Optimal driving characteristic adjustment for autonomous vehicles

1. A method, by a processor, for implementing intelligent driving characteristic adjustment for autonomous vehicles, comprising:

determining a user experience satisfaction level for one or more users during a journey within an autonomous vehicle according to historical user experience satisfaction levels, one or more user profiles, one or more contextual factors, or a combination thereof; and

dynamically adjusting one or more performance characteristics of the autonomous vehicle for the one or more users according to the historical user experience satisfaction levels, the one or more user profiles, one or more contextual factors, or a combination thereof, if the user experience satisfaction level is less than a predetermined threshold, wherein during the dynamic adjustment, the one or more performance characteristics are continuously adjusted to bring the user experience satisfaction level above the predetermined threshold by maintaining the one or more performance characteristics substantially below a stress threshold comprised of a first percentage above a value indicative of previous amalgamated user experiences represented by the one or more user profiles, notwithstanding the one or more performance characteristics are allowed to exceed the stress threshold to a recovery threshold, comprised of a second percentage, as the dynamic adjustment is performed, and wherein the stress threshold and the recovery threshold are implemented as dual thresholds for a particular characteristic of the one or more performance characteristics allowed to exceed the stress threshold to introduce the one or more users into becoming comfortable with the particular characteristic as the one or more users travel in the autonomous vehicle.

2. The method of claim 1 , further including:

collecting user data relating to the historical user experience satisfaction levels, the one or more user profiles, one or more contextual factors, or a combination thereof;

clustering the historical user experience satisfaction levels, the one or more user profiles, one or more contextual factors, or a combination thereof into a cluster of users for the one or more users; and

determining a risk threshold for the cluster of users and use reinforcement learning to dynamically adjust the one or more performance characteristics based on a global satisfaction level of the cluster of users.

3. The method of claim 1 , further including changing a type of autonomous vehicle or mode of transport of the autonomous vehicle.

4. The method of claim 1 , further including determining the user experience satisfaction level from one or more sensors, internet of things (IoT) computing devices, one or more user equipment (“UE”), a global positioning satellite (“GPS”) device, or a combination thereof.

5. The method of claim 1 , further including monitoring one or more activities, behavior, biometric data, or a combination thereof of the user via a reinforced feedback learning operation.

6. The method of claim 1 , further including collecting feedback from the user during the journey.

7. The method of claim 1 , further including implementing a machine learning mechanism to learn the one or more contextual factors, the user profiles, reinforced feedback learning, the user experience satisfaction level, or a combination thereof.

8. A system for implementing intelligent driving characteristic adjustment for autonomous vehicles, comprising:

one or more computers with executable instructions that when executed cause the system to:

determine a user experience satisfaction level during a journey within an autonomous vehicle according to historical user experience satisfaction levels, a user profile, one or more contextual factors, or a combination thereof; and

dynamically adjust one or more performance characteristics of the autonomous vehicle for the one or more users according to the historical user experience satisfaction levels, the one or more user profiles, one or more contextual factors, or a combination thereof, if the user experience satisfaction level is less than a predetermined threshold, wherein during the dynamic adjustment, the one or more performance characteristics are continuously adjusted to bring the user experience satisfaction level above the predetermined threshold by maintaining the one or more performance characteristics substantially below a stress threshold comprised of a first percentage above a value indicative of previous amalgamated user experiences represented by the one or more user profiles, notwithstanding the one or more performance characteristics are allowed to exceed the stress threshold to a recovery threshold, comprised of a second percentage, as the dynamic adjustment is performed, and wherein the stress threshold and the recovery threshold are implemented as dual thresholds for a particular characteristic of the one or more performance characteristics allowed to exceed the stress threshold to introduce the one or more users into becoming comfortable with the particular characteristic as the one or more users travel in the autonomous vehicle.

9. The system of claim 8 , wherein the executable instructions further:

collect user data relating to the historical user experience satisfaction levels, the user profile, and the one or more contextual factors;

cluster the historical user experience satisfaction levels, the one or more user profiles, one or more contextual factors, or a combination thereof into a cluster of users for the one or more users; and

determine a risk threshold for the cluster of users and use reinforcement learning to dynamically adjust the one or more performance characteristics based on a global satisfaction level of the cluster of users.

10. The system of claim 8 , wherein the executable instructions further change a type of autonomous vehicle or mode of transport of the autonomous vehicle.

11. The system of claim 8 , wherein the executable instructions further determine the user experience satisfaction level from one or more sensors, internet of things (IoT) computing devices, one or more user equipment (“UE”), a global positioning satellite (“GPS”) device, or a combination thereof.

12. The system of claim 8 , wherein the executable instructions further monitor one or more activities, behavior, biometric data, or a combination thereof of the user via a reinforced feedback learning operation.

13. The system of claim 8 , wherein the executable instructions further collect feedback from the user during the journey.

14. The system of claim 8 , wherein the executable instructions further implement a machine learning mechanism to learn the one or more contextual factors, the user profiles, reinforced feedback learning, the user experience satisfaction level, or a combination thereof.

15. A computer program product for implementing intelligent driving characteristic adjustment for autonomous vehicles by a processor, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

an executable portion that determines a user experience satisfaction level during a journey within an autonomous vehicle according to historical user experience satisfaction levels, a user profile, one or more contextual factors, or a combination thereof; and

an executable portion that dynamically adjusts one or more performance characteristics of the autonomous vehicle for the one or more users according to the historical user experience satisfaction levels, the one or more user profiles, one or more contextual factors, or a combination thereof, if the user experience satisfaction level is less than a predetermined threshold, wherein during the dynamic adjustment, the one or more performance characteristics are continuously adjusted to bring the user experience satisfaction level above the predetermined threshold by maintaining the one or more performance characteristics substantially below a stress threshold comprised of a first percentage above a value indicative of previous amalgamated user experiences represented by the one or more user profiles, notwithstanding the one or more performance characteristics are allowed to exceed the stress threshold to a recovery threshold, comprised of a second percentage, as the dynamic adjustment is performed, and wherein the stress threshold and the recovery threshold are implemented as dual thresholds for a particular characteristic of the one or more performance characteristics allowed to exceed the stress threshold to introduce the one or more users into becoming comfortable with the particular characteristic as the one or more users travel in the autonomous vehicle.

16. The computer program product of claim 15 , further including an executable portion that:

collects user data relating to the historical user experience satisfaction levels, the user profile, and the one or more contextual factors;

clusters the historical user experience satisfaction levels, the one or more user profiles, one or more contextual factors, or a combination thereof into a cluster of users for the one or more users; and

determines a risk threshold for the cluster of users and use reinforcement learning to dynamically adjust the one or more performance characteristics based on a global satisfaction level of the cluster of users.

17. The computer program product of claim 15 , further including an executable portion that changes a type of autonomous vehicle or mode of transport of the autonomous vehicle.

18. The computer program product of claim 15 , further including an executable portion that determines the user experience satisfaction level from one or more sensors, internet of things (IoT) computing devices, one or more user equipment (“UE”), a global positioning satellite (“GPS”) device, or a combination thereof.

19. The computer program product of claim 15 , further including an executable portion that:

monitors one or more activities, behavior, biometric data, or a combination thereof of the user via a reinforced feedback learning operation; and

collect feedback from the user during the journey.

20. The computer program product of claim 15 , further including an executable portion that implements a machine learning mechanism to learn the one or more contextual factors, the user profiles, reinforced feedback learning, the user experience satisfaction level, or a combination thereof.