Familiarity modeling

摘要

One or more embodiments of techniques or systems for modeling familiarity for a traveler are provided herein. Familiarity evidence can be received, indicative of how familiar a traveler is with an area or road segment, and based on a number of visits the traveler has made to that area. The familiarity evidence can be used to generate one or more familiarity models indicative of a predicted familiarity of locations around the area. Familiarity models can be based on kernels, graph distances, Markov random fields (MRFs), etc. When route directions are generated from an origin location to a destination location, one or more of the directions can be provided based on one or more of the familiarity models. For example, if a familiarity model indicates that a traveler is familiar with a route, driving directions of the route can be adapted to be more succinct.

主权项

1. A method for modeling familiarity for a traveler, comprising:
receiving familiarity evidence associated with one or more road segments of a map and one or more intersections of the map, wherein one or more of the intersections are connected via one or more of the road segments, wherein the familiarity evidence comprises one or more familiarity scores for one or more of the road segments or one or more of the intersections; generating a road network graph (RNG) comprising one or more RNG nodes corresponding to one or more of the intersections of the map and one or more RNG edges corresponding to one or more of the road segments of the map; generating a Markov random field (MRF) graph based on the RNG by connecting the RNG edges that are incident at a same intersection to form one or more nodes of the MRF graph and connecting at least a portion of the nodes of the MRF graph when corresponding edges of the RNG share a common node; and generating one or more familiarity models for the map based on the familiarity evidence, wherein one or more of the familiarity models are based on the MRF graph; and in response to detecting a difference between familiarity scores assigned to two nodes of a clique of the MRF graph exceeding a selected threshold in one of the familiarity models based on the MRF graph, adjusting the familiarity scores assigned to the two nodes to reduce the difference, wherein the adjusting comprises setting a fuel gauge or energy meter of a vehicle driven by the traveler to read lower than the actual reading of the vehicle fuel or energy level when the traveler is driving the vehicle in an unfamiliar area, wherein the receiving and the generating are implemented via a processing unit.