Semantic Segmentation on Radar Point Clouds

Schumann, Ole; Hahn, Markus; Dickmann, Jürgen; Wöhler, Christian

doi:10.23919/icif.2018.8455344

Cited by 146 publications

(87 citation statements)

References 13 publications

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“…A recent trend in machine learning is the incorporation of preprocessing and classification steps in a single convolutional neural network (CNN). This has also been done for automotive radar classification, e.g., [8] or [9]. Due to the aforementioned reasons, the LSTM approach is preferred, here.…”

Section: Introductionmentioning

confidence: 99%

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Scheiner

Appenrodt

Dickmann

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a one-vs-one binarization paradigm are enriched by one-vs-all correction classifiers. They are utilized to efficiently classify individual traffic participants and also identify hidden object classes which have not been presented to the classifiers during training. For each classifier of the ensemble an individual feature set is determined from a total set of 98 features. Thereby, the overall classification performance can be improved when compared to previous methods and, additionally, novel classes can be identified much more accurately. Furthermore, the proposed structure allows to give new insights in the importance of features for the recognition of individual classes which is crucial for the development of new algorithms and sensor requirements.

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Section: Introductionmentioning

confidence: 99%

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Scheiner

Appenrodt

Dickmann

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

View full text Add to dashboard Cite

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“…While clustering based methods are widely used, it is often noted (e.g. [11], [17]) that the clustering step is errorprone. Objects can be mistakenly merged (Fig.…”

Section: Related Workmentioning

confidence: 99%

“…However, the output of a single radar sweep is too sparse. To overcome this, they used multiple frames [11] or multiple radar sensors [20].…”

Section: Related Workmentioning

confidence: 99%

CNN Based Road User Detection Using the 3D Radar Cube

Palffy

Dong

Kooij

et al. 2020

IEEE Robot. Autom. Lett.

124

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This paper presents a novel radar based, singleframe, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data. The method provides class information both on the radar targetand object-level. Radar targets are classified individually after extending the target features with a cropped block of the 3D radar cube around their positions, thereby capturing the motion of moving parts in the local velocity distribution. A Convolutional Neural Network (CNN) is proposed for this classification step. Afterwards, object proposals are generated with a clustering step, which not only considers the radar targets' positions and velocities, but their calculated class scores as well. In experiments on a real-life dataset we demonstrate that our method outperforms the state-of-the-art methods both target-and objectwise by reaching an average of 0.70 (baseline: 0.68) target-wise and 0.56 (baseline: 0.48) object-wise F1 score. Furthermore, we examine the importance of the used features in an ablation study.

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“…For this reason, Qi et al [2] also propose a hierarchical neural network, called PointNet++, which applies PointNet recursively on small regions of the input point set. Schumann et al [14] use the same PointNet++ architecture for semantic segmentation on radar point clouds. For this purpose, the architecture is modified to handle point clouds with two spatial and two further feature dimensions.…”

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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Semantic Segmentation on Radar Point Clouds

Cited by 146 publications

References 13 publications

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

CNN Based Road User Detection Using the 3D Radar Cube

2D Car Detection in Radar Data with PointNets

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