DocumentCode :
2516574
Title :
Probabilistic localization method for trains
Author :
Heirich, Oliver ; Robertson, Patrick ; García, Adrián Cardalda ; Strang, Thomas ; Lehner, Andreas
Author_Institution :
DLR (German Aerosp. Center), Inst. of Commun. & Navig., Wessling, Germany
fYear :
2012
fDate :
3-7 June 2012
Firstpage :
482
Lastpage :
487
Abstract :
The localization of trains in a railway network is necessary for train control or applications such as autonomous train driving or collision avoidance systems. Train localization is safety critical and therefore the approach requires a robust, precise and track selective localization. Satellite navigation systems (GNSS) might be a candidate for this task, but measurement errors and the lack of availability in parts of the railway environment do not fulfill the demands for a safety critical system. Therefore, additional onboard sensors, such as an inertial measurement unit (IMU), odometer and railway feature classification sensors (e.g. camera) are proposed. In this paper we present a top-down train localization approach from theory. We analyze causal dependencies and derive a general Bayesian filter. Furthermore we present a generic algorithm based on particle filter in order to process the multi-sensor data, the train motion and a known track map. The particle filter estimates a topological position directly in the track map without using map matching techniques. First simulations with simplified particular state and measurement models show encouraging results in critical railway scenarios.
Keywords :
collision avoidance; distance measurement; particle filtering (numerical methods); railway engineering; railway safety; satellite navigation; sensor fusion; GNSS; autonomous train driving; collision avoidance systems; general Bayesian filter; inertial measurement unit; map matching; multisensor data; odometer; particle filter; probabilistic localization method; railway feature classification sensors; railway network; safety critical system; satellite navigation systems; top-down train localization approach; topological position; track map; track selective localization; train control; Bayesian methods; Global Navigation Satellite Systems; Measurement uncertainty; Radar tracking; Rail transportation; Sensors; Tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium (IV), 2012 IEEE
Conference_Location :
Alcala de Henares
ISSN :
1931-0587
Print_ISBN :
978-1-4673-2119-8
Type :
conf
DOI :
10.1109/IVS.2012.6232194
Filename :
6232194
Link To Document :
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