Neural net-based use of perceptrons to mimic human senses associated with a vehicle occupant
A system for operating a vehicle based on a state of a rider includes an artificial intelligence system, a vehicle control system, and a feedback loop. The artificial intelligence system processes a sensory input from a wearable device in a vehicle to determine a state of a rider and optimizes an operating parameter of the vehicle to improve the state of the rider. The artificial intelligence system includes a neural net with a perceptron to mimic human senses to facilitate determining a state of a rider based on an extent to which at least one of the senses of the rider is stimulated. The vehicle control system adjusts vehicle operating parameters and the feedback loop indicates the change in the state of the rider, where the vehicle control system adjusts at least one of the plurality of vehicle operating parameters responsive to the indication of the change.
1. A system for operating a vehicle based on an emotional state of a rider, the system comprising:
an artificial intelligence system for processing a sensory input from a wearable device in the vehicle to determine a state of the rider in the vehicle and optimizing an operating parameter of the vehicle to improve the state of the rider, wherein the artificial intelligence system is configured to execute a genetic algorithm to generate mutations from an initial operating state of the vehicle to determine at least one optimized vehicle operating state, wherein the at least one optimized vehicle operating state is optimized to improve the state of the rider,
the artificial intelligence system including a neural net with one or more perceptrons to mimic human senses to facilitate determining the state of the rider based on an extent to which at least one of the senses of the rider is stimulated, wherein the artificial intelligence system is to indicate a change in the state of the rider through recognition of patterns of emotional state indicative wearable sensor data of the rider in the vehicle;
a vehicle control system to control an operation of the vehicle by adjusting a plurality of vehicle operating parameters; and
a feedback loop through which the indication of the change in the state of the rider is communicated between the vehicle control system and the artificial intelligence system, wherein the vehicle control system adjusts at least one of the plurality of vehicle operating parameters responsive to the indication of the change to achieve the at least one optimized vehicle operating state.
2. The system of claim 1 , wherein the sensory input from the wearable device includes at least one of: heart rate data, galvanic skin response data, temperature data, or motion data of the rider.
3. The system of claim 1 , wherein the vehicle control system is further configured to adjust the at least one of the plurality of vehicle operating parameters to include at least one of: a vehicle speed, a vehicle acceleration, a vehicle deceleration, or a vehicle route.
4. The system of claim 1 , wherein the feedback loop includes a user interface within the vehicle configured to present the rider with real-time feedback regarding a current operating state of the vehicle and the emotional state of the rider.
5. The system of claim 1 , wherein the artificial intelligence system is further configured to utilize machine learning techniques to refine the genetic algorithm based on historical data of the emotional state of the rider and corresponding vehicle operating parameters.
6. The system of claim 1 , wherein the artificial intelligence system is further configured to communicate with an electronic commerce system interface to provide in-vehicle content adapted based on the emotional state of the rider.
7. The system of claim 1 , wherein the artificial intelligence system is further configured to optimize the at least one optimized vehicle operating state to achieve a favorable rider mood, wherein the favorable rider mood includes at least one of: relaxation or alertness.
8. The system of claim 1 , wherein the artificial intelligence system is further configured to predict changes in the emotional state of the rider based on a pattern recognition of the wearable sensor data over time.
9. The system of claim 1 , wherein the artificial intelligence system includes a second neural network trained to classify the emotional state of the rider based on the wearable sensor data and to predict future states of the rider within the vehicle.
10. A method for operating a vehicle based on an emotional state of a rider, the method comprising:
determining a state of the rider in the vehicle by processing, via an artificial intelligence system, a sensory input from a wearable device in the vehicle;
optimizing, via the artificial intelligence system, an operating parameter of the vehicle to improve the state of the rider;
generating, by executing a genetic algorithm via the artificial intelligence system, mutations from an initial operating state of the vehicle to determine at least one optimized vehicle operating state, wherein the at least one optimized vehicle operating state is optimized to improve the state of the rider,
wherein the artificial intelligence system includes a neural net with one or more perceptrons to mimic human senses to facilitate the determining the state of the rider based on an extent to which at least one of the senses of the rider is stimulated, wherein the artificial intelligence system is to indicate a change in the state of the rider through recognition of patterns of emotional state indicative wearable sensor data of the rider in the vehicle;
controlling, via a vehicle control system, an operation of the vehicle by adjusting a plurality of vehicle operating parameters;
feeding back, via a feedback loop, the indication of the change in the state of the rider by communicating the indication of the change in the state of the rider between the vehicle control system and the artificial intelligence system; and
adjusting, via the vehicle control system, at least one of the plurality of vehicle operating parameters in response to the indication of the change to achieve the at least one optimized vehicle operating state.
11. The method of claim 10 , wherein the sensory input from the wearable device includes at least one of: heart rate data, galvanic skin response data, temperature data, or motion data of the rider.
12. The method of claim 10 , wherein the at least one of the plurality of vehicle operating parameters includes at least one of: a vehicle speed, a vehicle acceleration, a vehicle deceleration, or a vehicle route.
13. The method of claim 10 , further comprising:
presenting to the rider, via a user interface within the vehicle, real-time feedback regarding a current operating state of the vehicle and the emotional state of the rider, wherein the user interface is included in the feedback loop.
14. The method of claim 10 , further comprising:
refining the genetic algorithm based on historical data of the emotional state of the rider and corresponding vehicle operating parameters, wherein the refining is via the artificial intelligence system utilizing machine learning techniques.
15. The method of claim 10 , further comprising:
communicating by the artificial intelligence system with an electronic commerce system interface; and
adapting in-vehicle content based on the emotional state of the rider.
16. The method of claim 10 , further comprising:
optimizing, via the artificial intelligence system, the at least one optimized vehicle operating state to achieve a favorable rider mood, wherein the favorable rider mood includes at least one of: relaxation or alertness.
17. The method of claim 10 , further comprising:
predicting, via the artificial intelligence system, changes in the emotional state of the rider based on recognition of a pattern in the wearable sensor data over time.
18. The method of claim 10 , further comprising:
training a second neural network to classify the emotional state of the rider based on the wearable sensor data;
classifying the emotional state of the rider based on the wearable sensor data; and
predicting future states of the rider within the vehicle, wherein the second neural network is included in the artificial intelligence system.