Vehicle Classification

Deshpande, Shashank; Muron, Wiktor; Cai, Yang

doi:10.1002/9781118971666.ch3

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…Meanwhile, the object candidates get classified using information from tracking. This is achieved through mostly rule-or machine learning-based algorithms [15]. The point clusters and their corresponding object candidates are illustrated in Fig.…”

Section: Functional Lidar Sensor System Decompositionmentioning

confidence: 99%

Benchmarking and Functional Decomposition of Automotive Lidar Sensor Models

Rosenberger

Holder

Hueh

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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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.

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Section: Functional Lidar Sensor System Decompositionmentioning

confidence: 99%