A system for transportation includes a vehicle configured to have a rider located therein or thereon, and an expert system to produce a recommendation for a configuration of the vehicle, wherein the recommendation includes at least one recommended parameter of configuration for the expert system that controls a parameter selected from the group consisting of a vehicle parameter, a rider experience parameter, and combinations thereof.

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.

An indoor mobile industrial robot system is configured to provide a weight to a detected object within an operating environment, where the weight relates to how static the feature is. The indoor mobile industrial robot system includes a mechanism configured to translate reflected light energy and positional information into a set of data points representing the detected object having at least one of Cartesian and/or polar coordinates, and an intensity. If any discrete data point within the set of data points representing the detected object has an intensity at or above a defined threshold the entire set of data points is converted into a weight and potentially classified representing a static feature, otherwise such set of data points is classified as representing a dynamic feature having a lower weight.

The technology relates controlling an autonomous vehicle through a multi-lane turn. In one example, data corresponding to a position of the autonomous vehicle in a lane of the multi-lane turn, a trajectory of the autonomous vehicle, and data corresponding to positions of objects in a vicinity of the autonomous vehicle may be received. A determination of whether the autonomous vehicle is positioned as a first vehicle in the lane or positioned behind another vehicle in the lane may be made based on a position of the autonomous vehicle in the lane relative to the positions of the objects. The trajectory of the autonomous vehicle through the lane may be adjusted based on whether the autonomous vehicle is positioned as a first vehicle in the lane or positioned behind another vehicle in the lane. The autonomous vehicle may be controlled based on the adjusted trajectory.

A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.

A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.

Systems and methods for controlling an autonomous vehicle and the service selection for an autonomous vehicle are provided. In one example embodiment, a computing system can obtain data indicative of a first vehicle service assignment for an autonomous vehicle. The first vehicle service assignment can be associated with a first service entity and indicative of a first vehicle service. The computing system can determine that the autonomous vehicle is available to perform a second vehicle service concurrently with the first vehicle service. The computing system can obtain data indicative of a second vehicle service assignment for the autonomous vehicle. The second vehicle service assignment can be associated with a second service entity that is different than the first service entity and is indicative of the second vehicle service. The computing system can cause the autonomous vehicle to concurrently perform the first vehicle service with the second vehicle service.

Provided herein is an autonomous or semi-autonomous vehicle fleet comprising a plurality of electric autonomous vehicles for apportioned display of a media, operating autonomously and a fleet management module for coordination of the autonomous vehicle fleet. Each autonomous or semi-autonomous vehicle comprising a screen configured to display the media. Activation, deactivation, brightness modification, in combination with specific media selection enables more efficient media display.

A dual agent reinforcement learning autonomous system (DARLAS) for the autonomous operation of aircraft and/or provide pilot assistance. DARLAS includes an artificial neural network, safe agent, and cost agent. The safe agent is configured to calculate safe reward Q values associated with landing the aircraft at a predetermined destination or calculated emergency destination. The cost agent is configured to calculate cost reward Q values associated with maximum fuel efficiency and aircraft performance. The safe and cost reward Q values are based on state-action vectors associated with an aircraft, which may include state data and action data. The system may include a user output device that provides an indication of an action to a user. The action corresponds to an agent action having the highest safe reward Q value and the highest cost require Q value. DARLAS prioritizes the highest safe reward Q value in the event of conflict.

The present disclosure includes systems, methods, and computer-readable storage media facilitating multi-target tracking with dependent likelihood structures. To facilitate tracking, sensor data associated with an environment that a vehicle is located in may be received. A set of candidate hypotheses may be obtained based on the sensor data. Each candidate hypothesis in the set of candidate hypotheses may include track and measurement information representative of candidate locations for one or more targets. The set of candidate hypotheses are evaluated against at least one criterion. A ranked set of hypotheses are determined based on the evaluation of the set of candidate hypothesis against the at least one criterion. The ranked set of hypotheses may include a subset of hypotheses selected from among the set of candidate hypotheses and may be used to estimate a location of each of the one or more targets.