Renovo Motors, Inc.

Woven Planet North America, Inc.

Ioannis Petousis

Christopher Heiser

Anand Ramesh

Owen Davis

Khalid Azam

Jason Stinson

A system and method that enables resource analysis within an intelligent content absorption network with autonomous mobility implementations includes generating a prioritization model, training the prioritization model on a data corpus to generate a prioritization schedule, deploying the prioritization schedule to one or more autonomous vehicles, offloading data at a local content absorption node, processing data at the local content absorption node, and optionally transmitting data results from the processed data.

Systems and methods for providing resource analysis for autonomous mobility on demand

A system and method that includes collecting vehicle sensor data, wherein prioritizing vehicle sensor data includes identifying a level of importance for each of a plurality of vehicle sensor data types included in the vehicle sensor data; generating a vehicle sensor data schedule, wherein generating the vehicle data schedule includes one or more of (i) identifying a transmission order for each of the plurality of vehicle sensor data types and (ii) identifying a storage scheme selected from a hierarchy of data storage types for each of the plurality of vehicle sensor data types; transforming vehicle sensor data into message data, wherein the transforming includes selectively converting one or more of the vehicle sensor data types of the vehicle sensor data to a messaging format based on the prioritization; and transmitting, via one or more selected communication networks, the message data according to the vehicle sensor data schedule.

Systems and methods for processing vehicle sensor data

Systems and methods include accessing streams of sensor data; constructing a corpus of seed sample data; initializing a first instance of a trained model using the corpus of seed sample data that: generates predictions of predicted sensor values; computing error values based on calculated differences between the actual sensor values and the predicted sensor values; transmitting the computed error values; initializing a second instance of the trained model based on an input of the corpus of the seed sample data, wherein the second instance of the trained model is identical to the first instance of the trained model, and wherein the second instance: generates inferences of predicted sensor values for each of the sensors based on the input of the corpus of seed sample data; reconstructing estimates of the actual sensor values based on a reconstruction computation with the parallel predicted sensor values and the error values.

Systems and methods for processing vehicle data

A system and method for dynamically managing application loads on a vehicle includes: receiving a plurality of distinct application requests; for each one application request, identifying expected computing resource expenditure data based on one or more attributes of each one application request; and identifying current state data for each of a plurality of computing resources of the vehicle based on an expected utilization of each of the plurality of computing resources for fulfilling each one application request; dynamically generating an execution schedule for executing a subset of or each of the plurality of distinct application requests based on (1) the expected computing resource expenditure data for each one application request and (2) the current state data for each of the plurality of computing resources of the vehicle; and executing the subset of or each of the plurality of distinct application requests based on the execution schedule.

Systems and methods for dynamic application management with an autonomous vehicle

A system and method that enables autonomous mobility implementations to prioritize, offload, and process data in an efficient manner includes generating a prioritization model, training the prioritization model on a data corpus to generate a prioritization schedule, deploying the prioritization schedule to one or more autonomous vehicles, offloading data at a local content absorption node, processing data at the local content absorption node, and optionally transmitting data results from the processed data.

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Campbell, CA, United States of America

Ioannis Petousis