The test-drive of an automobile along a given test-course can be modeled by formulating a suitable optimal control problem. However, if the length of the course is very long or if it has a very complicated structure, the numerical solution of the optimal control problem becomes very difficult. Therefore a moving horizon technique is employed, which splits the optimal control problem into a sequence of local optimal control problems that are combined by suitable continuity conditions. This approach yields a reference trajectory. A controller and differential GPS are integrated in a real-world car and allows a reference trajectory to be followed in realtime. A benefit of this approach is the very high accuracy obtained in reproducing the reference trajectory. Hence, it can be used for testing different setups of cars under the same conditions while excluding the comparatively large influence of a real-world driver. In this article, we will focus on a method for generating the reference trajectory and report our experiences with this algorithm. The method allows an locally optimal solution to be computed for various handling courses in a robust way.
This paper presents a novel approach to segmentation of a dense 3D point cloud, generated by a flash lidar type camera. Incorporating symmetries of the sensor, the algorithm is using a 2D grid approach to cluster data points and extrude object segments in complex scenes. The data representation allows for the handling of partially occluded, but connected objects at different ranges. The algorithm was tested on a variety of different sensor data sets and the obtained results are presented and discussed.
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