Expert system for vehicle configuration recommendations of vehicle or user experience parameters
A system for transportation includes a vehicle configured to have a rider located therein or thereon, and an expert system to produce a recommendation for a configuration of the vehicle, wherein the recommendation includes at least one recommended parameter of configuration for the expert system that controls a parameter selected from the group consisting of a vehicle parameter, a rider experience parameter, and combinations thereof.
1. A system for transportation, comprising:
a vehicle configured to have a rider located therein or thereon; and
an expert system to produce a recommendation for configuring a rider experience parameter to promote a rider entertainment degree of satisfaction,
wherein the recommendation includes a selection of an entertainment option to be presented to the rider that is based on a parameter of the rider's desired arrival time at a destination, and
wherein the expert system uses sensor input data from a plurality of in-vehicle sensors to detect a rider state of boredom and further produces the recommendation for configuring the rider experience parameter based on the detected rider state of boredom.
2. The system for transportation of claim 1 wherein the vehicle comprises an automation system for automating at least one control parameter of the vehicle.
3. The system for transportation of claim 2 wherein the vehicle is at least a semi-autonomous vehicle.
4. The system for transportation of claim 3 wherein the vehicle is automatically routed.
5. The system for transportation of claim 4 wherein the vehicle is a self-driving vehicle.
6. The system for transportation of claim 1 wherein the expert system is at least one of a neural network system, a deep learning system, a machine learning system, a model-based system, a rule-based system, or a random walk-based system.
7. The system for transportation of claim 1 wherein the expert system is at least one of a genetic algorithm system, a convolutional neural network system, a self-organizing system, a pattern recognition system, a hybrid artificial intelligence-based system, or an acrylic graph-based system.
8. The system for transportation of claim 1 wherein the expert system produces an other recommendation based on entertainment degrees of satisfaction of a plurality of riders in the system for transportation.
9. The system for transportation of claim 1 wherein the expert system produces an other recommendation based on a rider safety degree of satisfaction.
10. The system for transportation of claim 1 wherein the recommendation for configuring the rider experience parameter is further based on a parameter of traffic congestion.
11. The system for transportation of claim 1 wherein the recommendation for configuring the rider experience parameter is further based on a parameter of preferred routes.
12. The system for transportation of claim 1 wherein the recommendation for configuring the rider experience parameter is further based on a parameter of collective satisfaction.
13. The system for transportation of claim 1 wherein the expert system is trained to recognize a pattern in the sensor input data, and wherein the expert system is trained to detect the rider state of boredom based, at least in part, on the recognition of the pattern in the sensor input data.
14. The system for transportation of claim 1 wherein the expert system is trained using a stream of data from at least one of a plurality of social media sources, a rider voice system, or a combination thereof.
15. The system for transportation of claim 1 wherein the rider experience parameter includes a presentation, via an e-commerce system interface, of at least one of an interface display, content, search results, advertising, or a workflow associated with the advertising.
16. The system for transportation of claim 1 wherein the sensor input data from the plurality of in-vehicle sensors includes an image of a face of the rider; wherein the expert system is trained to recognize a pattern in feature vectors of the image of the face of the rider; and wherein the expert system is trained to detect the rider state of boredom based, at least in part, on the recognition of the pattern in the feature vectors.
17. A method, comprising:
detecting, via an expert system, a rider state of a rider in a vehicle using sensor input data from a plurality of in-vehicle sensors, wherein the rider state includes a rider state of boredom; and
producing, via the expert system, a recommendation for configuring a rider experience parameter to promote a rider entertainment degree of satisfaction and produce an optimized rider state, wherein the recommendation includes a selection of an entertainment option to be presented to the rider that is based on a parameter of the rider's desired arrival time at a destination.
18. The method of claim 17 , further comprising:
capturing, via the plurality of in-vehicle sensors, an image of a face of the rider;
communicating the sensor input data to the expert system, wherein the sensor input data includes the image of the face of the rider;
training the expert system to recognize a pattern in feature vectors of the image of the face of the rider; and
training the expert system to detect the rider state of boredom based, at least in part, on the recognition of the pattern in the feature vectors.
19. The method of claim 17 wherein the rider experience parameter includes a presentation, via an e-commerce system interface, of at least one of an interface display, content, search results, advertising, or a workflow associated with the advertising.