Volkswagen Group of America Investments, LLC

Ford Global Technolgoies, LLC

Argo Ai Gmbh

Matthew Puchalski

Scott Anthony Plant

Kevin Scott Swanson

Michel Laverne

Casey J Sennott

Daniel Swartz

Dale Humbert Lord

John Martin Seminatore

Christopher Attard

Adela Hsien-Neng Wee

Anthony R McAlexander

Jason Paul Ziglar

Benjamin David Ballard

Devices, systems, and methods are provided for indoor localization. A self-driving vehicle may detect a first fiducial marker located at a first location within a building, wherein the first fiducial marker comprises first fiducial marker information associated with the first location. The self-driving vehicle may retrieve the first fiducial marker information from the first fiducial marker. The self-driving vehicle may generate localization information of the self-driving vehicle based on the first fiducial marker information. The self-driving vehicle may utilize the localization information to transition to a second location within the building, wherein the second location comprises a second fiducial marker.

Indoor localization of autonomous vehicles

A fleet management system will receive, from each vehicle of a fleet of vehicles, a vehicle identification number (VIN). The system also will receive data elements that comprise: (a) vehicle operational parameters gathered during a run of the vehicle; (b) a hardware identification code that identifies a hardware component of the vehicle; and/or (c) a software identification code that identifies an installed software component in the vehicle. The processor will generate a data block that comprises the VIN and the one or more data elements. The processor will then save the data block to a shared digital ledger that includes VINs and data elements for a plurality of the vehicles in the fleet.

Distributed method and system for vehicle identification tracking

Disclosed herein are systems, methods, and computer program products for controlling data collection by resources. The methods comprise: receiving real-world data collected by the resources in accordance with data collection mission (DCM) parameters; receiving user defined DCM goal(s); updating goal(s) for DCM mission(s) based on the real-world data and the user defined DCM goal(s); modifying the data DCM parameter(s) based on the updated goal(s) and which ones of the resources are still available for DCMs; and causing data collection operations (which are currently being performed by the resource(s)) to change in accordance with the modified DCM parameter(s).

Systems and methods for dynamic data mining during data collection missions

Provided are systems, methods, and computer program products for monitoring, testing, or debugging transportation services, generating or transmitting an initiating message from a global manager cloud to an external service cloud, to invoke a transportation as a service (TaaS) message from external service clouds that comprise confirmation, also including generating or transmitting a simulated message from the global manager cloud to mirror the TaaS message, or a portion, transmitted on a TaaS link from the external service cloud to the on-vehicle modem, determining, a confidence threshold for a capability or security of the TaaS link, validating AV service data sent from the global manager cloud to a TaaS component in an on-vehicle black box of the autonomous vehicle system, validating AV compute data sent from the autonomous vehicle system to the TaaS component in the on-vehicle black box, validating TaaS message data received from the external service cloud.

Systems, methods, and computer program products for testing of cloud and onboard autonomous vehicle systems

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Matthew Puchalski