Intelligent transportation systems including digital twin interface for a passenger vehicle

1. A system for representing a set of operating states of a vehicle to a set of users of the vehicle, the system comprising:

a portion of the vehicle having a vehicle operating state;

a digital twin system receiving vehicle parameter data from one or more inputs to determine the vehicle operating state;

an identity management system to manage a set of identities and roles of users in the set of users of the vehicle, wherein each user in the set of users of the vehicle has at least one role, and wherein the at least one role of at least one user in the set of users includes at least one of a driver of the vehicle or a rider of the vehicle and wherein at least one other role of at least one other user in the set of users includes at least one of a vehicle maintenance entity or a vehicle design entity;

an interface for the digital twin system to present the vehicle operating state to the at least one user that has the at least one role of driver or rider; and

an other interface for the digital twin system to present the vehicle operating state to the at least one other user in the set of users that has the at least one other role of vehicle maintenance entity or vehicle design entity.

2. The system of claim 1 wherein the vehicle operating state is a vehicle maintenance state.

3. The system of claim 1 wherein the vehicle operating state is a vehicle energy utilization state.

4. The system of claim 1 wherein the vehicle operating state is a vehicle navigation state.

5. The system of claim 1 wherein the vehicle operating state is a vehicle component state.

6. The system of claim 1 wherein the vehicle operating state is a vehicle driver state.

7. The system of claim 1 wherein inputs for the digital twin system include at least one of an on-board diagnostic system, a telemetry system, a vehicle-located sensor, or a system external to the vehicle.

8. The system of claim 1 wherein the identity management system includes capabilities to view, modify and configure the digital twin system is based on an identity from the set of identities of the users in the set of users of the vehicle.

9. The system of claim 1 wherein the digital twin system is populated via an API from an edge intelligence system of the vehicle that provides connectivity to a system external to the vehicle.

10. The system of claim 1 wherein the digital twin system is populated via an API from an edge intelligence system of the vehicle that provides internal connectivity to a set of sensors and data sources of the vehicle.

11. The system of claim 1 wherein the digital twin system is populated via an API from an edge intelligence system of the vehicle that provides cellular connectivity to an onboard artificial intelligence system.

12. The system of claim 9 wherein the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activity by a set of digital twin users.

13. The system of claim 9 wherein the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activity by a driver user.

14. The system of claim 9 wherein the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activity by a rider user.

15. The system of claim 1 further comprising a first neural network to detect a detected satisfaction state of a rider user occupying the vehicle through analysis of data gathered from sensors deployed in the vehicle for gathering physiological conditions of the rider user; and a second neural network to optimize, for achieving a favorable satisfaction state of the rider user, an operational parameter of the vehicle in response to the detected satisfaction state of the rider user.

16. The system of claim 15 wherein the detected satisfaction state of the rider user is a detected emotional state of the rider user, and wherein the favorable satisfaction state of the rider user is a favorable emotional state of the rider user.

17. The system of claim 15 wherein the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network.

18. The system of claim 15 wherein at least one of the neural networks is a hybrid neural network and includes a convolutional neural network.

19. The system of claim 15 wherein the second neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a rider satisfaction state of the rider user.

20. The system of claim 15 wherein the second neural network optimizes the operational parameter in real time responsive to the detecting of the detected satisfaction state of the rider user by the first neural network.

21. The system of claim 15 wherein the first neural network comprises a plurality of connected nodes that form a directed cycle, the first neural network further facilitating bi-directional flow of data among the connected nodes.

22. The system of claim 15 wherein the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, or a proximity to other vehicles along the route.

23. The system of claim 1 wherein the interface for the digital twin system provides a 3D view showing a three-dimensional rendering of a model of the vehicle to the user, and wherein the other interface for the digital twin system provides the 3D view showing the three-dimensional rendering of the model of the vehicle to the other user.