@inproceedings{2d9b797564534319b7a31af26ebd9fb3,
title = "Modeling Perception Errors of Automated Vehicles",
abstract = "The assessment of automated driving relies increasingly on scenario-based virtual tests to achieve sufficient test coverage. Scenarios are generally based on ground truth information. Therefore, it is necessary to reproduce the view of the environment of the automated vehicle as it is seen by the autonomous driving function in the simulation. Typically, this view is erroneous compared to the ground truth due to sensor errors. This paper presents a novel approach to cluster, identify and finally to reproduce sensor errors by maneuver-dependent statistical models for the detection of other traffic objects. Errors are classified by their static and dynamic influences and incorporated into individual error models. These are evaluated in a final step based on real driving data.",
keywords = "Autonomous Vehicles, Sensor Errors, Sensor Models, Sensor Systems, Simulation, Vehicle Detection",
author = "Martin Sigl and Christoph Schutz and Sebastian Wagner and Daniel Watzenig",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 93rd IEEE Vehicular Technology Conference, VTC 2021-Spring ; Conference date: 25-04-2021 Through 28-04-2021",
year = "2021",
month = apr,
doi = "10.1109/VTC2021-Spring51267.2021.9448823",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings",
address = "United States",
}