US 9,810,544 B2Granted

Adaptive and personalized navigation system

AssigneeGoogle Inc.

InventorsAndrew R. Golding, Jens Eilstrup Rasmussen

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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.

Claims19

Dependent claims

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.

3 more elements

A computer-implemented method of generating one or more attribute models learned from a user's driving preferences, comprising:

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.

3 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/080,781Filed 25 Mar 2016 · continuation
Priority: 2 Nov 2006
Published: US 2016/0209228 A121 Jul 2016
Granted: 7 Nov 2017
Term ends: Not estimatedThe source records don’t support an estimate.

Classification4 codes

G01C 21/3492
Special cost functions, i.e. other than distance or default speed limit of road segments: employing speed data or traffic data, e.g. real-time or…
G01C 21/3484
Special cost functions, i.e. other than distance or default speed limit of road segments: personalized, e.g. from learned user behaviour or…
G01C 21/36
Route searching; Route guidance: input/output arrangements for on-board computers
G01C
Measuring distances, levels or bearings

Family

Application11/556,120Parent of this continuation · filed 2 Nov 2006
Application12/414,461Parent of this division · filed 30 Mar 2009
US 7,512,487 B1Parent of this continuation · filed 31 Mar 2009
Application14/170,471Parent of this continuation · filed 31 Jan 2014
US 8,682,574 B2Parent of this division · filed 25 Mar 2014
US 2016/0209228 A1Pre-grant publication · published 21 Jul 2016
US 9,297,663 B1Parent of this continuation
Application14/170,471Parent of this division
Application12/414,461Parent of this continuation
US 2009/0192705 A1Published application
US 9,810,544 B2This patent

From the US filing history. Foreign family members aren’t listed.