Highly-Scalable and Generalized Sensor Structures for Efficient Physically-Based Simulation of Multi-Modal Sensor Networks

Thieling, Jorn; Rosmann, Jurgen

doi:10.1109/icsenst.2018.8603563

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…Recently, ray tracing has become a key technology in this field that allows the simulation of different sensors, such as radars [26]- [28], LiDARs [29], cameras [30], and ultrasonic sensors [31]. A scalable, generic physically-based simulator using ray tracing is proposed in [32]. The commercial, automotive simulation tools, such as Microsoft AirSim [33], Vector DYNA4 [34], Hexagon VTD [35], dSPACE ASM [36], IPG CarMaker [37], and Siemens PreScan [38], besides the vehicle and traffic model, usually include a simplified simulation of specific sensor types using game engines, such as Unreal [39] or Unity [40].…”

Section: A Related Workmentioning

confidence: 99%

Object-Level Data-Driven Sensor Simulation for Automotive Environment Perception

Lindenmaier

Aradi

Bécsi

et al. 2023

IEEE Trans. Intell. Veh.

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The gradually evolving automated driving and ADAS functions require more enhanced environment perception.The key to reliable environmental perception is large amounts of data that are hard to collect. Several simulators provide realistic, raw sensor data based on physical sensor models. However, besides their high price, they also require very high computation capacity. Furthermore, most sensor suppliers provide high-level data, such as object detections, that is complicated to reproduce from simulated raw sensor data. This paper proposes a method that directly simulates the detections or object tracks provided by smart sensors. The model involves several uncertainties of the sensors, such as missed-, false detections, and measurement noise. In contrast to the conventional sensor models, this method tackles with state-dependent clutter model and considers the field of view in the detections model. The parameters of the proposed model are identified for an automotive smart radar and camera based on pre-evaluated real-world measurements. The resulting model provides synthetic object-level data with higher fidelity than the conventional probabilistic models, differing less than 2% from the precision and recall metrics of the actual sensors.

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Section: A Related Workmentioning

confidence: 99%

Object-Level Data-Driven Sensor Simulation for Automotive Environment Perception

Lindenmaier

Aradi

Bécsi

et al. 2023

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

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“…(a) real-time rastered (b) interactive rastered (c) ray tracing using [9] Fig. 6: Subset of the distilled static material model…”

Section: Fig 3: Two-dimensional Bounding Volume Hierarchymentioning

confidence: 99%

Towards Spatial Databases using Simulation State Transformations - Providing the Means for High Performance Simulation of Digital Twins in Three-Dimensional Scenarios

Alfrink

Roßmann

2020

Annals of Scientific Society for Assembly, Handling and Industrial Robotics

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The growing complexity of industrial plants, enormous advances in autonomous driving and an increasingly digitized world require a quickly rising amount of virtual planning and testing using threedimensional simulations. In addition to the number of technical advances, the complexity of individual objects and scenarios is increasing, pushing the requirements on simulation technology to generate sufficient realistic data within limited time frames. Simulation frameworks have their means to store the information necessary to represent the properties of all scenario entities -the simulation state -in their database. However, they lack simulation algorithmdependent transformations and augmentations of the simulation state that allow maximizing calculation efficiency. This paper introduces a generalized formalism to describe these transformations and illustrates how they help to bring the simulation framework closer to a specialized simulation database that stores spatial information with a focus on performance -the spatial database. Further, this paper demonstrates the advantages of the approach with the introduction of two concrete transformations. One targets the efficient representation of three-dimensional spatial relations for industrial robots while the other allows generating different levels of complexity in the definition of materials in the context of autonomous driving.

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Synthetic Data for Deep Learning

Nikolenko

2021

Springer Optimization and Its Applications

222

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Aims and ScopeOptimization has continued to expand in all directions at an astonishing rate. New algorithmic and theoretical techniques are continually developing and the diffusion into other disciplines is proceeding at a rapid pace, with a spot light on machine learning, artificial intelligence, and quantum computing. Our knowledge of all aspects of the field has grown even more profound. At the same time, one of the most striking trends in optimization is the constantly increasing emphasis on the interdisciplinary nature of the field. Optimization has been a basic tool in areas not limited to applied mathematics, engineering, medicine, economics, computer science, operations research, and other sciences.The series Springer Optimization and Its Applications (SOIA) aims to publish state-of-the-art expository works (monographs, contributed volumes, textbooks, handbooks) that focus on theory, methods, and applications of optimization. Topics covered include, but are not limited to, nonlinear optimization, combinatorial optimization, continuous optimization, stochastic optimization, Bayesian optimization, optimal control, discrete optimization, multi-objective optimization, and more. New to the series portfolio include Works at the intersection of optimization and machine learning, artificial intelligence, and quantum computing.Volumes from this series are indexed by Web of Science, zbMATH, Mathematical Reviews, and SCOPUS.

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Highly-Scalable and Generalized Sensor Structures for Efficient Physically-Based Simulation of Multi-Modal Sensor Networks

Cited by 10 publications

References 8 publications

Object-Level Data-Driven Sensor Simulation for Automotive Environment Perception

Object-Level Data-Driven Sensor Simulation for Automotive Environment Perception

Towards Spatial Databases using Simulation State Transformations - Providing the Means for High Performance Simulation of Digital Twins in Three-Dimensional Scenarios

Synthetic Data for Deep Learning

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