Some embodiments provide a mapping application that has a novel way of displaying traffic congestion along roads in the map. The mapping application in some embodiments defines a traffic congestion representation to run parallel to its corresponding road portion when the map is viewed at a particular zoom level, and defines a traffic congestion representation to be placed over its corresponding road portion when the map is viewed at another zoom level. The mapping application in some embodiments differentiates the appearance of the traffic congestion representation that signifies heavy traffic congestion from the appearance of the traffic congestion representation that signifies moderate traffic congestion. In some of these embodiments, the mapping application does not generate a traffic congestion representation for areas along a road that are not congested.

A shipment distribution system and method are presented. The system includes a plurality of goods shipments, one or more delivery vehicles, a tour planning system and a navigation system operating in each delivery vehicle and communicatively linked to the tour planning system. Each goods shipment has a destination address for delivery. Each of the delivery vehicles has a cargo area divided into loading zones. The tour planning system stores a volume of the cargo area of each delivery vehicle. The tour planning system transmits to the navigation system calculated tour routes to deliver each of the goods shipments to corresponding destination addresses according to the positions of the goods shipments in the cargo area and the destination address. Upon arrival at the destination address for each goods shipment, the tour planning system indicates the loading zone in the cargo area storing the goods shipment for delivery.

Disclosed is reducing starting time for a GNSS receiver that has an imprecise initial starting location by requesting starting assistance from a CDN that caches predictive data including first data indicated predicted LOS visibility from the receiver to individual satellites, wherein the request includes the imprecise initial staring location, receiving, from the CDN, data that includes a first block of the predictive data for the imprecise initial staring location and further adjoining second blocks of predictive data for areas surrounding the imprecise staring location, determining, by the GNSS receiver, commonly available satellites that have visibility from locations in both the first block and the second block, and calculating a first starting position using weighted values for the satellites, the commonly available satellites having higher weighted value than satellites without visibility in both locations, whereby position uncertainty of the first starting position is reduced from the imprecise initial starting location.

Systems and methods for determining autonomous vehicle user boarding times are provided. In one example embodiment, a computer implemented method includes obtaining location data associated with a user device associated with a user. The method includes determining an estimated time until the user starts boarding the autonomous vehicle based at least in part on the location data associated with the user device. The method includes obtaining data associated with the user. The method includes determining an estimated time of boarding duration for the user based at least in part on the data associated with the user. The method includes determining an estimated time until the user completes boarding of the autonomous vehicle based at least in part on the estimated time until the user starts boarding the autonomous vehicle and the estimated time of boarding duration for the user.

An electronic device for a vehicle includes a power supply configured to supply power, an interface configured to receive high-definition (HD) map data of a specified region from a server through a communication device and to receive traveling situation information of the vehicle, and at least one processor configured to continuously generate electronic horizon data of the specified region based on the HD map data in a state in which the power is received, and to adjust a speed of reception of the HD map data based on the traveling situation information.

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 server collects image pickup data picked up by an image pickup device that is held by a moving body. The server generates spot information in which information regarding a spot specified based on a post in a social networking service is associated with position information indicating a position of the spot and transmits the spot information to an information display device. The information display device displays a screen including first information for receiving a request for image pickup data regarding the spot information, and transmits request information including the position information of the spot information to the server when the first information is selected by a user. The server collects image pickup data picked up by the image pickup device in a region that satisfies a condition for the position indicated by the position information in the request information and transmits the collected data to the information display device.

Disclosed is determining GNSS satellite position visibility by possessing an orbital segment representing the transit of a satellite in orbit over time, a coarse ray angle interval, a fine ray angle interval, and a digital surface model. Disclosed is propagating coarse ray at coarse ray angle intervals increments in a first pass between an observable point and orbital segment at a respective coarse ray angle to determine whether the coarse ray is obstructed by features of the DSM, and recording a status of the coarse ray based on whether the coarse ray was obstructed. If pairs of successive coarse rays have different status, designating the coarse ray with NLOS visibility, then performing a second pass by propagating, per each designated coarse ray, fine rays at fine ray angle intervals, and saving an indication of time at which LOS visibility to the satellite is obstructed.

Technologies for sharing route navigation data in a community cloud include a mobile navigation device of a vehicle and a remote mobile navigation device of a remote vehicle. The mobile navigation device generates sensor data associated with a current route of the vehicle and determines whether a reference traffic event occurs within a segment of the current route of the vehicle. In response to a determination that a reference traffic event occurs, the mobile navigation devices transmits route update data to the remote mobile navigation device. Based on the route update data, the remote mobile navigation device updates a current route of the remote vehicle to avoid the reference traffic event within a corresponding segment of the current route of the remote vehicle. The mobile navigation device may also transmit the sensor data to a community compute device, which may transmit route update data to the remote mobile navigation device.

A method for automatically learning parking preferences for a driver and generating parking recommendations includes generating training data based at least in part on: 1) trajectory data indicating a trajectory of a vehicle during a plurality of parking events, each in which a driver of the vehicle selected a parking candidate from among a plurality of parking candidates, and 2) attribute data indicating attributes of each of the plurality of parking candidates, removing, from the training data, data associated with at least one available parking candidate based on one or more conditions that indicate the driver did not consider the at least one available parking candidate, and training a decision model, based on remaining training data, to estimate a preferred parking candidate for the driver from among a set of available parking candidates.