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d Road boundary result.
b Road boundary recognition.
b Road boundary recognition Fig. 21 K-means clustering and road boundary recognition.
Sometimes the ruts are more obvious than the road boundary.
Second, the road boundary is fuzzy and difficult to identify.
Fig. 20 DBSCAN clustering and road boundary recognition.
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This paper describes a method of robustly modeling road boundaries on-line for autonomous navigation.
A human driver reactively guides a vehicle to the final destination while performing a smooth trajectory and respecting the road boundaries.
Since sensory evidence for road boundaries might change from place to place, we cannot depend on a single cue but have to use multiple sensory features.
The potential for a vehicle include obstacles, road boundaries and all sides of the vehicle.
We give the car a sense of where the road boundaries are, and the car tries to figure out a way through those boundaries as quickly as possible.
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