from an inertial measurement unit (IMU) of the first vehicle; estimating an estimated-current location of the first vehicle and an estimated-current trajectory of the first vehicle using the odometry data from the sensor, the geospatial data from the GPS device, and the inertial data from the IMU of the first vehicle; inputting the inertial data into a Bayesian Network to determine a predicted location of the first vehicle and a predicted trajectory of the first vehicle, and updating the Bayesian Network using the estimated-current location and the estimated-current trajectory of the first vehicle using the odometry data and the geospatial data.

Abstract: A task allocation system for dynamically allocating computing tasks to networked computing resources with heterogeneous capabilities includes: a task library having a plurality of tasks, each task being one of a plurality of different implementations of an application, wherein the different implementations of an application provide different levels of accuracy and resource usage during execution, wherein the different implementations are configured based on a trade-off between level of accuracy and resource usage during execution; and a real-time scheduler module configured to monitor available computing resources and connectivity to the computing resources, receive a plurality of task requests, prioritize applications to be executed when performing the tasks wherein more critical applications are assigned a higher priority, allocate computing resources to the higher priority applications, allocate remaining computing resources to other applications, and select specific implementations of applications for ass

Abstract: Systems and methods are provided for interpreting traffic information. In one embodiment, a method includes: receiving, by a processor, visual data from a plurality of vehicles, wherein the visual data is associated with an intersection of a roadway having one or more lanes; receiving, by the processor, vehicle data from the plurality of vehicles, wherein the vehicle data is associated with the intersection of the roadway; determining, by the processor, a first state of a traffic light associated with the intersection based on the visual data; determining, by the processor, a second state of the traffic light associated with the intersection based on the vehicle data; correlating, by the processor, the first state and the second state based on a time synchronization; assigning, by the processor, the traffic light to a lane of the roadway based on the correlating; and communicating, by the processor, the traffic light to lane assignment for use in controlling a vehicle of the multiple vehicles.

Abstract: Analysis method of fuel cell input and output characteristics, which utilizes ? theorem and principle of similarity to carry out dimensional analysis and equation analysis for model parameters and governing equations respectively for a given proton exchange membrane fuel cell theoretical model, includes the steps: determine model parameters and dimensions of each parameter, and filter out basic parameters for dimensional analysis; use ? theorem to perform dimensional analysis to obtain dimensionless numbers; use principle of similarity to analyze model governing equations to obtain dimensionless numbers; compare the two sets of dimensionless numbers to determine the dimensionless number of the fuel cell model under study; define dimensionless voltage and dimensionless current to serve as the ordinate and abscissa of the dimensionless polarization curve, then any point on the dimensionless polarization curve represents a set of similar working conditions and the number and time of experiment or simulation can

Abstract: In one example implementation, a computer-implemented method includes receiving, by a host processing device, timestamps from a vehicle processing system of a target vehicle, the timestamps being determined based at least in part on parking area data for a target parking area and vehicle data for the target vehicle. The method further includes calculating, by the host processing device, an arrival rate and a service rate based on the timestamps. The method further includes calculating, by the host processing device, a probability of parking availability for the target parking area based at least in part on the arrival rate and the service rate. The method further includes controlling, by the host processing device, the vehicle based at least in part on the probability of parking availability for the target parking area.

Abstract: Embodiments include methods, systems, and computer readable storage medium for a method for providing a software update to a vehicle is disclosed. The method includes entering, by a vehicle, a wakeup state. The method further includes sending, by the vehicle, a vehicle registration request to a computing environment. The method further includes receiving, by the vehicle, a confirmation that the vehicle is to receive one or more software updates based on the vehicle registration of the vehicle. The method further includes receiving, by the vehicle, the one or more software updates from the computing environment.

Abstract: A processor-implemented method and a controller in a vehicle for estimating a wireless channel impulse response in a mobile environment are provided. The method comprises: receiving an orthogonal frequency-division multiplexing (OFDM) signal; applying a maximum likelihood estimator to the received OFDM signal to identify a data symbol that provides a smooth channel response; and estimating the channel impulse response by performing a division or reverse convolution operation between the received OFDM signal and the identified data symbol. The controller is configured to: receive an OFDM signal; apply a maximum likelihood estimator to the received OFDM signal to identify a data symbol that provides a smooth channel response; and estimate the channel impulse response by performing a division or reverse convolution operation between the received OFDM signal and the identified data symbol. The vehicle can use the estimated channel impulse response to decode data symbols from future instances of the OFDM signal.

Abstract: Presented are systems and methods for deriving speed limits of designated road segments by mining large-scale vehicle data traces. A method for controlling operation of a motor vehicle includes: determining a current location of the vehicle; determining a designated road segment corresponding to the vehicle's location; receiving host speed data indicative of the vehicle's speed while travelling on the road segment for a calibrated timeframe; receiving crowd-sourced speed data indicative of the speeds of participatory vehicles while travelling on the road segment for the calibrated timeframe; accumulating a speed distribution function for the road segment based on the host and crowd-sourced speed data; generating a finite mixture model from the speed distribution function to estimate a speed limit range; selecting a speed limit candidate from the estimated speed limit range; and transmitting command signals to a vehicle subsystem to execute a control operation based on the selected speed limit candidate.

Abstract: A computer-implemented method at a service facility for capturing vehicle state and service information (VSSI) is provided. The method includes: detecting the arrival of a vehicle at the service facility; initiating, by a processor at the service facility, the establishment of a secure communication link with the vehicle via an in-vehicle wi-fi hotspot; wirelessly retrieving, by the processor at the service facility from the vehicle, a subset of VSSI via the wi-fi hotspot, wherein the retrieved VSSI includes the subset of the VSSI that has changed since the last update of the VSSI to a cloud-based server and wherein the subset of the VSSI includes some, but not all of the VSSI; and scheduling a vehicle service based on service indications derived from the VSSI.

Abstract: A method for vehicle tracking and localization includes receiving, by a controller, odometry data from a sensor of the first vehicle; geospatial data from a Global Positioning System (GPS) device of the first vehicle; inertial data from an inertial measurement unit (IMU) of the first vehicle; estimating an estimated-current location of the first vehicle and an estimated-current trajectory of the first vehicle using the odometry data from the sensor, the geospatial data from the GPS device, and the inertial data from the IMU of the first vehicle; inputting the inertial data into a Bayesian Network to determine a predicted location of the first vehicle and a predicted trajectory of the first vehicle, and updating the Bayesian Network using the estimated-current location and the estimated-current trajectory of the first vehicle using the odometry data and the geospatial data.

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: Presented are systems and methods for extracting lane-level information of designated road segments by mining vehicle dynamics data traces.

Abstract: A task allocation system for dynamically allocating computing tasks to networked computing resources with heterogeneous capabilities includes: a task library having a plurality of tasks, each task being one of a plurality of different implementations of an application, wherein the different implementations of an application provide different levels of accuracy and resource usage during execution, wherein the different implementations are configured based on a trade-off between level of accuracy and resource usage during execution; and a real-time scheduler module configured to monitor available computing resources and connectivity to the computing resources, receive a plurality of task requests, prioritize applications to be executed when performing the tasks wherein more critical applications are assigned a higher priority, allocate computing resources to the higher priority applications, allocate remaining computing resources to other applications, and select specific implementations of applications for ass

Abstract: A method and system for gathering vehicle video data, processing the vehicle video data, and providing the processed data to a cloud layer that reconstructs the scene encountered by the vehicle. By reconstructing the encountered scene at the cloud layer, a variety of commands can be generated for that vehicle or other vehicles in the vicinity, where the commands address the conditions being experienced by the vehicles. This may be particularly useful for autonomous or semi-autonomous vehicles. If the reconstructed scene is not sufficiently accurate or detailed, one or more data extraction parameter(s) can be adjusted so that additional data is provided to the cloud layer; if the reconstructed scene is sufficiently accurate, then the data extraction parameter(s) can be adjusted so that less data is provided to the cloud layer, thus, reducing unnecessary cellular data charges.

Abstract: An autonomous vehicle (AV) perception system and method of determining an autonomous vehicle (AV) action for a host vehicle. The method includes: obtaining onboard vehicle sensor data from at least one onboard vehicle sensor, the onboard vehicle sensor is a part of vehicle electronics of the host vehicle; obtaining edge sensor data from at least one edge sensor, the edge sensor is a part of an edge layer; generating a unified perception output based on the onboard vehicle sensor data and the edge sensor data; determining an AV action for the host vehicle based on the unified perception output; and providing the AV action to the host vehicle, wherein the host vehicle is configured to carry out the AV action.

Abstract: In various embodiments, methods, systems, and vehicles are provided for distance determinations using cameras from different vehicles. The system includes a first camera onboard a first vehicle, providing a first image, the first image having therein an object of interest (OOI) for which a distance from the first vehicle is desired; and a second camera, providing second camera data including a second image having therein the OOI. The system further includes a control module configured to process and time synchronize first camera data and second camera data to generate a distance determination for the OOI, using the first camera data, the second camera data, and a distance between the first and second camera.

Abstract: In various embodiments, methods, systems, and vehicles are provided that include obtaining first camera images from a first camera onboard a first vehicle; generating, via one or more computer processors, a first situation awareness graph with respect to objects near the first vehicle, using the first camera images; obtaining second camera images from a second camera of a second device that is in proximity to the first vehicle; generating, via one or more computer processors, a second situation awareness graph with the respect to the objects, using the second camera images; and generating, via one or more computer processor, a global situation awareness graph with respect to the objects, by merging the first situation awareness graph with the second situation awareness graph, using respective first and second weights for the first and second situation awareness graphs.