Adaptive and personalized navigation system
First Claim
1. A non-transitory machine-readable storage medium encoded with instructions that, when executed by one or more processors, cause the processor to carry out a process for generating one or more attribute models learned from a user'"'"'s driving preferences, the process comprising:
- receiving attribute data for a set of driving sessions for a user, wherein attribute data for each driving session includes measurements relevant to one or more target attributes, wherein each driving session is defined in terms of one or more road segments of one or more roads traversed by the user during the set of driving sessions;
applying attribute estimation rules to the attribute data to compute an attribute value for each target attribute along each road segment traversed at least once in the driving sessions;
assigning a default attribute value for one or more unseen road segments of the one or more roads identified in each driving session, wherein unseen road segments correspond to road segments not yet traversed by the user during any one of the driving sessions;
determining and storing an attribute model comprising attribute values computed or assigned for each road segment of the one or more roads traversed by the user during the set of driving sessions; and
accessing the attribute model to generate directions for use in navigation.
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Abstract
Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user'"'"'s personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver'"'"'s personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user'"'"'s personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
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Citations
19 Claims
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1. A non-transitory machine-readable storage medium encoded with instructions that, when executed by one or more processors, cause the processor to carry out a process for generating one or more attribute models learned from a user'"'"'s driving preferences, the process comprising:
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receiving attribute data for a set of driving sessions for a user, wherein attribute data for each driving session includes measurements relevant to one or more target attributes, wherein each driving session is defined in terms of one or more road segments of one or more roads traversed by the user during the set of driving sessions; applying attribute estimation rules to the attribute data to compute an attribute value for each target attribute along each road segment traversed at least once in the driving sessions; assigning a default attribute value for one or more unseen road segments of the one or more roads identified in each driving session, wherein unseen road segments correspond to road segments not yet traversed by the user during any one of the driving sessions; determining and storing an attribute model comprising attribute values computed or assigned for each road segment of the one or more roads traversed by the user during the set of driving sessions; and accessing the attribute model to generate directions for use in navigation. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer-implemented method of generating one or more attribute models learned from a user'"'"'s driving preferences, comprising:
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receiving, by one or more processors, attribute data for a set of driving sessions for a user, wherein attribute data for each driving session includes measurements relevant to one or more target attributes, wherein each driving session is defined in terms of one or more road segments of one or more roads traversed by the user during the set of driving sessions, wherein receiving attribute data for a set of driving sessions for a user comprises receiving sensor data; applying, by the one or more processors, attribute estimation rules to the attribute data to compute an attribute value for each target attribute along each road segment traversed at least once in the driving sessions; assigning, by the one or more processors, a default attribute value for one or more unseen road segments of the one or more roads identified in each driving session, wherein unseen road segments correspond to road segments not yet traversed by the user during any one of the driving sessions; determining and storing, by the one or more processors, an attribute model comprising attribute values computed or assigned for each road segment of the one or more roads traversed by the user during the set of driving sessions; and accessing, by the one or more processors, the attribute model to generate directions for use in navigation. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19)
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Specification