Object Detection Probability for Highly Automated Vehicles: An Analytical Sensor Model

Florian Schiegg, Ignacio Llatser, Thomas Michalke

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 Harvard Style

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


in Bibtex Style

@conference{vehits19,
author={Florian 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},
}


in EndNote Style

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