Other

Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Jidosha Kabushiki Kaisha

Hongsheng Lu

John P Kenney

Rui Guo

Gaurav Bansal

Takayuki Shimizu

Prashant Tiwari

Bin Cheng

Toru Nakanishi

Onur Altintas

Ahmed Hamdi Sakr

Hansi Liu

Yoshiharu Doi

Roger D Melen

Sergei S Avedisov

Chenyuan He

Kyungtae Han

Chang-Heng Wang

Akila C Ganlath

Nejib Ammar

Qi Chen

Michael Clifford

Mahmoud Ashour

Peng Gao

Isfar Tariq

John Kenney

The disclosure includes embodiments for executing a beam alignment for a millimeter wave (mmWave) communication based on position data. In some embodiments, a method for an ego vehicle includes determining a beam sweeping direction setting for a mmWave beam alignment with an endpoint based on position data that describes a geographic location of the endpoint. The method includes modifying an operation of a Vehicle-to-Everything (V2X) radio of the ego vehicle to perform the mmWave beam alignment with the endpoint based on the beam sweeping direction setting so that an efficiency of the mmWave beam alignment is improved.

Position-based beam sweeping for directional vehicle-to-everything (V2X) networks

A controller for scheduling mmWave communication is provided. The controller is programmed to identify a plurality of states, each of the plurality of states indicating status of mmWave communication links among a plurality of nodes, calculate an updated value for each of the plurality of states iteratively based on a value iteration algorithm and a previous value for each of the plurality of states until the updated value for each of the plurality of states converges, and select one of the plurality of states based on the converged values for the plurality of states.

Methods and systems for scheduling mmWave communications using reinforcement learning

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.

Dynamic scene reconstruction using neural radiance field technology

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 frequency-domain, and selecting and reserving 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.

Resource selection for 5G NR V2X communications

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.

Methods and systems for enhanced scene perception using vehicle platoon

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.

Modifying a vehicular radio based on a schedule of point-to-point vehicular communications

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.

Methods, systems, and non-transitory mediums for augmented fine time measurement

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.

Systems and methods for matching objects in collaborative perception messages using multiple adaptive thresholds

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.

System and method for performing graph imputation

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.

Vehicle communication sender identification via hyper-graph matching

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.

Deep cooperative feature sharing among connected vehicles

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.

Roadside service estimates based on wireless vehicle data

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.

Systems and methods for matching objects in collaborative perception messages

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.

Location data correction service for connected vehicles

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.

Growing community of inventors

Mountain View, CA, United States of America

Hongsheng Lu