An Improved Multi-Kernel Estimation Method for Vehicle Localization

Vehicle localization is one of the key functions in Intelligent Vehicles (IVs). Localization result is usually provided by the combination of GPS data and additional sensors, which are able to improve the localization precision. In this paper, a monocular camera and map database help vehicle localization, aiming to enhancing the localization performance. To this end, map-based road lane markings are constructed according to open source map. Then, vision-based markings and map-based markings are fused to obtain the improved vehicle fix, using an improved Multi-Kernel Estimation (MKE) method. The results using real data show that our method leads to an obvious improvement in vehicle localization accuracy.

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