US 9,810,544 B2
Adaptive and personalized navigation system
Andrew R. Golding, Mountain View, CA (US); and Jens Eilstrup Rasmussen, San Francisco, CA (US)
Assigned to Google Inc., Mountain View, CA (US)
Filed by Google Inc., Mountain View, CA (US)
Filed on Mar. 25, 2016, as Appl. No. 15/80,781.
Application 14/170,471 is a division of application No. 12/414,461, filed on Mar. 30, 2009, granted, now 8,682,574, issued on Mar. 25, 2014.
Application 15/080,781 is a continuation of application No. 14/170,471, filed on Jan. 31, 2014, granted, now 9,297,663.
Application 12/414,461 is a continuation of application No. 11/556,120, filed on Nov. 2, 2006, granted, now 7,512,487, issued on Mar. 31, 2009.
Prior Publication US 2016/0209228 A1, Jul. 21, 2016
Int. Cl. G01C 21/00 (2006.01); G01C 21/34 (2006.01); G01C 21/36 (2006.01)
CPC G01C 21/3492 (2013.01) [G01C 21/3484 (2013.01); G01C 21/36 (2013.01)] 19 Claims
OG exemplary drawing
 
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.
 
12. 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.