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 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.

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.   
     
     
         2 . The transportation system of  claim 1  wherein the artificial intelligence system includes:
 a first neural network to detect the emotional state of the rider through recognition of patterns of the feature vectors of the image of the face of the rider in the vehicle, the feature vectors indicating at least one of a favorable emotional state of the rider and an unfavorable emotional state of the rider; and 
 a second neural network to optimize, for achieving the favorable emotional state of the rider, the operational parameter of the vehicle in response to the detected emotional state of the rider. 
 
     
     
         3 . The transportation system of  claim 2  wherein the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network. 
     
     
         4 . The transportation system of  claim 2  wherein the second neural network optimizes the operational parameter based on a correlation between the vehicle operating state and the emotional state of the rider. 
     
     
         5 . The transportation system of  claim 2  wherein the second neural network is to determine an optimum value for the operational parameter of the vehicle, and the transportation system is to adjust the operational parameter of the vehicle to the optimum value to induce the favorable emotional state of the rider. 
     
     
         6 . The transportation system of  claim 2  wherein the first neural network further learns to classify the patterns in the feature vectors and associate the patterns with a set of emotional states and changes thereto by processing a training data set, wherein the training data set is sourced from at least one of a stream of data from an unstructured data source, a social media source, a wearable device, an in-vehicle sensor, a rider helmet, a rider headgear, and a rider voice recognition system. 
     
     
         7 . The transportation system of  claim 2  wherein the second neural network optimizes the operational parameter in real time responsive to the detecting of the emotional state of the rider by the first neural network. 
     
     
         8 . The transportation system of  claim 2  wherein the first neural network is to detect a pattern of the feature vectors, wherein the pattern is associated with a change in the emotional state of the rider from a first emotional state to a second emotional state, wherein the second neural network optimizes the operational parameter of the vehicle in response to the detection of the pattern associated with the change in the emotional state. 
     
     
         9 . The transportation system of  claim 2  wherein the first neural network comprises a plurality of interconnected nodes that form a directed cycle, the first neural network further facilitating bi-directional flow of data among the interconnected nodes. 
     
     
         10 . The transportation system of  claim 2  further comprising: a feature vector generation system to process a set of images of the face of the rider, the set of images captured over an interval of time from a plurality of image capture devices while the rider is in the vehicle, wherein the processing of the set of images is to produce the feature vectors of the image of the face of the rider. 
     
     
         11 . The transportation system of  claim 2  further comprising:
 image capture devices disposed to capture a set of images of the face of the rider in the vehicle from a plurality of perspectives; and 
 an image processing system to produce the feature vectors from the set of images captured from at least one of the plurality of perspectives. 
 
     
     
         12 . The transportation system of  claim 11  further comprising an interface between the first neural network and the image processing system to communicate a time sequence of the feature vectors, wherein the feature vectors are indicative of the emotional state of the rider. 
     
     
         13 . The transportation system of  claim 2  wherein the feature vectors indicate at least one of a changing emotional state of the rider, a stable emotional state of the rider, a rate of change of the emotional state of the rider, a direction of change of the emotional state of the rider, a polarity of a change of the emotional state of the rider; the emotional state of the rider is changing to the unfavorable emotional state; and the emotional state of the rider is changing to the favorable emotional state. 
     
     
         14 . The transportation system of  claim 2  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. 
     
     
         15 . The transportation system of  claim 2  wherein the second neural network is to interact with a vehicle control system to adjust the operational parameter. 
     
     
         16 . The transportation system of  claim 2  wherein the artificial intelligence system further comprises a neural network that includes one or more perceptrons that mimic human senses that facilitates determining the emotional state of the 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 artificial intelligence system includes:
 a recurrent neural network to indicate a change in the emotional state of the rider through recognition of patterns of the feature vectors of the image of the face of the rider in the vehicle; and 
 a radial basis function neural network to optimize, for achieving the favorable emotional state of the rider, the operational parameter of the vehicle in response to the indication of the change in the emotional state of the rider. 
 
     
     
         18 . The transportation system of  claim 17  wherein the radial basis function neural network is to optimize the operational parameter based on a correlation between a vehicle operating state and a rider emotional state. 
     
     
         19 . The transportation system of  claim 17  wherein the operational parameter of the vehicle that is optimized is determined and adjusted to induce a favorable rider emotional state. 
     
     
         20 . The transportation system of  claim 17  wherein the recurrent neural network further learns to classify the patterns of the feature vectors and associate the patterns 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.