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Institute
Connected Traffic Systems Based on Referenced Landmarks as Part of Conventional Road Infrastructure
(2022)
OAFuser: Online Adaptive Extended Object Tracking and Fusion using automotive Radar Detections
(2020)
This paper presents the Online Adaptive Fuser: OAFuser, a novel method for online adaptive estimation of motion and measurement uncertainties for efficient tracking and fusion by applying a system of several estimators for ongoing noise along with the conventional state and state covariance estimation. In our system, process and measurement noises are estimated with steady-state filters to obtain combined measurement noise and process noise estimators for all sensors in order to obtain state estimation with a linear Minimum Mean Square Error (MMSE) estimator and accelerating the system’s performance. The proposed adaptive tracking and fusion system was tested based on high fidelity simulation data and several real-world scenarios for automotive radar, where ground truth data is available for evaluation. We demonstrate the proposed method’s accuracy and efficiency in a challenging, highly dynamic scenario where our system is benchmarked with Multiple Model filter in terms of error statistics and run time performance.
A matter of reality
(2018)
Due to the increasing relevance of data, more and more data from various sources is accumulated for a variety of purposes. At the same time, however, there is a shortage of data in areas where it is urgently needed. Particularly in the field of machine learning, there is a lack of good and usable training data. Therefore, this research paper is concerned with the virtual data acquisition for the training of neural networks. For this purpose, first an application was developed that aims to generate virtual, automatically labeled data. Subsequently, a neural network was trained on the generated virtual data and tested on real data.