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.

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.

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.

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 computer-implemented method of generating and broadcasting telematics and/or image data is provided. Telematics and/or image data may be collected, with customer permission, in real-time by a mobile device (or a Telematics App running thereon) traveling within an originating vehicle. The telematics data may include acceleration, braking, speed, heading, and location data associated with the originating vehicle. The mobile device may generate an updated telematics data broadcast including up-to-date telematics data at least every few seconds; and then broadcast the updated telematics data broadcast at least every few seconds via wireless communication to another computing device to facilitate alerting another vehicle or driver of an abnormal traffic condition or event that the originating vehicle is experiencing. An amount that an insured uses or otherwise employs the telematics data-based risk mitigation or prevention functionality may be used with usage-based insurance, or to calculate or adjust insurance premiums or discounts.

A computer system configured to use emergency response system (EMS) vehicle telematics data to reduce risk of accidents may be configured to (1) receive, when the EMS vehicle is en route to an emergency location, the EMS vehicle telematics data associated with the EMS vehicle and including GPS location, speed, route, heading, acceleration, and/or lane data; (2) determine that a current route of an autonomous vehicle will interfere with the route of the EMS vehicle; (3) determine an alternate route for the autonomous vehicle to avoid interfering with the route of the EMS vehicle; and (4) direct the autonomous vehicle to (i) travel along the alternate route or (ii) pull over to a side of a road on the current route to allow the EMS vehicle to pass unimpeded. Insurance discounts may be generated based upon the risk mitigation or prevention functionality.

An information generation method is performed by an information generation device which generates information for a learning model that infers whether a mobile object is movable in a predetermined region. The information generation method includes: obtaining at least (i) first information and (ii) second information when the mobile object moves in a first region, the first information being obtained from a sensor provided in the mobile object, the second information relating to movement of the mobile object; inferring whether the mobile object is movable in the first region according to the second information; and generating fourth information for a learning model, the fourth information associating the first information, the second information, and third information with one another, the third information indicating an inference result which is obtained in the inferring.

A robot is provided. The robot includes a camera, a driving unit, and a processor. The robot is configured to, if a plurality of users included in one group are identified in an image captured via the camera, acquire profile information of each of the plurality of users, based on the profile information, acquire group feature information including group type information of the group, priority information of the plurality of users, and preferred waypoint information of the one group, and control the driving unit to perform a route guidance function based on the group feature information and destination information.

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.

Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. A system includes an inference module, a policy module, a graphical representation module, and a planning module. The inference module receives sensor data associated with a plurality of agents. The inference module also maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents of the plurality of agents as cooperative or aggressive. The policy module predicts future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents. The graphical representation module generates a graphical representation based on the sensor data and a graphical representation neural network. The planning module generates a motion plan for the ego agent based on the predicted future trajectories and the graphical representation.