SC²S Colloquium - October 21, 2016
|Date:||October 21, 2016|
|Time:||3:00 pm, s.t.|
Tobias Neuhauser: A Method for Simulation-based Parameter Optimization of Autonomous Emergency Braking
The parameters of an Autonomous Emergency Braking (AEB) system are tuned manually on a test-track up to now. This approach is time-consuming, costly and yields suboptimal performance of the AEB system. To overcome these disadvantages, a method for simulation-based parameter optimization of autonomous emergency braking systems is presented and implemented within this master’s thesis. First of all, contrary cost functions are modelled, which specify the target behavior of the AEB system. Their objectives are on the one hand the effectiveness of the AEB system and on the other hand the customer-acceptance. Moreover, an optimization strategy is presented, which is required to tackle the high-dimensional optimization problem consisting of more than one hundred parameters. This includes also a sensitivity analysis to screen for non-influential parameters. Different optimization methods are investigated for the high-dimensional optimization: direct, metamodel-based and hybrid optimization. The hybrid optimizer “Efficient Global Optimization” proves as the best one for this task. Finally, the so-optimized parameters are fine-tuned with the help of a gradient-based optimization
Benedikt Kucis: A GPU-accelerated Chebyshev Function Approximation on an Adaptive Tree Structure
This bachelor’s thesis describes the process of designing and implementing a GPU-accelerated Chebyshev function approximation on an Adaptive Tree Structure. The adaptive trees are used for storing Chebyshev coefficients computed using an input function for e.g. vorticity fields or Gaussian functions. Chebyshev points are optimal interpolation points to approximate smooth functions over a specified domain. The evaluation of the Chebyshev interpolant for an arbitrary point is computationally expensive. Because the Chebyshev approximation can be heavily parallelized, the goal of this work was to speed up the evaluation by utilizing the massively parallel architecture of GPUs. Existing CPU implementations in the PVFMM and TbSLAS libraries are explained and the process of implementing and optimizing the GPU version of the evaluation is described. The largest achieved speedup was about 2X the CPU performance.