Generic architecture for simulation of ADAS sensors

Hanke, Timo; Hirsenkorn, Nils; Bernhard, Dehlink; Rauch, Andreas; Rasshofer, R.H.; Biebl, Erwin

doi:10.1109/irs.2015.7226306

Cited by 41 publications

(27 citation statements)

References 6 publications

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“…It enables to derive generic interfaces of such systems later used for sensor model validation. Related to functional decomposition, a generic architecture for sensor models has been proposed [5].…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…Phenomenological sensor models use stochastics to model sensor behavior. These models include, for example, Gaussian noise models for errors in the position measurement of other road users (Hanke et al, 2015). The standard ISO 23150 is currently being developed to describe the logical interface between sensors and the fusion unit for automated driving.…”

Section: Simulationmentioning

confidence: 99%

Requirement Analysis of 3d Road Space Models for Automated Driving

Schwab

Kolbe

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Automated driving has received a high degree of public attention in recent years as it will lead to profound changes in mobility, society and urban development. Despite several product announcements from automobile manufacturers and mobility providers, many questions have not yet been answered completely. The need of lane-level HD maps was widely discussed and has been the reason for company acquisitions. HD maps are tailored towards supporting the operation of an automated vehicle. However, the development of this technology also requires road space models, but with a completely different focus and level of detail. Therefore, this article investigates the system development and testing challenges of automated driving. Based on this, requirements of road space models for developing automated driving are derived and gaps to current standards are indicated.</p>

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“…Usually, simple geometric models are computationally rather cheap to evaluate, but do not offer enough flexibility to model detection ranges in detail. In contrast to this, very detailed sensor specific models typically require high computation power [4,6]. Also, they are often used for sensor hardware development and testing of low level data-processing.…”

Section: Generic Sensor Modelsmentioning

confidence: 99%

Fast generic sensor models for testing highly automated vehicles in simulation

Stolz

Nestlinger

2018

Elektrotech. Inftech.

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Automated driving is one of the big trends in automotive industry nowadays. Over the past decade car manufacturers have increased step by step the number and features of their driving assistance systems. Besides technical aspects of realizing a highly automated vehicle, the validation and approval poses a big challenge to automotive industry. Reasons for this are grounded in the complexity of interaction with the environment, and the difficulties in proving that an automated vehicle shows at least the same performance as a human driver, with respect to safety. There is a common agreement within the community, that physical vehicle testing only, will practically not be feasible for validation. Simulation is seen as a key enabler for validation as well as for development of highly automated vehicles. Several commercial simulation tools are available to model and test the entire chain of "sens -plan -act". But still there is a lack of fast generic sensor models, for the use from development to vehicle in the loop tests. This article proposes a fast generic sensor model to be used in testing and validation of highly automated driving functions in simulation.

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Generic architecture for simulation of ADAS sensors

Cited by 41 publications

References 6 publications

Benchmarking and Functional Decomposition of Automotive Lidar Sensor Models

Benchmarking and Functional Decomposition of Automotive Lidar Sensor Models

Requirement Analysis of 3d Road Space Models for Automated Driving

Fast generic sensor models for testing highly automated vehicles in simulation

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