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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.
Our current mobility paradigm increasingly faces economic, ecological, and social limits in urban areas. The aim of this paper is to analyse if a fleet of shared autonomous electric vehicles (AEVs) can meet these challenges while satisfying the current requirements of privately-owned internal combustion engine vehicles (ICEVs). Therefore, analytical models have been developed to simulate and investigate the impacts of mobility behaviour in Berlin and Stuttgart (Germany). The collected data were used to calculate the fleet size, the energy consumption, the emission of particulate matter, nitrogen oxides, and the carbon footprint of different shared AEVs in comparison with privately owned ICEVs. The approach shows that the system of a shared AEV fleet could lower externalities (accident avoidance, traffic jams, free spaces, parking costs and lifetime losses) in cities and generate cost benefits for customers.
Application of Induction Thermography for Detection of Near Surface Defects in Steel Products
(2020)
This paper describes the modelling, simulation and energy management of a fuel cell hybrid heavy-duty truck. For this purpose, a longitudinal dynamic model of a 26t truck was set up and the load requirement for the drive train was determined based on a driving cycle. To meet this load requirement as efficiently and dynamically as possible three different energy management strategies were implemented, tested and the impact on the overall system was analysed. In addition, the behaviour of the hybrid system with the various energy management strategies with different battery capacity is shown and analysed.
This paper provides an analysis of the trend in autonomous driving traffic and the development of infrastructural support, whereas the requirements on the infrastructural support will be analyzed. Then selected traffic scenes will be implemented in an autonomous driving simulator tool in order to figure out the required parameters to assist the autonomous vehicle from the infrastructure.