Abstract: A recommendation system for recommending a configuration of a vehicle includes an expert system that produces a recommendation of a parameter for configuring a vehicle control system that controls at least one of a vehicle parameter and a vehicle rider experience parameter.

Abstract: A system for transportation includes a vehicle occupied by a rider, and an artificial intelligence system for processing a voice of the rider to classify an emotional state of the rider and optimizing at least one operating parameter of the vehicle to improve the emotional state of the rider.

Abstract: A rider state modification system for improving a state of a rider in a vehicle includes a first neural network that operates to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet-of-things device during operation of the vehicle. The rider state modification system further includes a second neural network that operates to optimize at least one operating parameter of the vehicle based on the classified state of the vehicle, information about a state of a rider occupying the vehicle, and information that correlates vehicle operation with an effect on rider state.

Abstract: A system may collect human operator interactions with a vehicle control system interface operatively connected to a vehicle, and may collect vehicle response and operating conditions associated at least contemporaneously with the human operator interaction. Environmental information is collected contemporaneously with the human operator interaction.

Abstract: Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

Abstract: A hybrid neural network for rider satisfaction includes a first neural network to detect a detected satisfaction state of a rider occupying a vehicle through analysis of data gathered from sensors deployed in the vehicle for gathering physiological conditions of the rider. The hybrid neural network further includes a second neural network to optimize, for achieving a favorable satisfaction state of the rider, an operational parameter of the vehicle in response to the detected satisfaction state of the rider.

Abstract: A system may include a first neural network trained to determine an operating state of a vehicle from data about the vehicle captured in an operating environment of the vehicle, where the first neural network processes information about the vehicle captured by at least one Internet-of things device while the vehicle is operating. A data structure facilitates determining operating parameters configured to influence an operating state of a vehicle. A second neural network operates to: a) process information about a state of the rider occupying the vehicle, b) determine a correlation between the operating state and an effect on the state of the rider, and c) improve at least one of the determined operating parameters of the vehicle based on i) the determined operating state of the vehicle and ii) the correlation between the operating state of the vehicle and the effect on the state of the rider.

Abstract: A vehicle to operate with a rider according to an operating parameter. The vehicle includes a set of physiological monitoring sensors configured to measure a physiological parameter of a rider within the vehicle. The vehicle further includes a neural network trained on data related to a set of rider in-vehicle experiences to determine a state of the rider by processing outputs of the set of physiological monitoring sensors. The vehicle further includes an augmented or virtual reality system configured to present augmented reality content to the rider within the vehicle based, at least in part, on the physiological parameter. The vehicle further includes an optimization system to automatically identify a variation in the operating parameter to improve a measure of the state of the rider and generate a command to vary the operating parameter and the augmented reality content according to the variation.

Abstract: A transportation system optimizes an operating parameter of a vehicle based on a physiological state of an occupant of the vehicle. The transportation system includes a sensor to sense a physiological condition of the occupant and to output data based on the sensed physiological condition. The sensor includes a voice capture system to capture a voice of the occupant and includes the voice as at least part of the output data. The transportation system further includes an artificial intelligence system to receive the output data, classifies an emotional state of the captured voice based on analysis of aspects of the output data and processes the classified emotional state of the captured voice to determine an emotional state of the occupant and to optimize, for achieving a favorable emotional state of the occupant, at least one operating parameter of the vehicle in response to the determined emotional state of the occupant.

Abstract: A system for transportation includes a vehicle having a user interface, and a robotic process automation system wherein a set of data is captured for each user in a set of users as each user interacts with the user interface, and wherein an artificial intelligence system is trained using the set of data to interact with the vehicle to automatically undertake actions with the vehicle on behalf of the user.

Abstract: A system for transportation includes a self-driving vehicle, an artificial intelligence (AI) system in communication with the vehicle, and a vehicle routing system to plan a planned route for the vehicle to meet a common transportation need. The vehicle is to autonomously follow the planned route. The AI system includes a data processing system to gather social media-sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources, process the data to identify a subset of the individuals who form a social group based on group affiliation references in the data, and detect keywords in the data indicative of the transportation need. A neural network is trained to predict transportation needs based on the detected keywords to identify the common transportation need for the subset of the individuals.

Abstract: Vehicles and methods described herein include a vehicle that operates with a rider according to an operating parameter. The vehicle includes: a physiological monitoring sensor configured to measure a physiological parameter of the rider; an experience hybrid neural network trained on outcomes related to a rider in-vehicle experience associated with the physiological parameter to determine an emotional state of the rider; an augmented reality system configured to present augmented reality content to the rider of the vehicle based, at least in part, on the operating parameter; and an optimization hybrid neural network that identifies a variation in the operating parameter to change the emotional state of the rider and that generates a command to vary the operating parameter and the augmented reality content according to the variation.

Abstract: A transportation system, that optimizes at least one operating parameter of a vehicle based on a physiological state of an occupant of the vehicle, includes a sensor that senses a physiological condition of the occupant and that outputs data based on the sensed physiological condition. The transportation system further includes an artificial intelligence system that receives and processes the data to determine an emotional state of the occupant, and optimizes, for achieving a favorable emotional state of the occupant, the at least one operating parameter of the vehicle in response to detecting the emotional state of the occupant.

Abstract: A transportation system includes an artificial intelligence system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The artificial intelligence system detects the rider emotional state in the self-driving vehicle by recognition of patterns of emotional state indicative data from a set of wearable sensors worn by the rider. The patterns are indicative of at least one of a favorable emotional state and an unfavorable emotional state of the rider. The artificial intelligence system is to optimize, for achieving at least one of maintaining a detected favorable emotional state of the rider and achieving a favorable emotional state of a rider subsequent to a detection of an unfavorable emotional state, the operating parameter of the vehicle in response to the detected emotional state of the rider.

Abstract: A vehicle includes a display disposed to facilitate presenting an augmentation of content in an environment of a rider of the vehicle; a circuit for registering at least one of location and orientation of the vehicle; a machine learning circuit that determines at least one augmentation parameter by processing at least one input relating to at least one of the rider and the vehicle; and a reality augmentation circuit that, responsive to the at least one of the location or the orientation of the vehicle, generates an augmentation element for presenting in the display, the generating based at least in part on the at least one augmentation parameter.

Abstract: A transportation system includes an artificial intelligence (AI) system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The AI system includes a recurrent neural network to indicate a change in the emotional state of the rider by a recognition of patterns of emotional state indicative wearable sensor data from a set of wearable sensors worn by the rider. The patterns are indicative of a first degree of a favorable emotional state of the rider and/or a second degree of an unfavorable emotional state of the rider. The AI system further includes a radial basis function neural network to optimize, for achieving a target emotional state of the rider, the vehicle operating parameter in response to the indication of the change in the rider emotional state.

Abstract: Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

Abstract: A rider interface for a vehicle includes a data processor configured to facilitate communication between a rider using the rider interface and the vehicle, the vehicle and the rider interface communicating location and orientation of the vehicle. An augmented reality system with a display is disposed to facilitate presenting an augmentation of content in an environment of the rider using the rider interface, the augmentation responsive to a registration of the communicated location and orientation of the vehicle, wherein at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider and the rider interface.

Abstract: A method of optimizing rider satisfaction includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as indicative of an effect on a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, at least one aspect of rider satisfaction affected by an effect on the transportation system derived from the social media data classified as indicative of an effect on the transportation system. The method still further includes optimizing, using a third neural network of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying a vehicle in the transportation system.

Abstract: Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.