Methods and systems for managing charging stations for electric vehicles are described. According to one aspect of the present invention, an identity of a user driving a vehicle and contact information are obtained when the vehicle is detected to occupy a charging station or charger for charging the vehicle. The user is then periodically updated of the charging status and notified of the completion when the charging has reached to the full capacity of battery in the vehicle or a predefined charging level set by the user. A fee is assessed when the vehicle has not been removed from the charger after a predefined period (e.g., 5 minutes). As a result, a charging station or a charger can be used more efficiently as the demand for charging stations and the EVs are getting popular.

Described herein are embodiments of an autonomous drone for refueling. The autonomous drone may comprise a fuel storage area; a fuel delivery mechanism coupled to the fuel storage area; a delivery connection coupled to the fuel delivery mechanism; a drive mechanism; and a central controller. The central controller may be configured to receive a refueling location; and control the drive mechanism to pilot the autonomous drone to the refueling location.

Examples of the present disclosure describe systems and methods for intelligent ad-based routing. In example aspects, a destination input, desired time for arrival, and user profile data is received in a ride-sharing application. The input data is classified by applying one or more machine-learning models to the data. Based on the classified data results, candidate physical advertisement locations may be selected along a certain route. Different types of routes may be selected that range from the shortest possible route (i.e., a direct route) to a major detour (i.e., the most cost-effective route). A major detour takes the user on a route that exposes the user to as many advertisements as possible while still arriving at the final destination before the desired time of arrival. In exchange for a longer route and more exposure to physical advertisements, the cost of the ride may be offset.

A vehicle information display system includes a digital license plate attachable to a vehicle and having a display able to present electronically readable visual information. This electronically readable visual information is usable to facilitate provision of services, including but not limited to vehicle rental or providing vehicle access to authorized service providers.

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.

A method for improving a state of a rider through optimization of operation of a vehicle includes capturing vehicle operation-related data with at least one Internet-of-things device, and analyzing the captured data with a first neural network that determines a state of the vehicle based at least in part on a portion of the captured vehicle operation-related data. The method further includes receiving data descriptive of a state of a rider occupying the operating vehicle, and using a neural network to determine at least one vehicle operating parameter that affects a state of a rider occupying the operating vehicle. The method still further includes using an artificial intelligence-based system to optimize the at least one vehicle operating parameter so that a result of the optimizing includes an improvement in the state of the rider.

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.

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.

A method of robotic process automation for achieving an optimized margin of vehicle operational safety includes tracking expert vehicle control human interactions with a vehicle control-facilitating interface, and recording the tracked expert vehicle control human interactions in a robotic process automation system training data structure. The method further includes tracking vehicle operational state information of a vehicle, and recording vehicle operational state information in the robotic process automation system training data structure. The method further includes training, via at least one neural network, the vehicle to operate with an optimized margin of vehicle operational safety in a manner consistent with the expert vehicle control human interactions based on the expert vehicle control human interactions and the vehicle operational state information in the robotic process automation system training data structure, and controlling at least one aspect of the vehicle with the trained artificial intelligence system.

A system for transportation includes a vehicle interface for gathering hormonal state data of a rider in the vehicle. The system further includes an artificial intelligence-based circuit that is trained on a set of outcomes related to rider in-vehicle experience and that induces, responsive to the sensed rider hormonal state data, variation in one or more of the user experience parameters to achieve at least one desired outcome in the set of outcomes. The set of outcomes includes at least one outcome that promotes rider safety. The inducing variation includes control of timing and extent of the variation.