<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Do, Loi</style></author><author><style face="normal" font="default" size="100%">Herman, Ivo</style></author><author><style face="normal" font="default" size="100%">Zdeněk Hurák</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Rolf Findeisen</style></author><author><style face="normal" font="default" size="100%">Sandra Hirche</style></author><author><style face="normal" font="default" size="100%">Klaus Janschek</style></author><author><style face="normal" font="default" size="100%">Martin Mönnigmann</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Onboard Model-based Prediction of Tram Braking Distance</style></title><secondary-title><style face="normal" font="default" size="100%">IFAC-PapersOnLine</style></secondary-title><short-title><style face="normal" font="default" size="100%">IFAC-PapersOnLine</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S2405896320326409</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin, Germany</style></pub-location><volume><style face="normal" font="default" size="100%">53</style></volume><pages><style face="normal" font="default" size="100%">15047 - 15052</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper, we document a design of a computational method for an onboard prediction of a breaking distance for a city rail vehicle—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.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom2><style face="normal" font="default" size="100%">&lt;p&gt;21th IFAC World Congress&lt;/p&gt;
</style></custom2></record></records></xml>