Kai Schröter

Sewer modelling based on highly distributed calibration data sets and multi-objective auto-calibration schemes

Pollutant load modelling for sewer systems is state-of-the-art, especially for the estimation of discharged pollutant loads and development of sewer management strategies. However, conventionally obtained calibration data sets are often not exhaustive and have significant drawbacks. In the Graz West catchment area (Graz, Austria), continuous high-resolution long-term online measurements for discharge and pollutant concentration have been carried out since 2002. In this paper, the application of single- and multi-objective auto-calibration schemes based on evolution strategies for a deterministic hydrological pollutant load model will be discussed. Three approaches for pollutant load modelling are examined and compared: using a constant storm weather concentration and two build-up wash-off approaches with basic respectively extended wash-off equations. It is shown that the applied auto-calibration method leads to very satisfying results for both the calibration and the validation data set, and also for the dry and the storm weather runoff. However, until now, convective storms have not been convincingly represented. The build-up wash-off approach using the basic wash-off equation shows the best correlations between measured data and simulation results. As one of the chosen objectives for the multi-objective optimisation reacted highly sensitively to measurement errors, additional improvements can be expected after refining the criteria used in this algorithm.

Roll angle estimation for motorcycles: Comparing video and inertial sensor approaches

Advanced Rider Assistance Systems (ARAS) for powered two-wheelers improve driving behaviour and safety. Further developments of intelligent vehicles will also include video-based systems, which are successfully deployed in cars. Porting such modules to motorcycles, the camera pose has to be taken into account, as e. g. large roll angles produce significant variations in the recorded images. Therefore, roll angle estimation is an important task for the development of various kinds of ARAS. This study introduces alternative approaches based on inertial measurement units (IMU) as well as video only. The latter learns orientation distributions of image gradients that code the current roll angle. Until now only preliminary results on synthetic data have been published. Here, an evaluation on real video data will be presented along with three valuable improvements and an extensive parameter optimisation using the Covariance Matrix Adaptation Evolution Strategy. For comparison of the very dissimilar approaches a test vehicle is equipped with IMU, camera and a highly accurate reference sensor. The results state high performance of about 2 degrees error for the improved vision method and, therefore proofs the proposed concept on real-world data. The IMU-based Kalman filter estimation performed on par. As a naive result averaging of both estimates already increased performance an elaborate fusion of the proposed methods is expected to yield further improvements.