Highly Parameterizable and Generic Perception Sensor Model Architecture

Linnhoff, Clemens; Rosenberger, Philipp; Holder, Martin; Cianciaruso, Nicodemo; Winner, Hermann

doi:10.1007/978-3-658-34752-9_16

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…To support X-in-the-loop testing methods during the vehicle development process, a wide range of commercial or open-source simulation software is available to the automotive industry. Referring to our previous work [ 2 ], some examples are given: in [ 5 ]: TASS-PreScan, dSpace-ASM, in [ 3 ]: TESIS Dyna4-Driver Assistance, MathWorks-ADAS Toolbox, in [ 6 ]: CARLA, AirSim, DeepDrive, Udacity, or in [ 7 ]: CarMaker from IPG Automotive GmbH., VIRES-VTD. These software packages provide a variety of interfaces for modelling perception sensors at different levels of complexity, but their parallel use is often limited, whereas in real application data, the fusion of multiple sensors is state of the art.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Simulation of Automotive Radar Sensors Designed for Systematic Support of Vehicle Development

Magosi

Eichberger

2023

Sensors

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Despite the progress in driving automation, the market introduction of higher-level automation has not yet been achieved. One of the main reasons for this is the effort in safety validation to prove functional safety to the customer. However, virtual testing may compromise this challenge, but the modelling of machine perception and proving its validity has not been solved completely. The present research focuses on a novel modelling approach for automotive radar sensors. Due to the complex high-frequency physics of radars, sensor models for vehicle development are challenging. The presented approach employs a semi-physical modelling approach based on experiments. The selected commercial automotive radar was applied in on-road tests where the ground truth was recorded with a precise measurement system installed in ego and target vehicles. High-frequency phenomena were observed and reproduced in the model on the one hand by using physically based equations such as antenna characteristics and the radar equation. On the other hand, high-frequency effects were statistically modelled using adequate error models derived from the measurements. The model was evaluated with performance metrics developed in previous works and compared to a commercial radar sensor model. Results show that, while keeping real-time performance necessary for X-in-the-loop applications, the model is able to achieve a remarkable fidelity as assessed by probability density functions of the radar point clouds and using the Jensen–Shannon divergence. The model delivers values of radar cross-section for the radar point clouds that correlate well with measurements comparable with the Euro NCAP Global Vehicle Target Validation process. The model outperforms a comparable commercial sensor model.

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Section: Related Workmentioning

confidence: 99%

A Novel Approach for Simulation of Automotive Radar Sensors Designed for Systematic Support of Vehicle Development

Magosi

Eichberger

2023

Sensors

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“…A modular approach for simulation based safety validation of sensors is presented in [15]. In their work, Linnhoff et al, concentrates on simulation as part of virtual testing, especially on the compatibility between developed models and the Open Simulation Interface [16].…”

Section: Related Workmentioning

confidence: 99%

SafeSens - Uncertainty Quantification of Complex Perception Systems

Kurzidem,

Burton,

Schleiss

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Safety testing and validation of complex autonomous systems requires a comprehensive and reliable analysis of performance and uncertainty. Especially uncertainty quantification plays a vital part in perception systems operating in open context environments that are neither foreseeable nor deterministic. Therefore, safety assurance based on field tests or corner cases alone is not a feasible option as effort and potential risks are high. Simulations offer a way out. They allow, for example, simulation of potentially hazardous situations, without any real danger, by systematically computing a variety of different (input) parameters quickly. In order to do so, simulations need accurate models to represent the complex system and in particular include uncertainty as inherent property to accurately reflect the interdependence between system components and the environment. We present an approach to creating perception architectures via suitable meta-models to enable a holistic safety analysis to quantify the uncertainties within the system. The models include aleatoric or epistemic uncertainty, dependent on the nature of the approximated component. A showcase of the proposed method highlights, how validation under uncertainty can be used for a camera-based object detection.

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“…These models are often referred to as idealised or geometric models and provide object lists as output data. As examples, we mention some products that are widely used in the automotive industry: in [ 62 ], TwT GmbH, TASS-PreScan, dSpace-ASM; in [ 57 ], TESIS Dyna4-Driver Assistance, MathWorks-ADAS Toolbox; in [ 63 ], CARLA, AirSim, DeepDrive, Udacity, Constellation, Helios, GLIDAR, RADSim, SIMSonic; and in [ 64 ], CarMaker from IPG Automotive GmbH, VIRES-VTD, CARLA, and AirSim can provide GT information.…”

Section: Classification From the System Integrator’s Perspectivementioning

confidence: 99%

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving

Magosi

Rosenberger

et al. 2022

Sensors

Self Cite

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Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated.

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Highly Parameterizable and Generic Perception Sensor Model Architecture

Cited by 7 publications

References 16 publications

A Novel Approach for Simulation of Automotive Radar Sensors Designed for Systematic Support of Vehicle Development

A Novel Approach for Simulation of Automotive Radar Sensors Designed for Systematic Support of Vehicle Development

SafeSens - Uncertainty Quantification of Complex Perception Systems

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving

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