Adaptive and personalized navigation system
First Claim
1. A machine-readable, non-transitory storage medium encoded with instructions that, when executed by one or more processors, cause the one or more processors to carry out a process for generating directions for use in navigation during a current driving session, the process comprising:
- receiving a target destination;
generating at least one candidate route;
probabilistically determining that one of a plurality of conditional variant models associated with a target attribute corresponds to a condition of the target attribute, the plurality of conditional variant models learned from previous user driving sessions;
scoring the at least one candidate route using the determined conditional variant model;
providing a scored route to a user;
detecting a change in the condition of the target attribute,wherein the detecting a change in the condition of the target attribute comprises;
recomputing probabilities for the conditional variant models associated with the target attribute periodically during the current driving session using subsequently observed data of the current driving session; and
probabilistically determining that a second conditional variant model currently corresponds to the condition of the target attribute; and
providing an altered route to the user based on the detected change.
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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
14 Claims
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1. A machine-readable, non-transitory storage medium encoded with instructions that, when executed by one or more processors, cause the one or more processors to carry out a process for generating directions for use in navigation during a current driving session, the process comprising:
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receiving a target destination; generating at least one candidate route; probabilistically determining that one of a plurality of conditional variant models associated with a target attribute corresponds to a condition of the target attribute, the plurality of conditional variant models learned from previous user driving sessions; scoring the at least one candidate route using the determined conditional variant model; providing a scored route to a user; detecting a change in the condition of the target attribute, wherein the detecting a change in the condition of the target attribute comprises; recomputing probabilities for the conditional variant models associated with the target attribute periodically during the current driving session using subsequently observed data of the current driving session; and probabilistically determining that a second conditional variant model currently corresponds to the condition of the target attribute; and providing an altered route to the user based on the detected change. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-implemented method to be implemented via one or more processors, for generating directions for use in navigation during a current driving session, comprising:
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receiving a target destination; generating, via the one or more processors, at least one candidate route; probabilistically determining, via the one or more processors, that one of a plurality of conditional variant models associated with a target attribute corresponds to a condition of the target attribute, the plurality of conditional variant models learned from previous user driving sessions; scoring, via the one or more processors, the at least one candidate route using the determined conditional variant model; providing a scored route to a user; detecting, via the one or more processors, a change in the condition of the target attribute, wherein detecting a change in the condition of the target attribute comprises; recomputing probabilities for the conditional variant models associated with the target attribute periodically during the current driving session using subsequently observed data of the current driving session; and probabilistically determining that a second conditional variant model currently corresponds to the condition of the target attribute; and providing an altered route to the user based on the detected change. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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Specification