Transportation system having an artificial intelligence system to determine an emotional state of a rider in a vehicle

Abstract

A transportation system includes an artificial intelligence (AI) system for processing feature vectors of an image of a face of a rider in a vehicle to determine an emotional state of the rider and optimizing an operational parameter of the vehicle to improve the emotional state. The AI system is to maintain a favorable emotional state of the rider via a modular neural network that includes a rider emotional state determining neural network to process the feature vectors to detect patterns. The patterns indicate at least one of the favorable emotional state and an unfavorable emotional state. The modular neural network includes an intermediary circuit to convert data from the rider emotional state determining neural network into vehicle operational state data. The modular neural network includes a vehicle operational state optimizing neural network to adjust the operational parameter of the vehicle in response to the vehicle operational state data.

What is claimed is: 
     
         1 . A transportation system, comprising:
 an artificial intelligence system for processing feature vectors of an image of a face of a rider in a vehicle to determine an emotional state of the rider and optimizing an operational parameter of the vehicle to improve the emotional state of the rider;   wherein the artificial intelligence system is to maintain a favorable emotional state of the rider via a modular neural network, the modular neural network comprising:
 a rider emotional state determining neural network to process the feature vectors of the image of the face of the rider in the vehicle to detect patterns, wherein the patterns in the feature vectors indicate at least one of the favorable emotional state and an unfavorable emotional state; 
 an intermediary circuit to convert data from the rider emotional state determining neural network into vehicle operational state data; and 
 a vehicle operational state optimizing neural network to adjust the operational parameter of the vehicle in response to the vehicle operational state data. 
   
     
     
         2 . The transportation system of  claim 1  wherein the vehicle operational state optimizing neural network is to adjust the operational parameter of the vehicle for achieving the favorable emotional state of the rider. 
     
     
         3 . The transportation system of  claim 1  wherein the vehicle operational state optimizing neural network is to optimize the operational parameter based on a correlation between a vehicle operating state and a rider emotional state. 
     
     
         4 . The transportation system of  claim 1  wherein the operational parameter of the vehicle that is optimized is determined and adjusted to induce a favorable rider emotional state. 
     
     
         5 . The transportation system of  claim 1  wherein the rider emotional state determining neural network further learns to classify the patterns of the feature vectors and associate the pattern of the feature vectors to emotional states and changes thereto from a training data set sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. 
     
     
         6 . The transportation system of  claim 1  wherein the vehicle operational state optimizing neural network is to optimize the operational parameter in real time responsive to the detecting of a change in the emotional state of the rider by the rider emotional state determining neural network. 
     
     
         7 . The transportation system of  claim 1  wherein the rider emotional state determining neural network is to detect a pattern of the feature vectors that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state, wherein the vehicle operational state optimizing neural network is to optimize the operational parameter of the vehicle in response to the indicated change in emotional state. 
     
     
         8 . The transportation system of  claim 1  wherein the artificial intelligence system comprises a plurality of connected nodes that form a directed cycle, the artificial intelligence system further facilitating bi-directional flow of data among the connected nodes. 
     
     
         9 . The transportation system of  claim 1  further comprising a feature vector generation system that processes images of the face of the rider captured over time from a plurality of image capture devices while the rider is in the vehicle, the processing of the images producing the feature vectors. 
     
     
         10 . The transportation system of  claim 1  further comprising image capture devices disposed to capture an image of the face of the rider in the vehicle from a plurality of perspectives and an image processing system that produces feature vectors from an image captured from at least one of the plurality of perspectives. 
     
     
         11 . The transportation system of  claim 10  further comprising an interface between the rider emotional state determining neural network and the image processing system through which a time sequence of feature vectors that represent an emotional state of the rider are communicated. 
     
     
         12 . The transportation system of  claim 1  wherein the feature vectors indicate at least one of the emotional state of the rider is changing, the emotional state of the rider is stable, a rate of change of the emotional state of the rider, a direction of change of the emotional state of the rider, and a polarity of a change of the emotional state of the rider; the emotional state of a rider is changing to the unfavorable emotional state; and the emotional state of the rider is changing to the favorable emotional state. 
     
     
         13 . The transportation system of  claim 1  wherein the operational parameter that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. 
     
     
         14 . The transportation system of  claim 1  wherein the vehicle operational state optimizing neural network interacts with a vehicle control system to adjust the operational parameter. 
     
     
         15 . The transportation system of  claim 1  wherein the artificial intelligence system further comprises a neural net that includes one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. 
     
     
         16 . The transportation system of  claim 1  wherein the rider emotional state determining neural network comprises one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. 
     
     
         17 . The transportation system of  claim 1  wherein the rider emotional state determining neural network includes a recurrent neural network to indicate a change in the emotional state of the rider in the vehicle through recognition of patterns of the feature vectors of the image of the face of the rider in the vehicle; the transportation system further comprising:
 a vehicle control system to control operation of the vehicle by adjusting a plurality of vehicle operational parameters; and 
 a feedback loop to communicate the indicated change in the emotional state of the rider between the vehicle control system and the artificial intelligence system, wherein the vehicle control system is to adjust at least one of the plurality of vehicle operational parameters in response to the indicated change in the emotional state of the rider. 
 
     
     
         18 . The transportation system of  claim 17  wherein the at least one of the plurality of vehicle operation parameters that is responsively adjusted affects operation of a powertrain of the vehicle and a suspension system of the vehicle. 
     
     
         19 . The transportation system of  claim 17  further comprising a vehicle operational state radial basis function neural network wherein the radial basis function neural network interacts with the recurrent neural network via an intermediary component of the artificial intelligence system that produces vehicle control data indicative of an emotional state response of the rider to a current operational state of the vehicle. 
     
     
         20 . The transportation system of  claim 17  wherein the modular neural network comprises the rider emotional state recurrent neural network, a vehicle operational state radial basis function (RBF) neural network, and an intermediary system wherein the intermediary system processes rider emotional state characterization data from the recurrent neural network into vehicle control data that the RBF uses to interact with the vehicle control system for adjusting the at least one of the plurality of vehicle operational parameters. 
     
     
         21 . The transportation system of  claim 17  wherein the recognition of patterns of feature vectors comprises processing the feature vectors of the image of the face of the rider captured during at least two of before the adjusting at least one of the plurality of vehicle operational parameters, during the adjusting at least one of the plurality of vehicle operational parameters, and after adjusting at least one of the plurality of vehicle operational parameters. 
     
     
         22 . The transportation system of  claim 17  wherein the adjusting at least one of the plurality of vehicle operational parameters improves an emotional state of a rider in a vehicle. 
     
     
         23 . The transportation system of  claim 17  wherein the adjusting at least one of the plurality of vehicle operational parameters causes an emotional state of the rider to change from the unfavorable emotional state to the favorable emotional state, wherein the change is indicated by the recurrent neural network. 
     
     
         24 . The transportation system of  claim 17  wherein the recurrent neural network indicates a change in the emotional state of the rider responsive to a change in an operating parameter of the vehicle by determining a difference between a first set of feature vectors of an image of the face of a rider captured prior to the adjusting at least one of the plurality of operating parameters and a second set of feature vectors of an image of the face of the rider captured during or after the adjusting at least one of the plurality of operating parameters. 
     
     
         25 . The transportation system of  claim 17  wherein the recurrent neural network detects a pattern of the feature vectors that indicates an emotional state of the rider is changing from a first emotional state to a second emotional state, and wherein the vehicle control system adjusts the operational parameter of the vehicle in response to the indicated change in emotional state.