System and method for cloud coordinated vehicle data collection

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

an onboard vehicle data management subsystem, including:

an intelligent data transmission pipe selection module;

a vehicle data processing module (VDPM);

a vehicle cache management module (VCMM); and

a serial data communication adapter (SDCA), wherein the SDCA further comprises:

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; 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 program code portions comprising:

a first program code portion that collects vehicle data from onboard vehicle data sources;

a second program code portion that determines which of several distinct communications systems will be used to transmit the vehicle data to the remotely-located back-office subsystem;

a third program code portion that causes the remotely-located back-office subsystem to allocate data processing tasks to specific computing resources; and

a fourth program code portion that causes 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.

2 . The system of claim 1 wherein the VDPM further comprises:

control logic that executes code 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.

3 . The system of claim 1 wherein the vehicle cache management module further comprises:

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.

4 . The system of claim 1 wherein the intelligent data transmission pipe selection module further comprises:

control logic that selectively chooses a data communication channel to transmit vehicle data from the vehicle cache management module to the remotely located back-office subsystem.

5 . The system of claim 1 wherein the remotely-located back-office subsystem further comprises:

an intelligent task assignment module (ITAM); and

a vehicle data management module.

6 . The system of claim 5 wherein the ITAM further comprises:

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:

splitting and allocation of computational tasks to vehicles or geographic/temporal regions or cloud backend resources.

7 . The system of claim 5 wherein the vehicle data management module further comprises:

control logic that collects data from a fleet of vehicles; and

processes the data for internal and third-party applications.

8 . The system of claim 6 wherein the control logic for splitting and allocation of computational tasks further comprises:

an integer linear programming (ILP) algorithm with a Lagrange optimization solution that obtains a globally optimal solution; and

a vehicle-centric heuristic algorithm that obtains a locally optimal solution requiring fewer computational resources than a solution obtained by the ILP algorithm.

9 . The system of claim 1 wherein the fourth program code portion is executed by a performance monitoring and debugging module, wherein the fourth program code portion further comprises:

control logic that, once tasks are allocated to onboard-vehicle control modules, causes the remotely-located back-office subsystem to continuously monitor computational and communications resource consumption; and

upon observed underutilization or overutilization of computational communications resources, re-allocates tasks between vehicles; and

or sends control parameters to alter computational and communications resource requirements for tasks.

10 . 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.

11 . The method of claim 10 wherein utilizing the onboard vehicle data management subsystem further comprises:

executing, with a vehicle data processing module (VDPM), code that is uploaded from the remotely located back-office subsystem to a vehicle to support application tasks including: processing data and providing data fusion among different types of vehicle data.

12 . The method of claim 10 wherein utilizing the onboard vehicle data management subsystem further comprises:

storing, caching, and managing delay-tolerant vehicle serial data with a vehicle cache management module (VCMM); and

offloading the delay-tolerant vehicle serial data when an appropriate onboard vehicle data management subsystem-to-back-office subsystem communications channel is available.

13 . The method of claim 12 wherein utilizing the onboard vehicle data management subsystem further comprises:

selecting, with an intelligent data transmission pipe selection module, a data communication channel to transmit vehicle data from the vehicle cache management module to the remotely located back-office subsystem.

14 . The method of claim 10 wherein utilizing the remotely-located back-office subsystem further comprises:

assigning, with an intelligent task assignment module (ITAM), 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:

splitting and allocating computational tasks to vehicles, remotely-located back-office subsystem resources, and which are considered within given geographic and temporal regions or cloud backend resources; and

collecting data from a fleet of vehicles with a vehicle data management module, the vehicle data management module processing the data for internal and third-party applications.

15 . The method of claim 14 wherein splitting and allocating computational tasks further comprises:

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.

16 . The method of claim 10 further comprising:

utilizing a performance monitoring and debugging module that, once tasks are allocated to onboard-vehicle control modules, causes the remotely-located back-office subsystem to continuously monitor computational and communications resource consumption; and

upon observed underutilization or overutilization of computational and communications resources, re-allocating tasks between vehicles; or

sending control parameters to alter computational and communications resource requirements for tasks.

17 . 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.