In one aspect, an example method includes: (a) collecting sensor data from one or more vehicles operating within a geographic region and environmental data for the geographic region; (b) generating an accident risk model using one or more machine learning models; (c) receiving a request for navigating between a first geographic position and a second geographic position; (d) identifying attributes of one or more routes between the first geographic position and the second geographic position and through the geographic region, wherein the identified attributes of the one or more routes are based on at least the generated accident risk model; and (e) transmitting an instruction that causes a mobile computing device to display a graphical indication of: (i) the one or more routes; and (ii) the identified attributes of the one or more routes.

A system for collecting traffic information includes a vehicle having at least one sensor for collecting dead reckoning (DR) information, and a global positioning system (GPS) receiver configured to receive GPS information, and a server that maps the DR information and the GPS information to a server map to generate first map matching information, and collects the first map matching information as the traffic information when it is determined that a version of a software platform of the vehicle is a latest version and all the DR information and the GPS information are received.

A processing unit segments the route into a plurality of sections. It is, for each section, obtains a set of route section characteristic values, R.sub.SCV, that will impact the energy consumption of the vehicle whilst driving within the section, obtains a set of vehicle energy consumption values, V.sub.ECV, that will impact the energy consumption of the vehicle whilst driving within the section at least partly based on R.sub.SCV, estimates a first probability distribution, P.sub.1, of the energy consumption for the vehicle whilst driving within the section based on R.sub.SCV, V.sub.ECV, and a first set of traffic information values, T.sub.1, within the section, estimates a second probability distribution, P.sub.2, of the energy consumption for the vehicle whilst driving within the section based on R.sub.SCV, V.sub.ECV, and a second set of traffic information values, T.sub.2, within the section, estimates a traffic flow indicator, I.sub.TF, for the section based on R.sub.SCV, V.sub.ECV and a third set of traffic information values, T.sub.3, within the section, and determines a route section probability distribution, P.sub.RS, of the energy consumption for the vehicle whilst driving within the section based on the relation between I.sub.TF, P.sub.1, and P.sub.2.

A method of obtaining a route from a point of departure to a destination includes providing route guidance through an augmented reality (AR) view including an image captured by a camera. To provide route guidance, as a user terminal moves toward a destination on the basis of an obtained route, when an interaction occurs between the user terminal and a node or a link in which predetermined information included in the route is registered, content associated with the predetermined information is augmented and displayed as guidance information on an image of an AR view.

Systems, methods, computer-readable media, and apparatuses for providing hazard detection and broadcast functions are provided. In some examples, sensor data may be captured by a mobile device, vehicle, or the like. The data may be used to detect a hazard, identify a type of hazard, and the like. One or more users or groups of users for notification of the hazard may be identified and one or more notifications may be transmitted to users within the group.

This disclosure describes systems and methods for personalized route prediction. An example method may include receiving, at a first time, first input data associated with a first route traversed by a vehicle. The example method may also include populating a first database with the input data. The example method may also include receiving, at a third time, second input data associated with a second route traversed by the vehicle. The example method may also include comparing the second input data to the first input data included within the first database. The example method may also include determining, based on the comparison, a first cluster including the first data and the second input data or a second cluster including the second input data. The example method may also include populating a second database based on the first cluster or the second cluster. The example method may also include determining, using the first database and at a second time, at least one of: predicted departure data, predicted destination data, and/or predicted route data. The example method may also include causing, based on the predicted departure data, predicted destination data, and/or predicted route data, to perform an action in association with the vehicle.

A vehicle routing evaluation system makes predictions about network performance data along road segments relevant to navigation of a vehicle and generates routing instructions or recommendations based in part on the network performance data. The vehicle routing evaluation system may send control signals to automatically control navigation of the vehicle to follow the routing generated routing or may present the navigation instructions to a teleoperator that controls the vehicle. Alternatively, the vehicle routing evaluation system generates a display with a navigational map overlay providing visual cues indicative of predicted wireless performance along various road segments to enable the teleoperator to select between possible routes.

This disclosure relates to a distributed data center that includes resources carried by a fleet of vehicles. The system includes sensors configured to generate output signals conveying information related to the vehicles and/or the surroundings of vehicles. The system includes a remote computing server configured to maintain map data and distribute it to the fleet, including local map data to individual vehicles pertaining to their surroundings. Individual vehicles may compare the local map data with the information related to their individual surroundings. Based on such comparisons, individual vehicles may detect discrepancies between the local map data and the information related to their individual surroundings. The remote computing server may modify and/or update the map data based on the detected discrepancies.

In one embodiment, a driving environment is perceived based on sensor data obtained from a variety of sensors, including determining a current speed of an ADV. In response to a request for driving with an idle speed, a speed guideline is generated based on an idle speed curve in view of the current speed of the ADV. A speed planning operation is performed by optimizing a cost function based on the speed guideline to determine the speeds of the trajectory points at different points in time along a trajectory planned to drive the ADV. One or more control commands are then generated to control the ADV with the planned speeds along the planned trajectory, such that the ADV moves according to an intended idle speed.

A method of generating map data indicating a deviation from expected jam conditions on a segment of a plurality of segments in an area covered by an electronic map, the segment having associated therewith a jam probability indicating the likelihood of a jam on that segment. The method comprises establishing an expected jam condition for the segment based on the jam probability of that segment, and then obtaining live data indicating the jam conditions on at least one of the plurality of other segments in the area. A revised jam condition can then be established for the segment using the obtained live data.