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.

The present invention provides a self-location estimation method including: a first step of estimating the self-location of a moving body (1) from the detection information of a plurality of sensors (5) to (8) by using a plurality of algorithms (11) to (13); a second step of determining a weighting factor for each algorithm from one or more state quantities A, B and C, which are obtained by estimation processing for each of a plurality of algorithms, by using a trained neural network (14); and a third step of identifying, as the self-location of the moving body (1), a location obtained by synthesizing the self-locations, which have been estimated by the algorithms, by using weighting factors.

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.

An apparatus for acquiring surrounding information of a vehicle includes: a camera configured to acquire an entire image of at least one surrounding vehicle; and a controller configured to derive at least one of coordinates of a wheel image area or coordinates of a front-rear image area included in an entire image area, and determine distance information from the vehicle to the at least one surrounding vehicle based on a relative positional relationship between the entire image area and the at least one of the wheel image area coordinates or the front-rear image area coordinates.

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.

This invention refers to a navigation method applicable to general transport meansair, maritime or land transport , particularly whenever constant checking on the route or path is required for taking a certain action. This invention also describes a system that uses said method.

This invention refers to a navigation method applicable to general transport meansair, maritime or land transport , particularly whenever constant checking on the route or path is required for taking a certain action. This invention also describes a system that uses said method.

Agricultural machines utilize global positioning systems (GPS) to acquire the location of the machine as well as the location of an event, which may be based upon an operation of the agricultural machine. Because of the possibility of outage and/or inaccuracy of the GPS, a GPS augmentation system can be included with the agricultural machine. The GPS augmentation system can supplement the location determination of the GPS, or can be used in place of the GPS when the GPS is not available. An unmanned vehicle can also be used as part of the augmentation system to provide additional information for the location of the agricultural machine and/or the event.

Systems and methods for enhancing task performance and computer readable maps produced by robots using modular sensors is disclosed herein. According to at least one non-limiting exemplary embodiment, robots may perform a first set of tasks, wherein coupling one or more modular sensors to the robots may configure a robot to perform a second set of tasks, the second set of tasks includes the first set of tasks and at least one additional task.