Marek Junghans

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In this paper a method is introduced based on the concept of Bayesian networks (BNs), which is applied to model sensor fusion. Sensors can be characterised as real dynamical systems with specific physical functional principles, allowing to determine the value of a physical state of interest within certain ranges of tolerance. The measurements of the sensors(More)
A sensor system consisting of a multi layer laser scanner and a stereo camera is used to observe the surrounding environment of a vehicle. By a novel multi level fusion framework the heterogeneous sensor data can be merged on different processing levels. The following paper gives a short introduction to the processing steps of the sensor data and focuses on(More)
The Hough Transform is a histogram method for pattern recognition. In this paper an approach to apply the Hough Transform to the recognition of scale variant patterns is introduced. The approach is based on the Euclidean distance of the image and the pattern. Further, the concept of Graduated Non-Convexity (GNC) is applied to the problem of evaluating the(More)
In this paper we perform the analysis of the popular TPMS (tire pressure monitoring systems) and their application for traffic management purposes. In particular, we evaluate several of the commercially available TPMS devices and analyze their architecture and communication features. Furthermore, we propose the architecture of an external sensor device used(More)
Bayesian Data Fusion (BDF) is a well-established method in decision-level fusion to increase the quality of measured data of several equal or different sensors, e.g. [7], [13]. Although the method is powerful, the results of the fusion process are only (1) as good as the sensors are; (2) as good as the a priori knowledge about the sensors is and (3) as good(More)
In this paper, we evaluate Tire Pressure Monitoring System (TPMS) for traffic management purposes. It has been shown that up to 60% of the vehicles can be detected in urban traffic environments, which makes it suitable for deriving: routes, travel times and the traffic state. In particular, the theoretical background and basic concepts are given.(More)
In this paper the concept of Bayesian Networks (BN) is applied to the problem of traffic data acquisition by data fusion. Two wireless communication based sensors are used as data sources: IEEE 802.15.1 Bluetooth and IEEE 802.11p V2X (vehicle to vehicle and vehicle to infrastructure). Via V2X so called cooperative awareness messages (CAM) are received,(More)
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