GENERATING ATTRIBUTE MODELS FOR USE IN ADAPTIVE NAVIGATION SYSTEMS

• US 20100174479A1
• Filed: 03/15/2010
• Published: 07/08/2010
• Est. Priority Date: 11/02/2006
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Rasmussen, Jens Eilstrup, Golding, Andrew R.
• Granted Patent: US 7,996,345 B2
• Time in Patent Office: Days
• US Class Current: 701/200
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles

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.

Claims

1. A machine-readable medium encoded with instructions, that when executed by one or more processors, cause the processor to carry out a process for generating attribute models for use in a navigation system, the process comprising:

computing a value for each desired attribute along one or more road segments covered by a driving session for a user, wherein the desired attribute comprises at least one selected from a group consisting of road speed, road familiarity, road safety, and disfavored roads;

computing a default value for unseen road segments not yet traveled; and

forming one or more attribute models for each attribute based on the corresponding attribute values.