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

Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard

doi:10.1109/ivs.2019.8813773

Cited by 34 publications

(23 citation statements)

References 15 publications

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“…The output is either multi-class (one score for each class) or binary. In the latter case, an ensemble voting (subsection III-C) step combines the result of several binary classifiers similarly to [24]. A class-specific clustering step (i.e.…”

Section: Proposed Methodsmentioning

confidence: 99%

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CNN Based Road User Detection Using the 3D Radar Cube

Palffy

Dong

Kooij

et al. 2020

IEEE Robot. Autom. Lett.

132

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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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Section: Proposed Methodsmentioning

confidence: 99%

“…We also implemented an ensemble voting system of binary classifiers (networks with two output nodes). That is, aside training a single, multi-class network, we followed [24] and trained One-vs-All (OvA) and One-vs-One (OvO) binary classifiers for each class (e.g. car-vs-all) and pair of classes (e.g.…”

Section: Ensemble Classifyingmentioning

confidence: 99%

CNN Based Road User Detection Using the 3D Radar Cube

Palffy

Dong

Kooij

et al. 2020

IEEE Robot. Autom. Lett.

132

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“…In recent years, more and more studies employ diversiform methods to enhance the results of object detection and classification based on MMW radar data [ 23 , 24 , 42 ]. Researchers chose to process radar data with neural networks or grid-mapping to obtain rich target perception information.…”

Section: Mmw Radar Perception Approachesmentioning

confidence: 99%

“…Some successful DL techniques are applied to radar data. Specific convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are proposed for radar data processing [ 23 , 24 ]. Furthermore, related studies use DNN to improve the fusion performance [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

MMW Radar-Based Technologies in Autonomous Driving: A Review

Zhou

Yang

Jiang

et al. 2020

Sensors

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With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.

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“…The objects of interest are indicated by the same color in the two images. algorithms as shown, e.g., in [3]- [7] and summarized in Fig. 2.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stage Clustering Framework for Automotive Radar Data

Scheiner

Appenrodt

Dickmann

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Self Cite

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Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather conditions often makes them superior to other sensors such as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of the commonly used detection data level is a major challenge for subsequent signal processing. Therefore, the data points are often merged in order to form larger entities from which more information can be gathered. The merging process is often implemented in form of a clustering algorithm. This article describes a novel approach for first filtering out static background data before applying a twostage clustering approach. The two-stage clustering follows the same paradigm as the idea for data association itself: First, clustering what is ought to belong together in a low dimensional parameter space, then, extracting additional features from the newly created clusters in order to perform a final clustering step. Parameters are optimized for filtering and both clustering steps. All techniques are assessed both individually and as a whole in order to demonstrate their effectiveness. Final results indicate clear benefits of the first two methods and also the cluster merging process under specific circumstances.

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

Cited by 34 publications

References 15 publications

CNN Based Road User Detection Using the 3D Radar Cube

CNN Based Road User Detection Using the 3D Radar Cube

MMW Radar-Based Technologies in Autonomous Driving: A Review

A Multi-Stage Clustering Framework for Automotive Radar Data

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