Adaptive and personalized navigation system
First Claim
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.
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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.
47 Citations
20 Claims
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1. A computer-implemented method for travel time prediction, the method comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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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:
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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. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
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Specification