Generating attribute models for use in adaptive navigation...

Details Generating attribute models for use in adaptive navigation... Generating attribute models for use in adaptive navigation...

2011-08-09

2011-08-09

Starks, Jr., Wilbert L (Department: 2129)

Data processing: artificial intelligence

Adaptive system

C706S045000

Reexamination Certificate

active

07996345

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.

REFERENCES:
patent: 6680674 (2004-01-01), Park
patent: 7239959 (2007-07-01), Rasmussen et al.
Chen et al, Vision-Based Autonomous Land Vehicle Guidance in Outdoor Road Environments Using Combined Line and Road Following Techniques, Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan 300, R.O.C., 1997, pp. 711-728.

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