Abstract: Connected vehicles and methods described herein provide for message sender identification. Connected vehicles and methods disclosed herein can include generating a message based hyper-graph based on the positions of connected elements of a road traffic network as communicated to the connected vehicle by the connected elements. Connected vehicles and methods disclosed herein can include generating, a perception based hyper-graph based on the perceived positions of at least a subset of the elements of the road traffic network as determined by a sensor of the vehicle. Connected vehicles and methods disclosed herein can include matching a node of the message-based hyper-graph to a corresponding node of the perception-based hyper-graph to determine the sender of a received message.

Abstract: The disclosure includes implementations for providing a recommendation to a driver of a second DSRC-equipped vehicle. The recommendation may describe an estimate of how long it would take the second DSRC-equipped vehicle to receive a roadside service from a drive-through business. A method according to some implementations may include receiving, by the second DSRC-equipped vehicle, a Dedicated Short Range Communication message (“DSRC message”) that includes path history data. The path history data may describe a path of a first DSRC-equipped vehicle over a plurality of different times while the first DSRC-equipped vehicle is located in a queue of the drive-through business. The method may include determining delay time data for the second DSRC-equipped vehicle based on the path history data for the first DSRC-equipped vehicle. The delay time data may describe the estimate. The method may include providing the recommendation to the driver. The recommendation may include the estimate.

Abstract: A method for data sharing between a transmitter vehicle and a receiver vehicle includes obtaining sensor data, encoding the sensor data into structured data having a set of features and a set of channels, generating a channel attention map based on inter-channel relationships of the set of features, generating weights corresponding to channels of the channel attention map, selecting one or more channels among the set of channels based on the weights corresponding to the set of channels, and transmitting the selected channels to the receiver vehicle.

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: One example embodiment provides one or more of: measuring, via a user equipment (UE), sidelink received signal strength indicator (SL-RSSI) values and sidelink reference signal power received (SL-RSRP) values from a current window of a communication channel shared among a plurality of UEs, measuring, via the UE, the SL-RSRP values from a frequency-domain and a time-domain of a current window of a communication channel shared among the plurality of UEs, excluding a first subset of communication resources from a next window of the communication channel based on the RSSI values in the current window, excluding a first subset of communication resources from a next window of the communication channel based on the SL-RSRP values from a time-domain, excluding a second subset of communication resources from the next window of the communication channel based on the RSRP values, excluding a second subset of communication resources from the next window of the communication channel based on the SL-RSRP values from a freq

Abstract: One example embodiment provides one or more of: measuring, via a user equipment (UE), sidelink received signal strength indicator (SL-RSSI) values and sidelink reference signal power received (SL-RSRP) values from a current window of a communication channel shared among a plurality of UEs, excluding a first subset of communication resources from a next window of the communication channel based on the SL-RSSI values in the current window, excluding a second subset of communication resources from the next window of the communication channel based on the SL-RSRP values, and selecting and reserving a resource(s) remaining in the next window of the communication channel after exclusion of the first and second subsets of communication resources from the next window.

Abstract: The disclosure includes embodiments for adaptive sensor data sharing by a connected vehicle. A method includes calculating, by a processor of an ego vehicle, a view angle overlap between a first sensor of the ego vehicle and a second sensor of a roadway device, wherein the view angle overlap is an amount of sensor view overlap shared by first sensor and the second sensor. The method includes determining an amount of ego sensor data to share with the roadway device based on the view angle overlap. The method includes building a sharing message that includes the amount of ego sensor data in a payload of the sharing message. The method includes transmitting, by a communication unit of the ego vehicle, the sharing message to the roadway device.

Abstract: The disclosure includes embodiments for a location data correction service for connected vehicles. A method includes receiving, by an operation center via a serverless ad-hoc vehicular network, a first wireless message that includes legacy location data that describes a geographic location of a legacy vehicle. The method includes causing a rich sensor set included in the operation center to record sensor data describing the geographic locations of objects in a roadway environment. The method includes determining correction data that describes a variance between the geographic location of the legacy vehicle as described by the sensor data and the legacy location data. The method includes transmitting a second wireless message to the legacy vehicle, wherein the second wireless message includes the correction data so that the legacy vehicle receives a benefit by correcting the legacy location data to minimize the variance.

Abstract: A vehicle includes a processor configured to transmit a mmWave beacon signal during a probe phase of a first period; receive one or more mmWave beacon signals from one or more vehicles; generate a mmWave communication intention message for another period that is after the first period based on the received one or more mmWave beacon signals; and broadcast, during the first period, a packet including a mmWave transmission schedule for the another period generated based on the mmWave communication intention message.

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: The disclosure describes embodiments for modifying a vehicle component of an ego vehicle based on ranging and misbehavior information determined from digital data included in a Vehicle-to-Everything (V2X) message. In some embodiments, a method includes generating Received Signal Strength (RSS) data describing an RSS value for the V2X message which is originated by a remote vehicle. The method includes determining range data corresponding to the RSS value describing a first range from the ego vehicle to the remote vehicle. The method includes determining that the remote vehicle is providing inaccurate sensor data (an example of misbehavior information) by comparing the first range to a second range which is described by the sensor data which is extracted from the V2X message. The method includes modifying an operation of the vehicle component so that the vehicle component does not consider the sensor data that is provided by the remote vehicle.

Abstract: The disclosure includes embodiments for adaptive sensor data sharing by a connected vehicle. A method includes calculating, by a processor of an ego vehicle, a view angle overlap between a first sensor of the ego vehicle and a second sensor of a roadway device, wherein the view angle overlap is an amount of sensor view overlap shared by first sensor and the second sensor. The method includes determining an amount of ego sensor data to share with the roadway device based on the view angle overlap. The method includes building a sharing message that includes the amount of ego sensor data in a payload of the sharing message. The method includes transmitting, by a communication unit of the ego vehicle, the sharing message to the roadway device.

Abstract: A vehicle for interpreting a traffic scene is provided. The vehicle includes one or more sensors configured to capture an image of an external view of the vehicle, and a controller. The controller is configured to obtain the captured image of the external view of the vehicle from the one or more sensors, segment a plurality of instances from the captured image, determine relational information among the plurality of instances, and generate a hyper graph including a plurality of nodes representing the plurality of instances and a plurality of edges representing the relational information among the plurality of instances.

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: Connected vehicles and methods described herein provide for message sender identification. Connected vehicles and methods disclosed herein can include generating a message based hyper-graph based on the positions of connected elements of a road traffic network as communicated to the connected vehicle by the connected elements. Connected vehicles and methods disclosed herein can include generating, a perception based hyper-graph based on the perceived positions of at least a subset of the elements of the road traffic network as determined by a sensor of the vehicle. Connected vehicles and methods disclosed herein can include matching a node of the message-based hyper-graph to a corresponding node of the perception-based hyper-graph to determine the sender of a received message.

Abstract: The disclosure includes embodiments for pedestrian navigation by a group of connected vehicles executing a collaborative computing process. In some embodiments, a method includes analyzing pedestrian data generated by a pedestrian device and sensor data generated by the group of connected vehicles to determine digital twin data from a set that correlates with a scenario described by the pedestrian data and the sensor data. The digital twin data is an output of a historical digital twin simulation. The method includes predicting, based on the digital twin data, that the pedestrian is at risk of a collision. The method includes determining modified path data describing a modified walking path for the pedestrian. The method includes transmitting the modified path data to the pedestrian device so that the pedestrian is informed about the modified walking path and the risk is modified.

Abstract: Described are systems and methods for substituting missing graph information caused during the transmission of graph information from one entity to another. In one example, a system includes a processor and a memory with machine-readable instructions that cause the processor to receive an incoming graph stream comprising a plurality of graphs having detected object information and at least one missing graph, transform the plurality of graphs into incoming embedded vectors having values that represent detected object information of the incoming graph stream, provide the embedded vector that corresponds to the at least one missing graph with a missing graph value, and substitute the missing graph value with a substitute value using a recurrent neural network that interpolates the substitute value using the incoming embedded vectors that are not equal to the missing graph value.

Abstract: System, methods, and other embodiments described herein relate to improving monitoring of traffic flows. In one embodiment, a method includes aggregating perception data associated with a road network from information sources to a server over a network. The method also includes generating a graph structure from the perception data in association with a neural network model. The graph structure is an incomplete representation of the road network in view of missing data. The method also includes completing the graph structure using the neural network model that forms a graph model of the traffic flows to de-noise the graph structure according to road constraints between two points in the road network. The method also includes communicating the graph model of the traffic flows to a vehicle to navigate traffic in the road network.

Abstract: A method for data sharing between a transmitter vehicle and a receiver vehicle includes obtaining sensor data, encoding the sensor data into structured data having a set of features and a set of channels, generating a channel attention map based on inter-channel relationships of the set of features, generating weights corresponding to channels of the channel attention map, selecting one or more channels among the set of channels based on the weights corresponding to the set of channels, and transmitting the selected channels to the receiver vehicle.