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Recursive state estimation for lane detection using a fusion of cooperative and map based data

: Lorenz, Patrick; Schäufele, Bernd; Sawade, Oliver; Radusch, Ilja

Postprint urn:nbn:de:0011-n-3830507 (1.1 MByte PDF)
MD5 Fingerprint: 28cc76daf97cdff514e7758808099e3e
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Created on: 21.6.2016

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 18th International Conference on Intelligent Transportation Systems, ITSC 2015. Proceedings : Smart Mobility for Safety and Sustainability; 15-18 September 2015, Gran Canaria, Spain
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2015
ISBN: 978-1-4673-6595-6 (Print)
ISBN: 978-1-4673-6596-3
International Conference on Intelligent Transportation Systems (ITSC) <18, 2015, Gran Canaria>
European Commission EC
FP7-ICT; 318621; TEAM
Tomorrow's Elastic, Adaptive Mobility
Conference Paper, Electronic Publication
Fraunhofer FOKUS ()
lane detection; cooperative driving; cooperative positioning

Modern automated and cooperative driver assistance systems (CoDAS) rely deeply on the position estimation. Regardless of absolute positioning accuracy, the relative position in regard to driving environment and other vehicles needs to be of high quality to enable sophisticated functions. Global Navigation Satellite Systems (GNSS) fulfill this demand only partially. In this paper we present an algorithm to accurately infer the driving lane by utilizing Dedicated Short Range Communication (DSRC) and map data alone. We evaluate our approach against simulated and real-life data from Europes largest cooperative vehicle Field Operational Test (FOT): simTD. This lane detection algorithm will be an enabler for CoDAS functions like collaborative driving and merging developed in the TEAM IP project.