Benchmarking and Functional Decomposition of Automotive Lidar Sensor Models

Resource type
Conference
Author(s)
Philipp Rosenberger, Martin Holder, Sebastian Huch, Hermann Winner, Tobias Fleck, Marc René Zofka, J. Marius Zöllner, Thomas D’hondt and Benjamin Wassermann
Journal
2019 IEEE Intelligent vehicles Symposium (IV), Paris
Year
2019
Book title
Intelligent Vehicles Symposium 2019
Abstract
Simulation-based testing is seen as a major re- quirement for the safety validation of highly automated driving. One crucial part of such test architectures are models of environment perception sensors such as camera, lidar and radar sensors. Currently, an objective evaluation and the comparison of different modeling approaches for automotive lidar sensors are still a challenge. In this work, a real lidar sensor system used for object recognition is first functionally decomposed. The resulting sequence of processing blocks and interfaces is then mapped onto simulation methods. Subsequently, metrics applied to the aforementioned interfaces are derived, enabling a quantitative comparison between simulated and real sensor data at different steps of the processing pipeline. Benchmarks for several existing sensor models at a concrete selected interface are performed using those metrics by comparing them to measurements gained from the real sensor. Finally, we outline how metrics on low-level interfaces can correlate with results on more abstract ones. A major achievement of this work lies within the commonly accepted interfaces and a common understanding of real and virtual lidar sensor systems and, even more important, an initial guideline for the quantitative comparison of sensor models with the ambition to support future validation of virtual sensor models.
Research focus
Safe and Intelligent Vehicles
Project
Enable-S3
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Published by
Tobias Fleck