Abstract: Systems, methods, and other embodiments described herein relate to grouping vehicles by adapting communication parameters for damping disturbances through messaging. In one embodiment, a method includes acquiring sensor data and information from surrounding vehicles. The method also includes identifying traffic conditions for grouping with the vehicles on a road according to the sensor data and the information. The method also includes adapting communication parameters of messages for damping an environmental disturbance that includes factoring the traffic conditions and communicating the messages towards the vehicles until grouping parameters are satisfied, and the damping varies by vehicle positions among the vehicles.

Abstract: A method of fusing multi-modal sensor data is provided. The method includes obtaining features for 3D data captured by a first sensor of an ego vehicle, obtaining features for images captured by second sensors of the ego vehicle, flattening the features for 3D data to first features in bird eye view, transforming the features for images into second features in bird eye view, concatenating the first features and the second features to obtain first concatenated multi-sensor features, and fusing the first concatenated multi-sensor features with second concatenated multi-sensor features received from another vehicle to obtain fused multi-sensor features.

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, 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: Systems, methods, and other embodiments described herein relate to reconstructing a dynamic scene using Neural Radiance Field (NeRF) technology. In one embodiment, a method includes receiving, from one or more sensors, a plurality of video clips of an environment. The method includes generating a second plurality of video clips based on the plurality of video clips, and reconstructing, using NeRF technology, a scene as a continuous function based on the second plurality of video clips.

Abstract: Systems, methods, and other embodiments described herein relate to reconstructing a dynamic scene using Neural Radiance Field (NeRF) technology. In one embodiment, a method includes receiving, from one or more sensors, a plurality of video clips of an environment. The method includes generating a second plurality of video clips based on the plurality of video clips, and reconstructing, using NeRF technology, a scene as a continuous function based on the second plurality of video clips.

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: A vehicle includes one or more sensors configured to obtain raw data related to a scene, one or more processors, and machine readable instructions stored in one or more memory modules. The one machine readable instructions, when executed by the one or more processors, cause the vehicle to: process the raw data with a first neural network stored in the one or more memory modules to obtain a first prediction about the scene, transmit the raw data to a computing device external to the vehicle, receive a second prediction about the scene from the computing device in response to transmitting the raw data to the computing device, and determine an updated prediction about the scene based on a combination of the first prediction and the second prediction.

Abstract: The disclosure includes embodiments for modifying a vehicle-to-everything (V2X) radio of an ego vehicle that is a connected vehicle. In some embodiments, a method includes analyzing, by a machine learning module executed by a processor, a local dynamic map generated by the ego vehicle to determine schedule data describing a schedule for the ego vehicle to transmit a millimeter wave (mmWave) message to a remote vehicle. The method includes transmitting a V2X message including the schedule data for receipt by the remote vehicle so that the remote vehicle has access to the schedule. The method includes modifying an operation of the V2X radio of the ego vehicle based on the schedule so that the V2X radio transmits the mmWave message to the remote vehicle in compliance with the schedule. The method includes transmitting the mmWave message to the remote vehicle in compliance with the schedule.

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: An example operation includes one or more of receiving a request from a software application installed on a vehicle, detecting an identifier of the software application from identification information included in the received request, dynamically determining parameters to identify connected vehicles based on the identifier of the software application, and detecting a connected vehicle from among one or more surrounding vehicles via an exchange of messages between the vehicle and the one or more surrounding vehicles based on the dynamically determined parameters.

Abstract: An example operation includes one or more of identifying, via an ego vehicle, one or more surrounding vehicles of the ego vehicle based on sensor data from the ego vehicle, determining a state of an ego vehicle and a state of the one or more surrounding vehicles of the ego vehicle, dynamically determining parameters for identifying connected vehicles based on the determined states of the ego vehicle and the one or more surrounding vehicles, and detecting a connected vehicle from among the one or more surrounding vehicles via an exchange of messages between the ego vehicle and the one or more surrounding vehicles based on the dynamically determined parameters.

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: A method for relative localization is provided. The method includes obtaining key points from a point cloud obtained by a sender vehicle, obtaining road boundary coordinates from course map information, comparing the road boundary coordinates against the key points to generate matched road boundary points and to calibrate the key points, generating augmented key points by combining the matched road boundary points and calibrated key points of a receiver vehicle, registering the calibrated key points of the sender vehicle against the augmented key points to generate a transformation matrix, and transforming coordinates of the sender vehicle into a coordinate system of the receiver vehicle using the transformation matrix.

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: 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, 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.