State-of-the-Art Sensor Models for Virtual Testing of Advanced Driver
                    Assistance Systems/Autonomous Driving Functions

Schlager, Birgit; Muckenhuber, Stefan; Schmidt, Simon; Holzer, Hannes; Rott, Relindis; Maier, Franz Michael; Saad, Kmeid; Kirchengast, Martin; Stettinger, Georg; Watzenig, Daniel; Ruebsam, Jonas

doi:10.4271/12-03-03-0018

Cited by 51 publications

(37 citation statements)

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“…Virtual Vehicle Research GmbH (VIF) is a research organization that is actively working on all areas of model-based vehicle development, particularly including automated driving system solutions. Of special interest is the development of tools and methodologies that can aid in the scenario based validation and verification of ADAS systems at various abstraction levels spanning simulation-only and real-life testing [ 33 , 34 ]. With this motivation and background, VIF joined the M86 measurement campaign with one of its generic Automated Drive Demonstrator (ADD) vehicles.…”

Section: Measurement Campaign Realizationmentioning

confidence: 99%

Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

Tihanyi

Tettamanti

Csonthó

et al. 2021

Sensors

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A spectacular measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners. The measurement resulted in vehicle based and infrastructure based sensor data that will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles—equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization—carried out special test scenarios while collecting detailed data using different sensors. All of the test runs were recorded by both vehicles and infrastructure. The paper also showcases application examples to demonstrate the viability of the collected data having access to the ground truth labeling. This data set may support a large variety of solutions, for the test and validation of different kinds of approaches and techniques. As a complementary task, the available 5G network was monitored and tested under different radio conditions to investigate the latency results for different measurement scenarios. A part of the measured data has been shared openly, such that interested automotive and academic parties may use it for their own purposes.

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Section: Measurement Campaign Realizationmentioning

confidence: 99%

Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

Tihanyi

Tettamanti

Csonthó

et al. 2021

Sensors

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“…Previous work on automotive radar modeling includes both generic, i.e., applicable for any perception sensor, and radar specific approaches (Schlager et al, 2020).…”

Section: Previous Work On Automotive Radar Modelingmentioning

confidence: 99%

Performance evaluation of a state-of-the-art automotive radar and corresponding modeling approaches based on a large labeled dataset

Muckenhuber

Museljic

Stettinger

2021

Journal of Intelligent Transportation Systems

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Radar is a key sensor to achieve a reliable environment perception for advanced driver assistance system and automated driving (ADAS/AD) functions. Reducing the development efforts for ADAS functions and eventually enabling AD functions demands the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, the physical radar unit is replaced by a virtual radar model. Driving datasets, such as the nuScenes dataset, containing large amounts of annotated sensor measurements, help understand sensor capabilities and play an important role in sensor modeling. This article includes a thorough analysis of the radar data available in the nuScenes dataset. Radar properties, such as detection thresholds, and detection probabilities depending on object, environment, and radar parameters, as well as object properties, such as reflection behavior depending on object type, are investigated quantitatively. The overall detection probability of the considered radar (Continental ARS-408-21) was found to be 27.81%. Four radar models on object level with different complexity levels and different parametrisation requirements are presented: a simple RCS-based radar model with an accuracy of 51%, a linear SVC model with an accuracy of 70%, a Random Forest model with an accuracy of 83%, and a Gradient Boost model with an accuracy of 86%. The feature importance analysis of the machine learning algorithms revealed that object class, object size, and object visibility are the most important parameters for the presented radar models. In contrast, daytime and weather conditions seem to have only minor influence on the modeling results.

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“…In literature, lots of different models and model types are available. Schlager et al [2] provide an overview of the state of the art of perception sensor models. In a first approach they try to group and compare various models, but the diversity in existing models poses a challenge.…”

Section: State Of the Artmentioning

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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State-of-the-Art Sensor Models for Virtual Testing of Advanced Driver Assistance Systems/Autonomous Driving Functions

Cited by 51 publications

References 0 publications

Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

Performance evaluation of a state-of-the-art automotive radar and corresponding modeling approaches based on a large labeled dataset

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

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