Classification of sensor errors for the statistical simulation of environmental perception in automated driving systems

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

doi:10.1109/itsc.2016.7795621

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…Similar to the research interest of this paper, sensor modeling approaches like in Refs. [108][109][110][111] model object lists, which have been generated through both sensor hardware and perception software. This is common if the sensor's built-in perception algorithm is inaccessible to the modeling engineer due to sensor supplier intellectual property.…”

Section: System-under-test/system To Be Modeledmentioning

confidence: 99%

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A Review of Testing Object-Based Environment Perception for Safe Automated Driving

2022

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Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. The paper focuses on testing for verification and validation purposes at the interface between perception and planning, and structures the analysis along the three axes (1) test criteria and metrics, (2) test scenarios, and (3) reference data. Furthermore, the analyzed literature includes related safety standards, safety-independent perception algorithm benchmarking, and sensor modeling. It is found that the realization of safety-oriented perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved.

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Section: System-under-test/system To Be Modeledmentioning

confidence: 99%

“…Typically, sensor models aim at describing the specific phenomena of a sensor modality as detailed as feasible, no matter how safety-relevant these phenomena are [109,114]. However, some sensor modeling activities explicitly address this paper's research question.…”

Section: Relevance For Safety-oriented Perception Testingmentioning

confidence: 99%

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

2022

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“…To evaluate the cause-effect chains for requirement specification, first, effects are collected and sorted. The effects can be sorted into the data processing steps, where they occur in the sensor signal [21]. While in [21] only object models are considered, we propose to take the whole processing chain from emission to object identification into account, see Tab.…”

Section: Effectsmentioning

confidence: 99%

“…The effects can be sorted into the data processing steps, where they occur in the sensor signal [21]. While in [21] only object models are considered, we propose to take the whole processing chain from emission to object identification into account, see Tab. I. Consequently, a holistic view on modeling sensor systems, containing not only the front-end but also a complete data processing chain possibly up to object identification, is needed.…”

Section: Effectsmentioning

confidence: 99%

Towards Serious Perception Sensor Simulation for Safety Validation of Automated Driving - A Collaborative Method to Specify Sensor Models

Linnhoff

Rosenberger

Schmidt

et al. 2021

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

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Perception sensor modeling is essential for the safety validation of automated driving systems in virtual environments. Nevertheless, the community lacks a methodical approach to derive requirements for such sensor models that enables a serious application for safety validation in the first place. This article provides a method to specify sensor models for the environmental perception of automated driving systems.The key of the approach is a collaborative collection of causeeffect chains as the basis for specification. With this collection at hand, a tabular form is introduced to extract the relevance of the effect chains to be modeled. Combined profound expert assessments in the table enable the test engineer to specify sensor models within a traceable decision-making process.This work received funding from SET Level and VVM of the PEGASUS project family, promoted by the German Federal Ministry for Economic Affairs and Energy based on a decision of the Deutsche Bundestag.

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“…Accordingly, a statistical model of the perception process is proposed. Examples of statistical models can be found in [25,26]. In these models, the measurement and reference data drive the construction of the sensor model, where errors are calculated between data and the probability functions map the errors to reference data as the outputs of the model [27].…”

Section: Introductionmentioning

confidence: 99%

Phenomenological Modelling of Camera Performance for Road Marking Detection

Tarik

Arefnezhad

et al. 2021

Energies

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With the development of autonomous driving technology, the requirements for machine perception have increased significantly. In particular, camera-based lane detection plays an essential role in autonomous vehicle trajectory planning. However, lane detection is subject to high complexity, and it is sensitive to illumination variation, appearance, and age of lane marking. In addition, the sheer infinite number of test cases for highly automated vehicles requires an increasing portion of test and validation to be performed in simulation and X-in-the-loop testing. To model the complexity of camera-based lane detection, physical models are often used, which consider the optical properties of the imager as well as image processing itself. This complexity results in high efforts for the simulation in terms of modelling as well as computational costs. This paper presents a Phenomenological Lane Detection Model (PLDM) to simulate camera performance. The innovation of the approach is the modelling technique using Multi-Layer Perceptron (MLP), which is a class of Neural Network (NN). In order to prepare input data for our neural network model, massive driving tests have been performed on the M86 highway road in Hungary. The model’s inputs include vehicle dynamics signals (such as speed and acceleration, etc.). In addition, the difference between the reference output from the digital-twin map of the highway and camera lane detection results is considered as the target of the NN. The network consists of four hidden layers, and scaled conjugate gradient backpropagation is used for training the network. The results demonstrate that PLDM can sufficiently replicate camera detection performance in the simulation. The modelling approach improves the realism of camera sensor simulation as well as computational effort for X-in-the-loop applications and thereby supports safety validation of camera-based functionality in automated driving, which decreases the energy consumption of vehicles.

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Classification of sensor errors for the statistical simulation of environmental perception in automated driving systems

Cited by 14 publications

References 11 publications

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

Towards Serious Perception Sensor Simulation for Safety Validation of Automated Driving - A Collaborative Method to Specify Sensor Models

Phenomenological Modelling of Camera Performance for Road Marking Detection

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