@conference {784,
	title = {Onboard Model-based Prediction of Tram Braking Distance},
	booktitle = {IFAC-PapersOnLine},
	volume = {53},
	year = {2020},
	month = {2020},
	pages = {15047 - 15052},
	publisher = {Elsevier},
	organization = {Elsevier},
	address = {Berlin, Germany},
	abstract = {<p>In this paper, we document a design of a computational method for an onboard prediction of a breaking distance for a city rail vehicle{\textemdash}a tram. The method is based on an onboard simulation of tram braking dynamics. Inputs to this simulation are the data from a digital map and the estimated (current) position and speed, which are, in turn, estimated by combining a mathematical model of dynamics of a tram with the measurements from a GNSS/GPS receiver, an accelerometer and the data from a digital map. Experiments with real trams verify the functionality, but reliable identification of the key physical parameters turns out critically important. The proposed method provides the core functionality for a collision avoidance system based on vehicle-to-vehicle (V2V) communication.</p>
},
	issn = {24058963},
	doi = {10.1016/j.ifacol.2020.12.2006},
	url = {https://www.sciencedirect.com/science/article/pii/S2405896320326409},
	author = {Do, Loi and Herman, Ivo and Zden{\v e}k Hur{\'a}k},
	editor = {Rolf Findeisen and Sandra Hirche and Klaus Janschek and Martin M{\"o}nnigmann}
}