MiRE, A Mixed Reality Environment for Testing of Automated Driving Functions

Drechsler, Maikol Funk; Peintner, Jakob; Reway, Fabio; Seifert, Georg; Riener, Andreas; Huber, Werner

doi:10.1109/tvt.2022.3160353

Cited by 16 publications

(3 citation statements)

References 41 publications

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“…Machine Learning, Data-Driven models and Physics based models) with different levels of fidelity [14]- [17]. These models are key for X-i-L (anything-in-the-loop) and simulation based testing aware of sensor data nonideality [18]. Amongst the above mentioned models, key to enable automated driving in snowy conditions is to develop and validate LiDAR snow models, e.g.…”

Section: A Lidar Data Degradation and Snowmentioning

confidence: 99%

LIDAR De-Snow Score (DSS): combining quality and perception metrics for optimised data filtering

Chan,

Gummadi,

Raisuddin

et al. 2024

Preprint

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Section: A Lidar Data Degradation and Snowmentioning

confidence: 99%

LIDAR De-Snow Score (DSS): combining quality and perception metrics for optimised data filtering

Chan,

Gummadi,

Raisuddin

et al. 2024

Preprint

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“…Entire vehicles with all their subsystems can be tested on roller benches challenged by using virtual environment-based sensor spoofing or sensor simulation [29][30][31]. Mixed reality testing solutions with real-time connected but separated tested and target objects in space were also developed [32,33]. Digital twin technologies have started to be more common, and already, the classical proving ground-based testing approaches consider their usage [34].…”

Section: Introductionmentioning

confidence: 99%

Development and Functional Validation Method of the Scenario-in-the-Loop Simulation Control Model Using Co-Simulation Techniques

Toth,

Szalay

2023

Machines

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With the facilitated development of highly automated driving functions and automated vehicles, the need for advanced testing techniques also arose. With a near-infinite number of potential traffic scenarios, vehicles have to drive an increased number of test kilometers during development, which would be very difficult to achieve with currently utilized conventional testing methods. State-of-the-Art testing technologies such as Vehicle-in-the-Loop (ViL) or Scenario-in-the-Loop (SciL) can provide a long-term solution; however, validation of these complex systems should also be addressed. ViL and SciL technologies provide real-time control and measurement with multiple participants; however, they require enormous computational capacity and low-latency communication to provide comparable results with real-world testing. 5G (fifth-generation wireless) communication and Edge computing can aid in fulfilling these needs, although appropriate implementation should also be tested. In the current paper, a realized control model based on the SciL architecture was presented that was developed with real-world testing data and validated utilizing co-simulation and digital twin techniques. The model was established in Simcenter Prescan© connected to MATLAB Simulink® and validated using IPG CarMaker®, which was used to feed the simulation with the necessary input data to replace the real-world testing data. The aim of the current paper was to introduce steps of the development process, to present the results of the validation procedure, and to provide an outlook of potential future implementations into the state of the art in proving ground ecosystems.

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“…기존 자율주행 차량은 정형화된 도로 환경에서 주 행됨에 따라 일반적으로 카메라를 통해 측정된 영상 을 주로 사용하여 환경을 인지한다. 그에 따라 대부분 혼합 현실 기반의 증강 연구가 영상에 중점을 두고 있다 [7][8][9][10][11] 있다 [12] . 지역적으로 변화를 주는 방식은 일반적으로 특정 객체 영역에 대해 이루어진다.…”

unclassified

Real-virtual Point Cloud Augmentation Method for Test and Evaluation of Autonomous Weapon Systems

Yeo,

Hwang,

Tae

2024

J. KIMS Technol

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Autonomous weapon systems act according to artificial intelligence-based judgement based on recognition through various sensors. Test and evaluation for various scenarios is required depending on the characteristics that artificial intelligence-based judgement is made. As a part of this approach, this paper proposed a LiDAR point cloud augmentation method for mixed-reality based test and evaluation. The augmentation process is achieved by mixing real and virtual LiDAR signals based on the virtual LiDAR synchronized with the pose of the autonomous weapon system. For realistic augmentation of test and evaluation purposes, appropriate intensity values were inserted when generating a point cloud of a virtual object and its validity was verified. In addition, when mixing the generated point cloud of the virtual object with the real point cloud, the proposed method enhances realism by considering the occlusion phenomenon caused by the insertion of the virtual object.

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MiRE, A Mixed Reality Environment for Testing of Automated Driving Functions

Cited by 16 publications

References 41 publications

LIDAR De-Snow Score (DSS): combining quality and perception metrics for optimised data filtering

LIDAR De-Snow Score (DSS): combining quality and perception metrics for optimised data filtering

Development and Functional Validation Method of the Scenario-in-the-Loop Simulation Control Model Using Co-Simulation Techniques

Real-virtual Point Cloud Augmentation Method for Test and Evaluation of Autonomous Weapon Systems

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