Authors:
Moritz Schweppenhäuser
1
;
Karl Schrab
2
;
Robert Protzmann
1
and
Ilja Radusch
2
Affiliations:
1
Fraunhofer Institute FOKUS, Kaiserin Augusta Allee 31, 10589 Berlin, Germany
;
2
TU Berlin / Daimler Center for Automotive IT Innovations, Ernst-Reuter-Platz 7, 10587 Berlin, Germany
Keyword(s):
Traffic State Estimation, Simulation, Vehicle Perception, Eclipse MOSAIC.
Abstract:
Modern-day navigation systems by developers like Google© and TomTom© require user participation primarily in the form of Floating Car Data (FCD) for accurate Traffic State Estimation (TSE). However, to provide reliable information, systems rely on large road user participation of at least 5 %, which is only truly available to the big players. We propose a method to soften the participation requirement by utilizing modern perception sensors (e.g., radar, lidar, camera) of connected vehicles (CVs) to enrich the FCD set, compensating reduced data quantity with increased data quality. By using position and speed estimates of surrounding vehicles we increase the sample size and can additionally collect estimates of segments that are not traversed by CVs. To validate and assess the proposed system, we utilize Eclipse MOSAIC and conduct a simulation-based test series on the calibrated large-scale BeST scenario. Initial findings indicate improved estimation performance on selected road segme
nts, especially at lower rates of market penetrations. In a network-wide investigation, we show that travel time estimates of the proposed method are often more accurate than conventional approaches, while also requiring smaller penetration rates.
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