Vehicle rider satisfaction promoting systems based on adjusting operating parameters of the vehicle
Data processing systems disclosed herein may promote satisfaction of a rider of a vehicle and include a machine learning model and a vehicle control system. The machine learning model determines a measure of an emotional state of the rider based on data received from a sensor associated with the rider, where the data is indicative of a physiological condition of the rider. The vehicle control system: determines a target value of an operating parameter of the vehicle based on a correlation between the emotional state of the rider and the target value of the operating parameter; and adjusts the operating parameter of the vehicle based on the target value of the operating parameter.
1. A data processing system that promotes satisfaction of a rider of a vehicle, the data processing system comprising:
a machine learning model that determines a measure of an emotional state of the rider based on data received from a sensor associated with the rider, wherein the data is indicative of a physiological condition of the rider, wherein the machine learning model is configured to predict an unfavorable change in the emotional state of the rider based on a pattern in the data; and
a vehicle control system that:
determines a target value of a user experience parameter of the vehicle based on a correlation between a mitigation of the predicted unfavorable change in the emotional state of the rider and the target value of the user experience parameter, wherein the target value of the user experience parameter includes a configuration of entertainment settings in the vehicle to mitigate the predicted unfavorable change in the emotional state of the rider; and
adjusts the entertainment settings in the vehicle based on the target value of the user experience parameter.
2. The data processing system of claim 1 , further comprising a wearable sensor configured to be worn by the rider, to collect the data indicative of the physiological condition of the rider, and to communicate the data indicative of the physiological condition of the rider to the vehicle control system.
3. The data processing system of claim 1 , wherein the machine learning model includes a first neural network.
4. The data processing system of claim 3 , wherein the first neural network determines the measure of the emotional state of the rider.
5. The data processing system of claim 4 , wherein the vehicle control system includes a second neural network that adjusts the entertainment settings in the vehicle.
6. The data processing system of claim 5 , wherein the second neural network optimizes the entertainment settings in the vehicle in real time responsive to the measure of the emotional state of the rider being determined by the first neural network.
7. The data processing system of claim 5 , wherein the first neural network comprises a plurality of connected nodes that form a directed cycle and facilitates bi-directional flow of data among the plurality of connected nodes.
8. The data processing system of claim 5 , wherein the first neural network is a recurrent neural network to predict the unfavorable change in the emotional state of the rider in the vehicle through recognition of patterns of the data indicative of the physiological condition of the rider received from the sensor associated with the rider.
9. The data processing system of claim 8 , wherein the second neural network is a radial basis function neural network to optimize, for achieving a favorable emotional state of the rider, the entertainment settings in the vehicle in response to the prediction of the unfavorable change in the emotional state of the rider.
10. The data processing system of claim 9 , wherein the radial basis function neural network determines the entertainment settings in the vehicle that are to be adjusted.
11. The data processing system of claim 1 , wherein the vehicle control system determines the entertainment settings in the vehicle that are to be adjusted.
12. The data processing system of claim 1 , wherein the entertainment settings in the vehicle include at least one of news content, music content, comedy content, or sports content.
13. A method of promoting satisfaction of a rider in a vehicle, comprising:
determining, using a machine learning model included in a data processing system, a measure of an emotional state of the rider based on data received from a sensor associated with the rider, wherein the data is indicative of a physiological condition of the rider,
predicting, using the machine learning model, an unfavorable change in the emotional state of the rider based on a pattern in the data;
determining, using a vehicle control system associated with the data processing system, a target value of a user experience parameter of the vehicle based on a correlation between a mitigation of the predicted unfavorable change in the emotional state of the rider and the target value of the user experience parameter, wherein the user experience parameter includes a configuration of entertainment settings in the vehicle to mitigate the unfavorable change in the emotional state of the rider; and
adjusting the entertainment settings the vehicle based on the target value of the user experience parameter.
14. The method of claim 13 , wherein the sensor associated with the rider is a wearable sensor configured to be worn by the rider, to collect the data indicative of the physiological condition of the rider, and to communicate the data indicative of the physiological condition of the rider to the vehicle control system.
15. The method of claim 13 , wherein the machine learning model includes a first neural network.
16. The method of claim 15 , wherein the first neural network is used to determine the measure of the emotional state of the rider.
17. The method of claim 16 , wherein the vehicle control system includes a second neural network that is used to adjust the entertainment settings in the vehicle.
18. The method of claim 17 , wherein the second neural network is used to adjust the entertainment settings in the vehicle in real time responsive to the measure of the emotional state of the rider being determined using the first neural network.
19. The method of claim 17 , wherein the first neural network comprises a plurality of connected nodes that form a directed cycle and facilitates bi-directional flow of data among the plurality of connected nodes.
20. The method of claim 13 , wherein the entertainment settings in the vehicle include at least one of news content, music content, comedy content, or sports content.