Mining GPS data to augment road models
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Instead of traditional ways of creating road maps, an attractive alternative is to create a map based on GPS traces of regular drivers. One important aspect of this approach is to automatically compute the number and locations of driving lanes on a road. We introduce the idea of using a Gaussian mixture model (GMM) to model the distribution of GPS traces across multiple traffic lanes. The GMM naturally accounts for the inherent spread in GPS data. We present a new variation of the GMM that enforces constant lane width and GPS variance in each lane. For fitting the GMM, we also introduce a new regularizer that is sensitive to the overall spread of the GPS data across the road. Our experiments on real GPS data show that our new GMM is better at counting lanes than a more traditional GMM, and it gives more consistent results across our data set.