Abstract
AbstractThis paper presents a precise and robust road model for Map Matching (MM). Map matching approach is generally used to move vehicle localization output to the nearest road segment in order to improve the accuracy of vehicle lateral localization. Most of the MM approaches adopted piece-wise line segment road network model, which will generate large bias at curve segment or turning point on the intersection. In this paper, a two-dimensional parabola road network model is employed in order to correctly represent vehicle’s real state (position and orientation). Furthermore, an advanced longitudinal MM approach is also proposed here by comparing current road geometry model with visual lane detection results to improve longitudinal accuracy. Simulation and real road tests verified the proposed approaches.KeywordsGlobal Position SystemRoad NetworkRoad SegmentCurve SegmentDead ReckoningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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