A non-parametric approach for modeling sensor behavior

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

doi:10.1109/irs.2015.7226346

Cited by 35 publications

(29 citation statements)

References 3 publications

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“…A statistical study on radar measurements from vehicles in dependence on the aspect angle was conducted in [30]. In [31] and [32], kernel density estimation methods are employed to learn a probabilistic measurement model for simulation.…”

Section: Introductionmentioning

confidence: 99%

Tracking Multiple Vehicles Using a Variational Radar Model

Scheel

Dietmayer

2019

IEEE Trans. Intell. Transport. Syst.

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High-resolution radar sensors are able to resolve multiple detections per object and therefore provide valuable information for vehicle environment perception. For instance, multiple detections allow to infer the size of an object or to more precisely measure the object's motion. Yet, the increased amount of data raises the demands on tracking modules: measurement models that are able to process multiple detections for an object are necessary and measurement-to-object associations become more complex. This paper presents a new variational radar model for tracking vehicles using radar detections and demonstrates how this model can be incorporated into a Random-Finite-Setbased multi-object filter. The measurement model is learned from actual data using variational Gaussian mixtures and avoids excessive manual engineering. In combination with the multiobject tracker, the entire process chain from the raw measurements to the resulting tracks is formulated probabilistically. The presented approach is evaluated on experimental data and it is demonstrated that the data-driven measurement model outperforms a manually designed model.

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Section: Introductionmentioning

confidence: 99%

Tracking Multiple Vehicles Using a Variational Radar Model

Scheel

Dietmayer

2019

IEEE Trans. Intell. Transport. Syst.

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“…In non-parametric modeling this can be included due to the separation into relevance and contribution. This article will not go into further detail here, however the next section and Hirsenkorn et al (2015) will clarify this.…”

Section: Critical Discussion Of the Non-parametric Modelmentioning

confidence: 99%

“…A further motivation of virtual testing and sensor models for driving simulators is presented in Hanke et al (2015); Hirsenkorn et al (2015); Bernsteiner et al (2013Bernsteiner et al ( , 2015 and Schubert et al (2014).…”

Section: Introductionmentioning

confidence: 99%

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Virtual sensor models for real-time applications

et al. 2016

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Abstract. Increased complexity and severity of future driver assistance systems demand extensive testing and validation. As supplement to road tests, driving simulations offer various benefits. For driver assistance functions the perception of the sensors is crucial. Therefore, sensors also have to be modeled. In this contribution, a statistical data-driven sensormodel, is described. The state-space based method is capable of modeling various types behavior. In this contribution, the modeling of the position estimation of an automotive radar system, including autocorrelations, is presented. For rendering real-time capability, an efficient implementation is presented.

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“…To be able to simulate degraded sensors, physically based sensor models offer the flexibility to inject failures based on their respective causation. Ideal, probabilistic, or phenomenological models as in [5] or [6] have a disadvantage in generalization ability for unseen situations. All of them are built using a-prioriknowledge and possibilities based on collected data.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models

Rosenberger

Holder

Zirulnik

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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Safety validation tests of automated driving (AD) use simulated environments and perception sensor models. To achieve the level of fidelity needed for safety approval, such sensor simulations can be physically based. For formulating requirements for sensor models to be used in such test frameworks, the extent to which they must include physical effects should be determined. One approach is to clarify their relevance for following processing steps like object detection or mapping. But at first, an analysis is needed to determine, which effects are relevant and if they can be implemented at all. In this work, we focus on one lidar sensor and analyze its observable real world sensor behavior to derive the possible effects, physically based lidar sensor models can include. Consequently, we describe environmental parameters that could be considered to influence physically based lidar sensor models. By investigating the specifications given by the manufacturer with own measurements, we show that some of them should be implemented in a dynamic manner. In conclusion, we enable to formulate detailed requirements for sensor models, as their actual possible fidelity is presented.

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A non-parametric approach for modeling sensor behavior

Cited by 35 publications

References 3 publications

Tracking Multiple Vehicles Using a Variational Radar Model

Tracking Multiple Vehicles Using a Variational Radar Model

Virtual sensor models for real-time applications

Analysis of Real World Sensor Behavior for Rising Fidelity of Physically Based Lidar Sensor Models

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