Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

A method includes: selecting a set of access points in a focus zone that identifies an area around the travel destination; determining access paths through one or more access points in the set of selected access points; listing crossings that the access paths pass through; for each of the listed crossings: determining a probability that the listed crossing is passed through by an access path; determining at least one complexity score of the listed crossing; determining a set of points of interest (POIs) along access paths passing through the listed crossing; and determining quality scores for the POIs, respectively, the quality score of one of the POIs reflecting a utility of that one of the POIs to facilitate orientation; selecting POIs from the set of POIs based on the quality scores; generating the access map including the access paths and the selected POIs; and outputting the access map.

A POI (Point Of Interest) information updating device (1) includes an input unit (10) configured to input POI identification information for identifying a POI, an acquisition unit (11) configured to acquire event information on an event performed at a target POI identified by the POI identification information input by the input unit, a search unit (12) configured to search for similar event information similar to the event information acquired by the acquisition unit (11), and an updating unit (13) configured to update information on the target POI on the basis of information on a POI at which an event indicated by the similar event information searched for by the search unit (12) has been performed.

A processing apparatus is provided, to, for each candidate point-of-interest of at least one candidate point-of-interest identified for association with a road in a network of roads for determining a name for the road, generate first data indicative of a relationship relating to a distance between the candidate point-of-interest and a road segment of the road, generate second data indicative of a relationship between the road segment and a projection of the candidate point-of-interest in a direction of the road segment, and, if the first and second relationships satisfy a first and second conditions for association respectively, generate data indicative of the candidate point-of-interest being an associated point-of-interest, process data corresponding to the associated point-of-interest to extract name data indicative of a road name associated with the associated point-of-interest, and generate, based on the name data, data indicative of the name for the road.

Methods for providing a location of an object or objects of interest that may be performed by a processor of a computing device may include generating a map of an environment around the computing device by a first simultaneous location and mapping (SLAM) operation using information received from the optical sensor, identifying an object of interest in the environment of the computing device by an object recognition operation using information received from the optical sensor, determining a location of the object or objects of interest in the environment, and presenting the determined location of the object or objects of interest in the environment in response to a trigger event correlated to the object or objects of interest.

The present disclosure provides a method of providing guidance for use of electric power of an electric vehicle capable of limiting use of electric power in a vehicle-to-load (V2L) mode or providing various pieces of information on use of the electric power. The method includes receiving information on a set travel route to an electric vehicle charging station in the V2L mode, calculating battery energy requirements necessary for the vehicle to move from the current location to a charging station along the travel route, calculating available amount of energy in the V2L mode based on the battery energy requirements and the current amount of energy of a battery, calculating an available usage time of an electronic product based on electric power consumption per unit time of the electronic product and the available amount of energy, and displaying the available usage time of the electronic product through an information-providing device.

Point-of-Interest (POI) data is stored according to a first, POI library as a flat file in a database. An engine is positioned between the POI library and an interface. The interface is configured to interact with data structured in a hierarchical manner and stored according to a second library (e.g., a modeling interface utilizing folders). The engine functions to wrap a POI object taken from a table of the POI library, into an augmented object recognized and handled by the second library of the (hierarchical) interface. To accomplish this, the engine augments the POI table with an additional column that is to be indexed by the second library. In this manner, embodiments may impart additional, hierarchy-based functionality to existing POI handling schemes. Particular embodiments may impart enhanced security (e.g., permissions at the data object-level) to existing POI schemes that implement permissions only at the POI-level and/or at the application-level.

Systems and methods for providing product samples to users in autonomous vehicles, thereby allowing businesses to advertise their products via the rideshare platform. A physical advertising platform is provided leveraging existing rideshare and delivery infrastructure to allow businesses to advertise their products by providing samples in rideshare vehicles and/or with rideshare vehicle deliveries. Consumers have the opportunity to try new products from local businesses that they may otherwise not be exposed to. In some implementations, a rideshare application interface can allow users to see what new local product samples are available. In some examples, a user can select one or more product samples to try.

A system for providing information to navigate to a parking space preferred by an operator of a vehicle can include a processor and a memory. The memory can store a preliminary operations module and a communications module. The preliminary operations module can include instructions that cause the processor to obtain, for the operator: (1) a first model for a first duration between a commencement of a parking space identification process and a first threshold and (2) a second model for a second duration between the first threshold and a second threshold. The communications module can include instructions that cause the processor to communicate, to a component of the vehicle for an action, information to navigate to the parking space. The parking space can be identified, in response to a current time being within: (1) the first duration, by the first model and (2) the second duration, by the second model.

An apparatus, method and computer program product are provided for predicting road locations that are likely to induce tire pressure changes. In one example, the apparatus receives historical data of an event in which a first vehicle is impacted by a first tire pressure change at a first road segment. The historical data indicates point-of-interest (POI) information associated with the first road segment. The apparatus trains a prediction model using the historical data. The prediction model is configured to determine a likelihood in which a second road segment induces a second tire pressure change for a second vehicle based on POI information associated with the second road segment.