Infrastructure supported autonomous driving is getting in the focus of current research. In this work, we investigated the usage of a traffic monitoring infrastructure combined with the environmental model of an autonomous driving vehicle for localization. The forwarded environmental model of the infrastructure contains tracked road users, but the used tracking algorithm is unknown. The result is based on a two-stage transformation process with optimized fusion and tracking by a Kalman filter. Experiments show, that the algorithm provides a consistent localization at the first time step.
Vehicle Localization Using Infrastructure Sensing
Lect.Notes Mobility
International Forum on Advanced Microsystems for Automotive Applications ; 2020 ; Berlin, Germany May 26, 2020 - May 27, 2020
11.12.2020
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Roadside sensors , Pose estimation , Information fusion , Infrastructure sensors , MEC-Server , Localization , Intelligent vehicles , Optimized fusion , Kalman filter , MEC-View project , Automated driving Automotive Engineering , Cyber-physical systems, IoT , Engineering , Transportation Technology and Traffic Engineering , Robotics and Automation
VEHICLE INFRASTRUCTURE COOPERATIVE LOCALIZATION USING FACTOR GRAPHS
British Library Conference Proceedings | 2016
|Underground infrastructure sensing using unmanned aerial vehicle (UAV)
Europäisches Patentamt | 2021
|Underground infrastructure sensing using unmanned aerial vehicle (UAV)
Europäisches Patentamt | 2023
|UNDERGROUND INFRASTRUCTURE SENSING USING UNMANNED AERIAL VEHICLE (UAV)
Europäisches Patentamt | 2019
|UNDERGROUND INFRASTRUCTURE SENSING USING UNMANNED AERIAL VEHICLE (UAV)
Europäisches Patentamt | 2021
|