Abstract: A method for operating a digital key configured to wirelessly connect to a vehicle includes receiving wireless communication signal data and vehicle data. The method further includes ranking, using a machine learning model, a plurality of wireless communication technologies for wirelessly connecting a mobile device to the vehicle and selecting one of the plurality of wireless communication technologies based on ranking of the plurality of wireless communication technologies. Further, the method includes establishing a wireless communication between the mobile device and the vehicle using the selected wireless communication technology. A digital key application is running on a mobile device, thereby allowing the digital key application to operate as a digital key for the vehicle after the wireless communication between the mobile device and the vehicle has been established.

Abstract: A system for optimizing traffic signal timing using telemetry includes: wirelessly connected devices (WCDs), traffic operations centers (TOCs) controlling one or more traffic signals, and control modules storing and continuously executing control logic in a closed loop. The control logic continuously receives real-time WCD telemetry data and performs real-time processing of the telemetry data to match the telemetry data to physical locations. Processed telemetry data is aggregated to determine WCD trajectories. The system selectively performs time-of-day (TOD), offset, and green split optimizations, and receives the WCD trajectories and outputs of the TOD, offset, and green split optimization within an API. The system generates an optimized signal plan from data from the API, and verifies the optimized signal plan. The optimized signal plan is received by the TOCs and selectively uploaded to the traffic signals.

Abstract: A method for video streaming includes receiving a video service request from a vehicle. The video service request is indicative of a request for a video content. The method further includes receiving a bandwidth prediction of a cellular network from the vehicle, and a list of available video codec configurations for a video content associated with the video service request. Moreover, method includes determining a selected video codec configuration based on the bandwidth prediction of the vehicle, determining that the selected video codec configuration is part of the list of available video codec configurations, and providing the video content to the vehicle using the selected video codec configuration.

Abstract: A method for generating a traffic signal timing plan may include determining a current traffic metric. The method further may include identifying a timing plan generation trigger. The method further may include determining at least one historic traffic metric. The method further may include generating the traffic signal timing plan based at least in part on at least one of: the current traffic metric, the timing plan generation trigger, and the at least one historic traffic metric. The method further may include adjusting an operation of one or more traffic signals based at least in part on the traffic signal timing plan.

Abstract: An object detection system for a vehicle that estimates a location of a target object located in an environment surrounding the vehicle includes an ultra-wide band (UWB) sensor network including three or more anchors mounted to the vehicle in wireless communication with a tag mounted to the target object, a non-stereo camera system that captures image data representing the target object located in the environment surrounding the vehicle, and one or more controllers in electronic communication with the UWB sensor network and the non-stereo camera system. The one or more controllers includes one or more processors that execute instructions to fuse together a camera-based location of the target object and a UWB-based location of the target object by a Bayesian filter to estimate the location of the target object.

Abstract: A method and system of enabling interactive learning for connected vehicles. The system includes a vehicle data hub (VDH) component, a system learning and update (SLU) component, and an application delivery network (ADN). The VDH component enables cloud-coordinated data collection by the connected vehicles. The VDH component includes a vehicle based portion having a smart buffer in communications with a vehicle communications system and a VDH cloud based service portion. The SLU component is a cloud based service configured to manage data campaigns and specific triggers for efficient collection of data, processing the data, and deploy updated and/or new applications based on the processed data to connected vehicles. The ADN component is disposed on the vehicle and prioritize data campaign requests with respect to other active vehicle services.

Abstract: A method for requesting and receiving vehicle telemetry data may include receiving telemetry data from one or more of a plurality of vehicles. The method further may include determining a traffic metric based at least in part on the telemetry data. The method further may include comparing the traffic metric to a traffic metric target. The method further may include requesting telemetry data from one or more of the plurality of vehicles based at least in part on comparing the traffic metric to the traffic metric target.

Abstract: A system for measuring insurability based on relative vehicle operator performance (MIROP) includes sensors capturing host and remote vehicle information, and information about an environment of the host and remote vehicles. A controller executes a MIROP application that identifies event information within data obtained from the sensors, transmits, the event information to a cloud computing server, assesses a physical location and proximity of the host vehicle to remote vehicles participating in the system. The MIROP application assesses a road surface condition of a road segment upon which the host vehicle is traveling, estimates a traffic density on the road segment, aggregates host vehicle behavioral data and identifies event IDs within the behavioral data. The MIROP application computes a vehicle operator insurability score and automatically notifies an insurance carrier of the score as well as automatically presenting the score and suggestions to improve the score to a host vehicle operator.

Abstract: A method for crowdsourced detection of a dynamic road feature includes detecting the dynamic road feature using a sensor of a vehicle and communicating with a dynamic road feature database of a remote server. Further, the method includes uploading a feature data to the dynamic road feature database of the remote server in response to detecting the dynamic road feature. The feature data includes information about the road dynamic feature previously detected by the sensor of the vehicle. Also, the method further includes controlling the movement of the vehicle using the feature data.

Abstract: A collaborative perception system that creates a cooperative perception map based on perception data collected by a plurality of vehicles and includes one or more central computers in wireless communication with one or more controllers of each of the plurality of vehicles located in an environment containing a plurality of static roadside objects. The one or more central computers executes instructions to rank each static roadside object in the environment based on a respective utility function value and create the cooperative perception map by annotating map data of the environment based on a respective rank and geographic location of each of the static roadside objects located in the environment.

Abstract: A system integrating vehicle sensors with remote system sensors includes a vehicle having a controller communicating with a vehicle sensor generating data associated with a vehicle threat alert. A vehicle communication device communicating with the vehicle controller and transmitting vehicle sensor data. A remote system including a controller communicating with a remote sensor generating data associated with a remote threat alert. A communication hub communicating with the remote controller and the vehicle communication device. The vehicle communicating with the remote system through the communication hub, sharing vehicle threat alerts with the remote system, receiving and reviewing remote threat alerts from the remote system and modifying an operational mode of the vehicle sensor based on remote threat alerts. The remote network controller reviewing received vehicle threat alerts and modifying a detection mode of the remote system based on the received vehicle threat alerts.

Abstract: Systems and methods for diagnosing sensor performance of a UWB sensor localization are provided. The system comprises a UWB tag, at least four UWB anchors, and a gateway. The gateway comprises an ECU arranged to receive sensor signals from the UWB anchors. The ECU comprises a preprocessing module arranged to align the sensor signals defining aligned data and is arranged to determine intersections of the aligned data defining points of intersections. The system further comprises a clustering module arranged to cluster the points of intersections defining at least one cluster of points of the UWB anchors to calculate a clustering quality and a clustering variance of each of the at least one cluster. The ECU is arranged to find a clustering contribution of each anchor defining a first contribution low of one of the anchors and is arranged to determine an erratic anchor based the first contribution low.

Abstract: A system for predicting a user plane using control plane features and Granger causality includes a host device a cloud computing server and controllers. The controllers execute control logic including a prediction application (PA) that obtains sensor data from the sensors, sends control and user plane data to infrastructure, and to the cloud computing servers, and accesses prior knowledge data stored within memory of the cloud computing servers. Additional control logic performs a Granger causality test on the user and control plane data, and utilizes a prediction model to generate a prediction from fused user and control plane data. A prediction verifier is applied to the prediction from the prediction model, and the PA enables the host device to make reliable predictions that allow the host device to adapt to dynamic wireless communications network conditions even when a best possible model is not fully trained.

Abstract: A system for resolving discrepancies in map data includes one or more central computers in wireless communication with one or more vehicles. The one or more central computers are programmed to receive a first map dataset and a second map dataset. The one or more central computers are further programmed to receive a plurality of crowdsourced map datasets. Each of the plurality of crowdsourced map datasets represents the predefined geographical area. The one or more central computers are further programmed to compare each of the plurality of crowdsourced map datasets with the first map dataset and the second map dataset to determine one or more common lane lines. The one or more central computers are further programmed to determine a fused map dataset based on the first map dataset, the second map dataset, the plurality of crowdsourced map datasets, and the one or more common lane lines.

Abstract: A system for managing wireless connections for a vehicle may include one or more wireless devices, a vehicle communication system, and a vehicle controller in electrical communication with the vehicle communication system. The vehicle controller is programmed to determine a selected device of the one or more wireless devices with which to establish a connection using the vehicle communication system. The vehicle controller is further programmed to establish a wireless connection between the selected device and the vehicle communication system in response to determining the selected device.

Abstract: A system for fusing two or more versions of map data together includes one or more central computers that receive road network data representing a road network for a predefined geofenced area. The central computers receive road network data that includes a discrete random curve that represents lane markings. The discrete random curve includes a plurality of state vectors that are each defined by a respective location and tangent angle. The central computers estimate the position for the state vectors of the discrete random curve based on a signed distance and the tangent angle by minimizing a spatial Kalman filter cost function and execute a Kalman smoothening function to estimate the position and the tangent angle for the state vectors that are part of the discrete random curve, where the state vectors each represent a map point of the fused map data.

Abstract: A vehicle system is provided for correcting in real-time a camera-based estimated position of a road object. The system includes a camera for generating an image input signal including image sensor data associated with the road object. The system further includes one or more input devices for generating a vehicle input signal including vehicle sensor data associated with a position, a speed, and a heading of the vehicle. The system further includes a computer, which includes one or more processors and a non-transitory computer readable medium (CRM) storing instructions. The processor is programmed to match the image sensor data and the vehicle sensor data to one another based on a common time of collection. The processor is further programmed to determine an error model and a deviation of a current camera-based position from a predicted position. The processor is further programmed to update the error model based on the deviation.

Abstract: A method of creating a high-definition (HD) map of a roadway includes receiving a multi-layer probability density bitmap. The multi-layer probability density bitmap represents a plurality of lane lines of the roadway sensed by a plurality of sensors of a plurality of vehicles. The multi-layer probability density bitmap includes a plurality of points. The method further includes recursively conducting a hill climbing search using the multi-layer probability density bitmap to create a plurality of lines. In addition, the method includes creating the HD map of the roadway using the plurality of lines determined by the hill climbing search.

Abstract: A method for merging lane line maps includes determining a road topology of a road segment. The method also includes identifying a plurality of fused points based at least in part on the road topology and based at least in part on a first lane line map of the road segment and a second lane line map of the road segment. The method also includes forming a fused lane line map based at least in part on the plurality of fused points. The method also includes performing a first action based at least in part on the fused lane line map.

Abstract: A method to identify lane-level road congestion includes: modeling a lane-level congestion evolution process as a 2-dimensional (2D) Markov chain having multiple vehicles moving through a predetermined segment of a road; aggregating a volume of the multiple vehicles over a predetermined unit of time moving through the predetermined segment of a road; populating a lane congestion map identifying individual road lanes having differing levels of congestion; directing an output of the lane congestion map and an output of a lane congestion model to a lane routing engine; and identifying routes and route changes for a host vehicle to apply to improve a host vehicle estimated time of arrival (ETA) at a predetermined finish location.