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Authors: Florian Schiegg 1 ; Ignacio Llatser 2 and Thomas Michalke 3

Affiliations: 1 Institute of Communications Technology, Leibniz University of Hannover, Appelstraße 9A, Hannover, Germany, Robert Bosch GmbH, Corporate Research, Robert-Bosch-Strasse 200, Hildesheim and Germany ; 2 Robert Bosch GmbH, Corporate Research, Robert-Bosch-Strasse 200, Hildesheim and Germany ; 3 Robert Bosch GmbH, Corporate Research, Robert-Bosch-Campus 1, Renningen and Germany

ISBN: 978-989-758-374-2

Keyword(s): Sensor Model, Object Detection, Advanced Driver Assistance Systems, Collective Perception, V2X, Environmental Model, Highly Automated Driving, Cooperative Awareness.

Abstract: Modern advanced driver assistance systems (ADAS) increasingly depend on the information gathered by the vehicle’s on-board sensors about its environment. It is thus of great interest to analyse the performance of these sensor systems and its dependence on macroscopic traffic parameters. The work at hand aims at building up an analytical model to estimate the number of objects contained in a vehicle’s environmental model. It further considers the exchange of vehicle dynamics and sensor data by vehicle-to-vehicle (V2X) communication to enhance the environmental awareness of the single vehicles. Finally, the proposed model is used to quantify the improvement in the environmental model when complementing sensor measurements with V2X communication.

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Paper citation in several formats:
Schiegg, F.; Llatser, I. and Michalke, T. (2019). Object Detection Probability for Highly Automated Vehicles: An Analytical Sensor Model.In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-374-2, pages 223-231. DOI: 10.5220/0007767602230231

@conference{vehits19,
author={Florian Alexander Schiegg. and Ignacio Llatser. and Thomas Michalke.},
title={Object Detection Probability for Highly Automated Vehicles: An Analytical Sensor Model},
booktitle={Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2019},
pages={223-231},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007767602230231},
isbn={978-989-758-374-2},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Object Detection Probability for Highly Automated Vehicles: An Analytical Sensor Model
SN - 978-989-758-374-2
AU - Schiegg, F.
AU - Llatser, I.
AU - Michalke, T.
PY - 2019
SP - 223
EP - 231
DO - 10.5220/0007767602230231

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