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.

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

A technique is described herein for automatically logging journeys taken by a user, and then automatically classifying the purposes of the journeys. In one implementation, the technique obtains journey data from one or more movement-sensing devices as a user travels from a starting location to an ending location in a vehicle. The technique generates a set of features based on the journey data, and then uses a machine-trainable model (such as a neural network) to make its classification based on the features. The machine-trainable model accepts at least one feature that is based on statistical information regarding at least one aspect of prior journeys that the user has taken. Overall, the technique provides a resource-efficient solution that rapidly provides personalized results to individual respective users. In some implementations, the technique performs its personalization without sharing journey data with a remote server.

A curbside management system and method for managing curbside use by connected and/or autonomous vehicles that includes one or more computing devices that execute instructions. One or more processors integrate at least a navigation component, an asset verification component, and a curbside management component, configured to generate recommended curbside access destinations for a vehicle associated with the customer and one of which is selected as the final curbside destination for the vehicle.

A method for activation or cancellation of presentation of navigation directions by a navigation system for a vehicle user operating a vehicle includes: —comparing a navigation route with a plurality of stored road stretches, wherein the stored road stretches are classified as known to the vehicle user, —activating presentation of navigation directions associated to the navigation route when travelling on a road stretch not included in the stored road stretches, or —cancelling presentation of navigation directions associated to the navigation route when travelling on a road stretch included in the stored road stretches.

A processing apparatus includes a control unit. The control unit is configured to acquire facility information containing an advertisement or publicity on a facility located along a travel route that a vehicle is scheduled to travel or a facility located within a predetermined range from the travel route, and, while the vehicle is traveling along the travel route, process an image of a first facility associated with the facility information or an image of a second facility present around the first facility based on the facility information and display the image of the first facility or the image of the second facility on a display provided in the vehicle.

A navigation device for a vehicle including a display unit, and a processor configured to determine a location of the navigation device, detect an object loaded into the vehicle by wirelessly communicating with the object, identify the detected object based on attribute information of the detected object, save a destination history of the identified object including destination information of the vehicle having the loaded identified object, and display at least one recommended destination on the display unit based on the destination history of the object in response to the identified object again being loaded into the vehicle after the destination history has been saved.

A system includes a GPS module, user interface module, database and routing engine. To find nearby POIs in one embodiment the routing engine identifies POIs in the database located within a threshold distance from the planned route in Euclidean distance. The routing engine filters the results by exploring the road network to determine whether each of the POIs is within the threshold distance of the route in driving distance. The filtering may include exploring outward from the POI, until all roads within the threshold distance are explored or the route is reached; alternatively, distance to the POIs is checked by exploring outward from the route until all roads within the threshold distance of the route have been exposed. For those POIs reached in the exploration, the nearest point on the route and the distance to that point are known. Similar searches are performed based on driving time rather than distance.

In one embodiment, a cognitive load driving assistant increases driving safety based on cognitive loads. In operation, the cognitive load driving assistant computes a current cognitive load of a driver based on sensor data. If the current cognitive load exceeds a threshold cognitive load, then the cognitive load driving assistant modifies the driving environment to reduce the cognitive load required to perform the primary driving task and/secondary task(s), such as texting via a cellular phone. The cognitive load driving assistant may modify the driving environment indirectly via sensory feedback to the driver or directly through reducing the complexity of the primary driving task and/or secondary tasks. In particular, if the driver is exhibiting elevated cognitive loads typically associated with distracted driving, then the cognitive load driving assistant modifies the driving environment to allow the driver to devote appropriate mental resources to the primary driving task, thereby increasing driving safety.

Navigation devices and methods of operation are provided. The navigation device includes a communication unit; a display; an input unit for receiving an input of data; a communication unit; and a controller for controlling operation of the display and the input unit. The controller connects to a data server through the communication unit, requests path setting information to the data server, receives the path setting information from the data server, acquires present position information of the navigation device, acquires a user moving path by reflecting the acquired position information and the received path setting information, and sets the user moving path as a guidance path. In this case, the path setting information is generated in another electronic device or the data server based on user input information input from the another electronic device.