Generating automatic road network definition files for unstructured areas using a multiclass support vector machine

Smooth and safe paths.Tolerance to the sensor measurement errors.First method in using Multiclass SVM for path planning.Better results in smoothness than other related SVM path planning methods.Similar results in terms of safety than other related SVM path planning methods. In this paper, an innovative methodology for the generation of a Road Network Definition File (RNDF) using only an obstacle map as input is presented. This RNDF, which relies on a Multiclass Support Vector Machine(MSVM)-based trajectory generation method, will be used by an autonomous vehicle for transporting people in closed, unstructured areas for which no previous information is available, such as residential areas or industrial parks. The advantages of using this technique are the generation of a safe and smooth trajectory graph (making the trip more comfortable for riders by having trajectories pass as far away as possible from obstacles). Moreover, although there exist other previous Support Vector Machine (SVM) path planning methods, this is the first to use a MSVM. The advantages of doing so are that by obtaining a decision boundary for each object in the scene, all possible trajectories are computed and joined to form a graph. This is done through a combination of a Nearest-Neighbor Graph (NNG) and a Relative Neighborhood Graph (RNG). The method was tested with real data and in real conditions, yielding good results. At the end of the paper, results for two kinds of studies are presented. The first set of tests is intended to determine the best parameter values for the proposed methodology. In the second set of evaluations, the approach is compared with other state-of-the-art SVM-based methods, as well as with a classical approach, demonstrating that the method outperforms them in some aspects. Furthermore, the source code of the method is available for testing, as are some videos in which the output of the method is shown, including a comparison with previous methods.

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