US 9,810,545 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 Nov. 21, 2016, as Appl. No. 15/356,866.
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 15/356,866 is a continuation of application No. 15/080,781, filed on Mar. 25, 2016.
Application 15/080,781 is a continuation of application No. 14/170,471, filed on Jan. 31, 2014, granted, now 9,297,663, issued on Mar. 29, 2016.
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 2017/0067749 A1, Mar. 9, 2017
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)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for travel time prediction, the method comprising:
receiving, by one or more computing devices, a route request associated with a user;
determining, by the one or more computing devices, one or more candidate routes responsive to the route request, wherein each of the one or more candidate routes comprises one or more road segments;
accessing, by the one or more computing devices, a plurality of different road speed models, wherein each road speed model comprises a model of vehicle speeds on one or more of the one or more road segments during different conditions, each road speed model having been created by a road speed modeler configured to:
collect observations of road speed data during a plurality of driving sessions, the road speed data including a set of sensor measurements relevant to road speed; and
create the plurality of different road speed models corresponding to different conditions that impact the road speed;
selecting, by the one or more computing devices, at least one of the plurality of different road speed models based at least in part on one or more conditions associated with the route request; and
employing, by the one or more computing devices, the selected at least one road speed model to predict one or more travel times respectively for the one or more candidate routes.
 
13. 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 comprising:
receiving a route request associated with a user;
determining one or more candidate routes responsive to the route request, wherein each of the one or more candidate routes comprises one or more road segments;
accessing a plurality of different road speed models, wherein each road speed model comprises a model of vehicle speeds on one or more of the one or more road segments during different conditions, each road speed model having been created by a road speed modeler configured to:
collect observations of road speed data during a plurality of driving sessions, the road speed data including a set of sensor measurements relevant to road speed; and
create the plurality of different road speed models corresponding to different conditions that impact the road speed;
selecting at least one of the plurality of different road speed models based at least in part on one or more conditions associated with the route request; and
employing the selected at least one road speed model to predict one or more travel times respectively for the one or more candidate routes.