System and method for cloud coordinated vehicle data collection

A method for cloud coordinated vehicle data collection, the method comprising:

• utilizing an onboard vehicle data management subsystem and a remotely-located back-office subsystem, wherein utilizing the onboard vehicle data management subsystem includes:
• converting, with a serial data communication adapter (SDCA), vehicle serial data into predetermined data formats via data classification, data up/down sampling, filtering, threshold testing, and data fidelity processing, wherein each of the onboard vehicle data management subsystem and the remotely located back-office subsystem comprise:
• one or more control modules, each of the one or more control modules having a processor, a memory, and input/output (I/O) ports, the control module executing program code portions stored in the memory, the program code portions:
• collecting vehicle data from onboard vehicle data sources;
• determining which of several distinct communications systems will be used to transmit the vehicle data to the remotely-located back-office subsystem;
• causing the remotely-located back-office subsystem to allocate data processing tasks to specific computing resources;
• and
• causing the onboard vehicle data management subsystem and the remotely-located back-office subsystem to continuously adjust data processing task allocation between onboard vehicle control modules and remotely located back-office control modules by:
• reducing computational and communications costs from a first level to a second level having a lower value than the first level, and honoring task deadlines and resource consumption constraints.

A system for cloud coordinated vehicle data collection, the system comprising:

• an onboard vehicle data management subsystem;
• and
• a remotely-located back-office subsystem, wherein each of the onboard vehicle data management subsystem and the remotely located back-office subsystem comprise:
• one or more control modules, each of the one or more control modules having a processor, a memory, and input/output (I/O) ports, the control module executing program code portions stored in the memory, the one or more control modules including:
• a vehicle data processing module (VDPM);
• a vehicle cache management module (VCMM);
• an intelligent data transmission pipe selection module;
• a serial data communication adapter (SDCA);
• a performance monitoring and debugging module;
• an intelligent task assignment module (ITAM);
• and
• a vehicle data management module,
• wherein the VDPM executes control logic that is uploaded from the remotely located back-office subsystem to the vehicle to support application tasks including:
• processing data and providing data fusion among different types of vehicle data,
• wherein the VCMM executes control logic that stores, caches, and manages a delay-tolerant vehicle serial data;
• and
• control logic that offloads the delay-tolerant vehicle serial data when an appropriate onboard vehicle data management subsystem-to-back-office subsystem communications channel is available,
• wherein the intelligent data transmission pipe selection module executes control logic that selectively chooses a data communication channel to transmit vehicle data from the VCMM module to the remotely located back-office subsystem;
• wherein the SDCA executes control logic that converts vehicle serial data into data formats required by the VDPM via data classification, data up/down sampling, filtering, threshold testing, and data fidelity processing;
• wherein the ITAM executes control logic that assigns tasks to control modules in the remotely-located back-office subsystem and in the onboard vehicle data management subsystem based on a global optimization goal, including:
• applying an integer linear programming (ILP) algorithm with a Lagrange optimization solution that obtains a globally optimal solution;
• and
• applying a vehicle-centric heuristic algorithm that obtains a locally optimal solution requiring fewer computational resources than a solution obtained by the ILP algorithm, wherein the ILP and vehicle centric heuristic algorithms split and allocate computational tasks to vehicles or geographic and temporal regions or cloud backend resources;
• and
• wherein the vehicle data management module executes control logic that collects data from a fleet of vehicles;
• and
• processes the data for internal and third-party applications;
• wherein the performance monitoring and debugging module executes control logic that causes the onboard vehicle data management subsystem and the remotely-located back-office subsystem to continuously monitor computational and communications resource consumption once tasks are allocated to onboard-vehicle control modules;
• and
• upon observed underutilization or overutilization of computational and communications resources, re-allocates tasks between vehicles;
• or sends control parameters to alter computational and communications resource requirements for tasks, and thereby minimizing:
• reducing computational and communications costs from a first level to a second level having a lower value than the first level, and honoring task deadlines and resource consumption constraints.