Template matching for radar-based orientation and position estimation in automotive scenarios

Schlichenmaier, Johannes; Selvaraj, Niranjan; Stolz, Martin; Waldschmidt, Christian

doi:10.1109/icmim.2017.7918865

Cited by 23 publications

(14 citation statements)

References 3 publications

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“…Several of such approaches have been proposed for radar-based tracking. These include clustering and extraction of reference points as in [1], [2], or fitting bounding boxes and L-shapes [3], [4], reflection center models [5], or velocity profiles [6]- [8] to the data. While preprocessing routines are oftentimes effective, computationally fast, and lead to clearly separable system architectures, they face difficulties if the data from a single time step is ambiguous and the correct meta-measurement cannot be easily extracted.…”

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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“…For this reason, position and dimension of vehicles are estimated using a variant of the k-nearest-neighbors method. Furthermore, Schlichenmaier et al [13] present an algorithm to estimate bounding boxes representing vehicles using template matching. Especially in challenging scenarios, the templating matching algorithm outperforms the orientated bounding box methods.…”

Section: Related Workmentioning

confidence: 99%

2D Car Detection in Radar Data with PointNets

Danzer

Griebel

Bach

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

123

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For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to detect 2D objects solely depending on sparse radar data using PointNets. In literature, only methods are presented so far which perform either object classification or bounding box estimation for objects. In contrast, this method facilitates a classification together with a bounding box estimation of objects using a single radar sensor. To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box. The algorithm is evaluated using an automatically created dataset which consist of various realistic driving maneuvers. The results show the great potential of object detection in high-resolution radar data using PointNets.

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“…While there is a vast variety of concepts for the generation of radar targets with variable distances and velocities [5]- [8], there are only a few concepts for the generation of targets with a specific direction of arrival (DoA). The DoA of targets allows radar systems to estimate the position and orientation of other road users [9] or obstacles [10] and must be simulated as well for a realistic simulation. A test environment that is able to generate multiple targets with different DoAs for an automotive radar sensor was presented in [11] and [12].…”

Section: Introductionmentioning

confidence: 99%

Flexible Direction-of-Arrival Simulation for Automotive Radar Target Simulators

Schoeder¹,

Janoudi²,

Meinecke³

et al. 2021

IEEE J. Microw.

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In order to simulate realistic traffic scenarios, a radar target simulator must be able to generate multiple radar targets with different directions of arrival. The presented concept is able to generate an arbitrary amount of targets with individual directions of arrival for the radar under test. By measuring the radar channel, the novel approach enables target simulators to simulate arbitrary directions of arrival, while minimizing the required hardware. The optimum setup is derived for radars with a uniform linear receive antenna array. The compensation of placement errors for automotive chirp-sequence frequency modulated continuous wave radars is demonstrated. Finally, the calibration for the setup is provided, and the performance of the presented approach is validated.INDEX TERMS Automotive radar, chirp-sequence modulation, direction of arrival, FMCW radar, radar target simulator.This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

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Template matching for radar-based orientation and position estimation in automotive scenarios

Cited by 23 publications

References 3 publications

Tracking Multiple Vehicles Using a Variational Radar Model

Tracking Multiple Vehicles Using a Variational Radar Model

2D Car Detection in Radar Data with PointNets

Flexible Direction-of-Arrival Simulation for Automotive Radar Target Simulators

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