Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Jidosha Kabushiki Kaisha

Other

Dawei Chen

BaekGyu Kim

Kyungtae Han

Roger D Melen

Rohit Gupta

Haoxin Wang

Yin-Chen Liu

Qi Chen

Yitao Chen

Daniel Doe

Systems and methods for selecting an edge node include a processor operable to send a request of bidding to edge nodes regarding performing a task for an inquiry vehicle, receive one or more bids from one or more bidding edge nodes of the edge nodes, wherein each bid includes a resource allocation offer and a price, calculate a preference value based on a weighted sum of preference factors for each bidding edge node, wherein the preference factors are determined based on the resource allocation offer, and the weight assigned to each preference factor is determined based on the task, select a performing edge node based on the preference values and the prices, and operate the inquiry vehicle based on a result of the task performed by the performing edge node.

Systems and methods for selecting edge nodes for task performance

System and method for reducing latency and bandwidth usage in reality devices include a reality device and one or more processors. The reality device includes a camera to operably capture a set of consequent frames of views external to a vehicle. The one or more processors are operable to monitor movements of one or more eyes of a user to obtain eye-tracking data using the reality device, identify an area of interest (AoI) in the frame based on the eye-tracking data, AoI history, or driving statistics of the user, crop the frame to obtain a reduced-size frame including the AoI in the frame, and transmit the reduced-size frame to an edge server for performing a task on behalf of the vehicle.

Systems and methods for resource-optimized mixed reality using user-centric adaptive object detection

A system for communication-aware federated learning includes a server and edge nodes. Each of the edge nodes trains a machine learning model using first local data obtained by sensors of corresponding edge node. Each of the edge nodes obtains network bandwidth for a channel between corresponding edge node and the server. One or more of the edge nodes determines a level of compression based on the bandwidth for the channel, compresses the trained machine leaning model based on the determined level of compression, and transmits the compressed trained machine learning model to the server. The server decompresses the compressed trained machine learning models and aggregates the decompressed trained machine learning models to obtain the aggregated machine learning model, and transmits the aggregated machine learning model to each of the edge nodes. Each of the edge nodes receives the aggregated machine learning model from the server.

Systems and methods for communication-aware federated learning

The disclosure includes embodiments that provide a switching decision for vehicle computational offloading to a roadside edge server. A method includes causing a sensor set of a connected vehicle to determine a current driving context of the connected vehicle. The method includes comparing the current driving context to a set of historical data to determine a predicted latency for using offboard computing resources of an edge server. The method includes determining that the predicted latency for using the offboard computing resources satisfies a threshold for the predicted latency. The method includes executing a switching decision that includes deciding to use the offboard computing resources of the edge server. The method includes causing the edge server to wirelessly provide digital data generated by the edge server responsive to a calculation. The method includes modifying an operation of the onboard vehicle computer based on the digital data.

Switching decision for vehicle computational offloading to roadside edge server

A method includes receiving a request from a vehicle to perform a computing task, selecting a machine learning model from among a plurality of machine learning models based at least in part on the request, and predicting an amount of computing resources needed to perform the computing task using the selected machine learning model.

Methods and systems for selecting machine learning models to predict distributed computing resources

The disclosure includes embodiments including a vehicular edge server switching mechanism based on historical data and digital twin simulations. A method includes causing a sensor set of a connected vehicle to determine a current driving context of the connected vehicle. A method includes comparing the current driving context to a set of digital twin data and a set of historical data to determine a predicted latency for using offboard computing resources of an edge server. The method includes determining that the predicted latency for using the offboard computing resources satisfies a threshold for the predicted latency. The method includes executing a switching decision that includes deciding to use the offboard computing resources of the edge server based on the comparing of the current driving context to the set of digital twin data and the set of historical data and the determining that the threshold for the predicted latency is satisfied.

Vehicular edge server switching mechanism based on historical data and digital twin simulations

The disclosure includes embodiments that provide a digital twin-based edge server switching decision. A method includes causing a sensor set of a connected vehicle to determine a current driving context of the connected vehicle. The method includes comparing the current driving context to a set of digital twin data to determine a predicted latency for using offboard computing resources of an edge server. The method includes determining that the predicted latency for using the offboard computing resources satisfies a threshold for the predicted latency. The method includes executing a switching decision that includes deciding to use the offboard computing resources of the edge server based on the comparing of the current driving context to the set of digital twin data and the determining that the threshold for the predicted latency is satisfied.

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Mountain View, CA, United States of America

Dawei Chen