Automated usage driven engineering

1. A computer-implemented method for automating vehicle feature updates, the method being executed by one or more processors and comprising:

receiving telematics data identifying an actual usage of a first vehicle;

automatically performing a gap analysis, using a gap analysis component, to determine a difference between the actual usage of the first vehicle and an expected usage of the first vehicle based at least in part on the telematics data;

aggregating, at a data aggregator component, collected vehicle data for other vehicles;

determining, using a configuration adjustment component, a plurality of feature updates that are implementable as software updates based at least in part on (i) the gap analysis difference between the actual usage of the first vehicle and the expected usage of the first vehicle, and (ii) the collected vehicle data for other vehicles;

assigning a weight value to each of the plurality of feature updates based at least in part on the gap analysis;

determining a selected feature update from the plurality of feature updates to provide to an onboard computer system of an active vehicle based at least in part on a weight value assigned to the selected feature update meeting a threshold; and

updating a selected feature on the onboard computer system of the active vehicle using the selected feature update when the selected feature update can be implemented by a software update.

2. The computer-implemented method of claim 1 , wherein the selected feature update is a modification to a software component of the vehicle.

3. The computer-implemented method of claim 2 , wherein the software component is a display feature shown on an adaptable display in the vehicle.

4. The computer-implemented method of claim 1 further comprising:

assigning a weight value to each of the plurality of feature updates based at least in part on a machine learning algorithm, and wherein determining the selected feature is determined at least in part based on the assigned weight value of a feature update meeting a threshold.

5. The computer-implemented method of claim 1 , wherein the telematics data is collected by a plurality of onboard sensors associated with the vehicle.

6. The computer-implemented method of claim 5 , wherein the telematics data includes internal and external information collected by the onboard sensors.

7. The computer-implemented method of claim 1 , wherein the selected feature update is a corrective action.

8. One or more non-transitory computer-readable storage media coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for automating vehicle feature updates, the operations comprising:

receiving telematics data identifying an actual usage of a first vehicle;

automatically performing a gap analysis, using a gap analysis component to determine a difference between the actual usage of the first vehicle and an expected usage of the first vehicle based at least in part on the telematics data;

aggregating, at a data aggregate component, collected vehicle data for other vehicles;

determining, using a configuration adjustment component, a plurality of feature updates that are implementable as software updates based at least in part on (i) the gap analysis difference between the actual usage of the first vehicle and the expected usage of the first vehicle, and (ii) the collected vehicle data for other vehicles;

assigning a weight value to each of the plurality of feature updates based at least in part on the gap analysis;

determining a selected feature update from the plurality of feature updates to provide to an onboard computer system of an active vehicle based at least in part on a weight value assigned to the selected feature update meeting a threshold; and

updating a selected feature on the onboard computer system of the active vehicle using the selected feature update when the selected feature update can be implemented by a software update.

9. The non-transitory computer-readable storage media of claim 8 , wherein the selected feature update is a modification to a software component of the vehicle.

10. The non-transitory computer-readable storage media of claim 9 , wherein the software component is a display feature shown on an adaptable display in the vehicle.

11. The non-transitory computer-readable storage media of claim 8 , wherein the operations further comprise:

assigning a weight value to the plurality of feature updates based at least in part on a machine learning algorithm, and wherein determining the selected feature at least in part based on the assigned weight value meeting a threshold.

12. The non-transitory computer-readable storage media of claim 8 , wherein the telematics data is collected by a plurality of onboard sensors associated with the vehicle.

13. The non-transitory computer-readable storage media of claim 12 , wherein the telematics data includes internal and external information collected by the onboard sensors.

14. A system, comprising:

one or more processors; and

a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for automating vehicle feature updates, the operations comprising:

receiving telematics data identifying an actual usage of a first vehicle;

automatically performing a gap analysis, using a gap analysis component, to determine a difference between the actual usage of the first vehicle and an expected usage of the first vehicle based at least in part on the telematics data;

aggregating, at a data aggregator component, collected vehicle data for other vehicles;

determining, using a configuration adjustment component, a plurality of feature updates that are implementable as software updates based at least in part on (i) the gap analysis difference between the actual usage of the first vehicle and the expected usage of the first vehicle, and (ii) the collected vehicle data for other vehicles;

assigning a weight value to each of the plurality of feature updates based at least in part on the gap analysis;

determining a selected feature update from the plurality of feature updates to provide to an onboard computer system of an active vehicle based at least in part on a weight value assigned to the selected feature update meeting a threshold; and

updating a selected feature on the onboard computer system of the active vehicle using the selected feature update when the selected feature update can be implemented by a software update.

15. The system of claim 14 , wherein the selected feature update is a modification to a software component of the vehicle.

16. The system of claim 15 , wherein the software component is a display feature shown on an adaptable display in the vehicle.

17. The system of claim 14 , wherein a product engineering module validates the selected feature update.

18. The system of claim 17 , wherein validating the selected feature updates includes employing virtual reality, digital simulation, or micro-factories within a respective region.

19. The system of claim 18 , wherein the selected feature update is integrated into a next version of a model of the vehicle once validated by the product engineering module.

20. The system of claim 17 , wherein the selected feature update is a modification to a physical aspect of the vehicle.

21. The system of claim 14 , wherein the operations further comprise:

providing the feature update to a product engineering module when the selected feature update cannot be implemented by a software update.

22. The system of claim 14 , wherein updating the selected feature on the onboard computer system of the active vehicle using the selected feature update comprises sending the selected feature update to the onboard computer system of the active vehicle.