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Benchmarking and Functional Decomposition of Automotive Lidar Sensor Models

Rosenberger, Philipp ; Holder, Martin Friedrich ; Huch, Sebastian ; Winner, Hermann ; Zofka, Marc René ; Zöllner, Johann Marius ; D'hondt, Thomas ; Wassermann, Benjamin (2019)
Benchmarking and Functional Decomposition of Automotive Lidar Sensor Models.
IEEE Intelligent Vehicles Symposium (IV) 2019. Paris, France (June 9-12, 2019)
doi: 10.1109/IVS.2019.8814081
Conference or Workshop Item, Bibliographie

Abstract

Simulation-based testing is seen as a major requirement 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.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Rosenberger, Philipp ; Holder, Martin Friedrich ; Huch, Sebastian ; Winner, Hermann ; Zofka, Marc René ; Zöllner, Johann Marius ; D'hondt, Thomas ; Wassermann, Benjamin
Type of entry: Bibliographie
Title: Benchmarking and Functional Decomposition of Automotive Lidar Sensor Models
Language: English
Date: 10 June 2019
Place of Publication: Paris
Event Title: IEEE Intelligent Vehicles Symposium (IV) 2019
Event Location: Paris, France
Event Dates: June 9-12, 2019
DOI: 10.1109/IVS.2019.8814081
Corresponding Links:
Abstract:

Simulation-based testing is seen as a major requirement 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.

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD)
16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD) > Driver Assistance
16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD) > Safety
16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD) > Test Methods
Date Deposited: 22 Sep 2019 19:55
Last Modified: 24 May 2023 09:23
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