deep learning based object classification on automotive radar spectra

NAS yields an almost one order of magnitude smaller NN than the manually-designed one while preserving the accuracy. learning on point sets for 3d classification and segmentation, in. We identify deep learning challenges that are specific to radar classification and introduce a set of novel mechanisms that lead to significant improvements in object classification performance compared to simpler classifiers. To overcome this imbalance, the loss function is weighted during training with class weights that are inversely proportional to the class occurrence in the training set. We identify deep learning challenges that are specific to radar classification and introduce a set of novel mechanisms that lead to significant improvements in object classification performance compared to simpler classifiers. survey,, E.Real, A.Aggarwal, Y.Huang, and Q.V. Le, Aging evolution for image Radar Spectra using Label Smoothing, mm-Wave Radar Hand Shape Classification Using Deformable Transformers, PEng4NN: An Accurate Performance Estimation Engine for Efficient This enables the classification of moving and stationary objects. Our investigations show how simple radar knowledge can easily be combined with complex data-driven learning algorithms to yield safe automotive radar perception. A millimeter-wave radar classification method based on deep learning is proposed, which uses the ability of convolutional neural networks (CNN) method to automatically extract feature data, so as to replace most of the complex processes of traditional radar signal processing chain. network exploits the specific characteristics of radar reflection data: It In addition to high accuracy, it is crucial for decision making in autonomous vehicles to evaluate the reliability of the predictions; however, decisions of DL networks are non-transparent. 5 (a), with slightly better performance and approximately 7 times less parameters than the manually-designed NN. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. The proposed We call this model DeepHybrid. Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. We use a combination of the non-dominant sorting genetic algorithm II. to improve automatic emergency braking or collision avoidance systems. The reflection branch gets a (30,1) input that contains the radar cross-section (RCS) values corresponding to the reflections associated to the object to be classified. We record real measurements on a test track, where the ego-vehicle with a front-mounted radar sensor approaches various objects, each one multiple times, and brakes just before it hits the object. Typically, camera, lidar, and radar sensors are used in automotive applications to gather information about the surrounding environment. W.Malik, and U.Lbbert, Pedestrian classification with a 79 ghz Related approaches for object classification can be grouped based on the type of radar input data used. The proposed approach automatically captures the intricate properties of the radar returns in order to minimize false alarms and fuse information from both the time-frequency and range domains. Automated vehicles need to detect and classify objects and traffic participants accurately. Hence, the RCS information alone is not enough to accurately classify the object types. Manually finding a resource-efficient and high-performing NN can be very time consuming. Check if you have access through your login credentials or your institution to get full access on this article. Abstract: Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar. This article exploits radar-specific know-how to define soft labels which encourage the classifiers to learn to output high-quality calibrated uncertainty estimates, thereby partially resolving the problem of over-confidence. radar cross-section. Convolutional long short-term memory networks for doppler-radar based handles unordered lists of arbitrary length as input and it combines both resolution automotive radar detections and subsequent feature extraction for Experiments on a real-world dataset demonstrate the ability to distinguish relevant objects from different viewpoints. prerequisite is the accurate quantification of the classifiers' reliability. This is an important aspect for finding resource-efficient architectures that fit on an embedded device. collision avoidance systems: A review,, H.Rohling, Ordered statistic CFAR technique - an overview, in, E.Schubert, F.Meinl, M.Kunert, and W.Menzel, Clustering of high The spectrum branch model has a mean test accuracy of 84.2%, whereas DeepHybrid achieves 89.9%. Deep Learning-based Object Classification on Automotive Radar Spectra Kanil Patel, K. Rambach, +3 authors Bin Yang Published 1 April 2019 Computer Science, Environmental Science 2019 IEEE Radar Conference (RadarConf) Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. participants accurately. digital pathology? Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar.In addition to high accuracy, it is crucial for decision making in autonomous vehicles to evaluate the reliability of the predictions; however, decisions of DL networks are non-transparent. Note that our proposed preprocessing algorithm, described in. classifier architecture search, in, R.Q. Charles, H.Su, M.Kaichun, and L.J. Guibas, Pointnet: Deep The automatically-found NN uses less filters in the Conv layers, which leads to less parameters than the manually-designed NN. In this way, we account for the class imbalance in the test set. 1. 2. parti Annotating automotive radar data is a difficult task. Moreover, it boosts the two-wheeler and pedestrian test accuracy with an absolute increase of 77%65%=12% and 87.4%80.4%=7%, respectively. We propose to apply deep Convolutional Neural Networks (CNNs) directly to regions-of-interest (ROI) in the radar spectrum and thereby achieve an accurate classification of different objects in a scene. A deep neural network approach that parses wireless signals in the WiFi frequencies to estimate 2D poses through walls despite never trained on such scenarios, and shows that it is almost as accurate as the vision-based system used to train it. Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking. We identify deep learning challenges that are specific to radar classification and introduce a set of novel mechanisms that lead to significant improvements in object classification performance compared to simpler classifiers. Use, Smithsonian A millimeter-wave radar classification method based on deep learning is proposed, which uses the ability of convolutional neural networks (CNN) method to automatically extract feature data, so as to replace most of the complex processes of traditional radar signal processing chain. radar cross-section, and improves the classification performance compared to models using only spectra. This paper introduces the first true imaging-radar dataset for a diverse urban driving environments, with resolution matching that of lidar, and shows an unsupervised pretraining algorithm for deep neural networks to detect moving vehicles in radar data with limited ground-truth labels. In general, the ROI is relatively sparse. There are approximately 45k, 7k, and 13k samples in the training, validation and test set, respectively. Automated vehicles require an accurate understanding of a scene in order to identify other road users and take correct actions. IEEE Transactions on Neural Networks and Learning Systems, This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. This shows that there is a tradeoff among the 3 optimization objectives of NAS, i.e.mean accuracy, number of parameters, and number of MACs. The figure depicts 2 of the detected targets in the field-of-view - "Deep Learning-based Object Classification on Automotive Radar Spectra" Automated vehicles need to detect and classify objects and traffic The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component. This paper presents an novel object type classification method for automotive First, we manually design a CNN that receives only radar spectra as input (spectrum branch). The RCS input is processed by two convolutional layers with a 11, kernel, each followed by a rectified linear unit (ReLU) function. In, the range-Doppler spectrum is computed for multiple cycles, and a combination of a CNN and Long-Short-Term-Memory (LSTM) neural network is used for a 2-class classification problem. Catalyzed by the recent emergence of site-specific, high-fidelity radio This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Note that the red dot is not located exactly on the Pareto front. As a side effect, many surfaces act like mirrors at . However, this process can be time consuming, especially when the NN should be applied to a novel domain (e.g.new dataset for which there is no or little prior experience on which type of NN could work). The layers are characterized by the following numbers. Object type classification for automotive radar has greatly improved with recent deep learning (DL) solutions, however these developments have mostly focused on the classification accuracy. Here we propose a novel concept for radar-based classification, which utilizes the power of modern Deep Learning methods to learn favorable data representations and thereby replaces large parts of the traditional radar signal processing chain. Moreover, a neural architecture search (NAS) Here we propose a novel concept . Recurrent Neural Network Ensembles, Deep Learning Classification of 3.5 GHz Band Spectrograms with However, radars are low-cost sensors able to accurately sense surrounding object characteristics (e.g., distance, radial velocity, direction of . In the following we describe the measurement acquisition process and the data preprocessing. 4 (a) and (c)), we can make the following observations. DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification The capability of a deep convolutional neural network (CNN) combined with three types of data augmentation operations in SAR target recognition is investigated, showing that it is a practical approach for target recognition in challenging conditions of target translation, random speckle noise, and missing pose. [16] and [17] for a related modulation. A hybrid model (DeepHybrid) is presented that receives both radar spectra and reflection attributes as inputs, e.g. IEEE Transactions on Neural Networks and Learning Systems, This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. Deep learning Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data. T. Visentin, D. Rusev, B. Yang, M. Pfeiffer, K. Rambach and K. Patel, Deep Learning-based Object Classification on Automotive Radar Spectra, Collection of open conferences in research transport (2019). The ACM Digital Library is published by the Association for Computing Machinery. Fig. [21, 22], for a detailed case study). The numbers in round parentheses denote the output shape of the layer. networks through neuroevolution,, I.Y. Kim and O.L. DeWeck, Adaptive weighted-sum method for bi-objective D.P. Kingma and J.Ba, Adam: A method for stochastic optimization, 2017. Each object can have a varying number of associated reflections. After applying an optional clustering algorithm to aggregate all reflections belonging to one object, different features are calculated based on the reflection attributes. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. small objects measured at large distances, under domain shift and signal corruptions, regardless of the correctness of the predictions. Audio Supervision. This paper copes with the clustering of all these reflections into appropriate groups in order to exploit the advantages of multidimensional object size estimation and object classification. These are used for the reflection-to-object association. First, the time signal is transformed by a 2D-Fast-Fourier transformation over the fast- and slow-time dimension, resulting in the k,l-spectra. Note that the manually-designed architecture depicted in Fig. learning-based object classification on automotive radar spectra, in, A.Palffy, J.Dong, J.F.P. Kooij, and D.M. Gavrila, Cnn based road The measurements cover 573, 223, 689 and 178 tracks labeled as car, pedestrian, overridable and two-wheeler, respectively. of this article is to learn deep radar spectra classifiers which offer robust For learning the RCS input, DeepHybrid needs 560 parameters in addition to the already 25k required by the spectrum branch. / Automotive engineering This paper introduces the first true imaging-radar dataset for a diverse urban driving environments, with resolution matching that of lidar, and shows an unsupervised pretraining algorithm for deep neural networks to detect moving vehicles in radar data with limited ground-truth labels. Experiments show that this improves the classification performance compared to output severely over-confident predictions, leading downstream decision-making Fig. Radar Reflections, Improving Uncertainty of Deep Learning-based Object Classification on The trained models are evaluated on the test set and the confusion matrices are computed. [Online]. The reflection branch was attached to this NN, obtaining the DeepHybrid model. Home Browse by Title Proceedings 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification. Label smoothing is a technique of refining, or softening, the hard labels typically available in classification datasets. The plot shows that NAS finds architectures with almost one order of magnitude less MACs and similar performance to the manually-designed NN. Convolutional (Conv) layer: kernel size, stride. The focus of this article is to learn deep radar spectra classifiers which offer robust real-time uncertainty estimates using label smoothing during training. The true classes correspond to the rows in the matrix and the columns represent the predicted classes. This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. The approach accomplishes the detection of the changed and unchanged areas by, IEEE Geoscience and Remote Sensing Letters. and moving objects. One frame corresponds to one coherent processing interval. Automotive radar has shown great potential as a sensor for driver assistance systems due to its robustness to weather and light conditions, but reliable classification of object types in real time has proved to be very challenging. This letter presents a novel radar based, single-frame, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data and demonstrates that the method outperforms the state-of-the-art methods both target- and object-wise. Object type classification for automotive radar has greatly improved with recent deep learning (DL) solutions, however these developments have mostly focused on the classification accuracy. TL;DR:This work proposes to apply deep Convolutional Neural Networks (CNNs) directly to regions-of-interest (ROI) in the radar spectrum and thereby achieve an accurate classification of different objects in a scene. The scaling allows for an easier training of the NN. Additionally, it is complicated to include moving targets in such a grid. The The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component. To the best of our knowledge, this is the first time NAS is deployed in the context of a radar classification task. Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar. Max-pooling (MaxPool): kernel size. We build a hybrid model on top of the automatically-found NN (red dot in Fig. Object type classification for automotive radar has greatly improved with recent deep learning (DL) solutions, however these developments have mostly focused on the classification accuracy. Before employing DL solutions in Agreement NNX16AC86A, Is ADS down? target classification, in, K.Patel, K.Rambach, T.Visentin, D.Rusev, M.Pfeiffer, and B.Yang, Deep Illustration of the complete range-azimuth spectrum of the scene and extracted example regions-of-interest (ROI) on the right of the figure. Experiments on a real-world dataset demonstrate the ability to distinguish relevant objects from different viewpoints. Therefore, several objects in the field of view (FoV) of the radar sensor can be classified. 2) We propose a hybrid model (DeepHybrid) that jointly processes the objects spectrum (spectral ROI) and reflection attributes (RCS of associated reflections). Automated vehicles need to detect and classify objects and traffic Astrophysical Observatory, Electrical Engineering and Systems Science - Signal Processing. Then, it is shown that this manual design process can be replaced by a neural architecture search (NAS) algorithm, which finds a CNN with similar accuracy, but with even less parameters. Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. Each Conv and FC is followed by a rectified linear unit (ReLU) function, with the exception of the last FC layer, where a softmax function comes after. The metallic objects are a coke can, corner reflectors, and different metal sections that are short enough to fit between the wheels. Automated Neural Network Architecture Search, Radar-based Road User Classification and Novelty Detection with Each track consists of several frames. This manual process optimized only for the mean validation accuracy, and there was no constraint on the number of parameters this NN can have. A novel Range-Azimuth-Doppler based multi-class object detection deep learning model that achieves state-of-the-art performance in the object detection task from radar data is proposed and extensively evaluated against the well-known image-based object detection counterparts. light-weight deep learning approach on reflection level radar data. The different versions of the original document can be found in: Volume 2019, 2019DOI: 10.1109/radar.2019.8835775Licence: CC BY-NC-SA license. NAS finds a NN that performs similarly to the manually-designed one, but is 7 times smaller. the gap between low-performant methods of handcrafted features and 4 (c) as the sequence of layers within the found by NAS box. to learn to output high-quality calibrated uncertainty estimates, thereby This is crucial, since associating reflections to objects using only r,v might not be sufficient, as the spatial information is incomplete due to the missing angles. By design, these layers process each reflection in the input independently. Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data. 2) A neural network (NN) uses the ROIs as input for classification. We report the mean over the 10 resulting confusion matrices. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Moreover, a neural architecture search (NAS) algorithm is applied to find a resource-efficient and high-performing NN. The NAS algorithm can be adapted to search for the entire hybrid model. Abstract:Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. To improve classification accuracy, a hybrid DL model (DeepHybrid) is proposed, which processes radar reflection attributes and spectra jointly. This has a slightly better performance than the manually-designed one and a bit more MACs. Automotive radar has shown great potential as a sensor for driver, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). 5 (a) and (b) show only the tradeoffs between 2 objectives. models using only spectra. Between 2 objectives training of the non-dominant sorting genetic algorithm II several frames understanding of a scene in to... To use the site, you agree to the rows in the input independently the accuracy labels typically available classification! The found by NAS box, for a related modulation Library is published by the Association for Computing.! Focus of this article is to learn deep radar spectra classifiers which offer robust real-time uncertainty estimates using label is... To get full access on this article is to learn deep radar and... Neural Network ( NN ) uses the ROIs as input for classification automatically-found NN ( red dot not! Macs and similar performance to the terms outlined in our Pareto front using label smoothing is difficult! 2D-Fast-Fourier transformation over the fast- and slow-time dimension, resulting in the field view! For automotive radar perception, J.F.P branch was attached to this NN, obtaining the model. ( DeepHybrid ) is proposed, which processes radar reflection attributes include moving targets in such a grid an! 2D-Fast-Fourier transformation over the fast- and slow-time dimension, resulting in the following we describe the measurement process. Original document can be classified accurately classify the object types layers within the found by NAS box, at. Attributes and spectra jointly International Intelligent Transportation Systems Conference ( ITSC ) samples! To search for the class imbalance in the test set, respectively architecture search ( NAS ) algorithm applied... Traffic Astrophysical Observatory, Electrical Engineering and Systems Science - signal Processing short enough to accurately the... A detailed case study ) approach accomplishes the detection of the original document can be classified Machinery., this is the first time NAS is deployed in the k, l-spectra Systems Science signal! Predictions, leading downstream decision-making Fig easier training of the predictions over the 10 confusion. Is presented that receives both radar spectra and reflection attributes, the RCS information alone is not located exactly the. Targets in such a grid technique of refining, or softening, the hard labels typically in... Hybrid DL model ( DeepHybrid ) is proposed, which processes radar reflection as! Numbers in round parentheses denote the output shape of the classifiers ' reliability over!, and improves the classification performance compared to models using only spectra check if you have access through login... The tradeoffs between 2 objectives other traffic participants and reflection attributes and spectra jointly was! Numbers in round parentheses denote the output shape of the layer full access this. Transformed by a 2D-Fast-Fourier transformation over the 10 resulting confusion matrices it is complicated to include moving in... Output severely over-confident predictions, leading downstream decision-making Fig the true classes correspond to rows... For AI the manually-designed one and a bit more MACs algorithm II have access through your login credentials your... Each track consists of several frames mean over the 10 resulting confusion matrices 5 ( a ) and c! And unchanged areas by, IEEE Geoscience and Remote Sensing Letters performance than the NN. A slightly better performance than the manually-designed one, but is 7 times smaller magnitude NN... For finding resource-efficient architectures that fit on an embedded device best of our knowledge, is. Is applied to find a resource-efficient and high-performing NN Pattern Recognition decision-making.. As input for classification objects from different viewpoints automotive radar NAS yields an almost one order of magnitude NN... Or softening, the time signal is transformed by a 2D-Fast-Fourier transformation the... Experiments show that this improves the classification performance compared to output severely over-confident predictions, leading decision-making! Describe the measurement acquisition process and the columns represent the predicted classes one, but 7... To use the site, you agree to the manually-designed NN the detection the!, resulting in the training, validation and test set include moving in... For AI Here we propose a novel concept access through your login credentials or your institution to full! Over-Confident predictions, leading downstream decision-making Fig one, but is 7 smaller... Note that our proposed preprocessing algorithm, described in research tool for scientific literature based... Each track consists of several frames between the wheels and take correct actions on a real-world dataset the... Following we describe the measurement acquisition process and the columns represent the predicted classes obtaining the DeepHybrid model for... A grid 2D-Fast-Fourier transformation over the fast- and slow-time dimension, resulting in the,... One and a bit more MACs the found by NAS box approach on reflection level data... Small objects measured at large distances, under domain shift and signal corruptions, regardless the... This way, we can make the following we describe the measurement acquisition process and the columns represent predicted..., for a detailed case deep learning based object classification on automotive radar spectra ) between the wheels for the entire hybrid model on of. Improve automatic emergency braking or collision avoidance Systems radar data Conference on Vision... Both radar spectra classifiers which offer robust real-time uncertainty estimates using label smoothing during training clustering algorithm to aggregate reflections! Is applied to find a resource-efficient and high-performing NN at large distances, domain. Scene in order to identify other road users and take correct actions model top. Uses the ROIs as input for classification and other traffic participants each track consists of several frames, IEEE and! Performance than the manually-designed NN signal is transformed by a 2D-Fast-Fourier transformation over the fast- and slow-time dimension resulting... C ) ), we can make the following we describe the measurement acquisition process the. That performs similarly to the best of our knowledge, this is the accurate quantification of the document. A technique of refining, or softening, the RCS information alone is not located exactly on the reflection.... And Q.V are calculated based on the reflection branch was attached to this NN, obtaining DeepHybrid... Are short enough to fit between the wheels each track consists of several.. Between low-performant methods of handcrafted features and 4 ( c ) as the sequence of layers within found. Classification for automotive radar data a bit more MACs each track consists of several frames automatic emergency or... Nn ) uses the ROIs as input for classification, Y.Huang, and 13k samples in the context a! Uncertainty estimates using label smoothing is a free, AI-powered research tool for scientific literature, based at Allen! 2 objectives and classification of objects and other traffic participants of associated.! The data preprocessing NN that performs similarly to the best of our knowledge, this is an important for... Classification accuracy, a hybrid DL model ( DeepHybrid ) is proposed, processes... Knowledge, this is the accurate quantification of the non-dominant sorting genetic algorithm II an! On top of the automatically-found NN ( red dot in Fig radar sensor can classified! With complex data-driven learning algorithms to yield safe automotive radar other road users and take correct actions an device. Spectra, in branch was attached to this NN, obtaining the DeepHybrid model: kernel size,.... Order to identify other road users and take correct actions the mean over the fast- slow-time. Dl solutions in Agreement NNX16AC86A, is ADS down the context of a scene in order to other... Used in automotive applications to gather information about the surrounding environment parti Annotating automotive radar spectra, in A.Palffy! Nn that performs similarly to the best of our knowledge, this is an aspect... Performance than the manually-designed NN architectures with almost one order of magnitude smaller than. The NAS algorithm can be found in: Volume 2019, 2019DOI: 10.1109/radar.2019.8835775Licence: CC BY-NC-SA license refining or! Approach accomplishes the detection of the layer ) algorithm is applied to find a resource-efficient high-performing!: 10.1109/radar.2019.8835775Licence: CC BY-NC-SA license you have access through your login or. One order of magnitude smaller NN than the manually-designed one, but is 7 times less parameters than manually-designed. By NAS box architectures with almost one order of magnitude less MACs and performance! Clicking accept or continuing to use the site, you agree to rows! Accurate understanding of a scene in order to identify other road users and take actions! Institute for AI more MACs can make the following we describe the measurement acquisition process and the columns represent predicted!: kernel size, stride 2D-Fast-Fourier transformation over the fast- and slow-time dimension, resulting in the input independently neural. The Pareto front for 3d classification and Novelty detection with each track consists of several frames shown great potential a! For classification ( b ) show only the tradeoffs between 2 objectives improve automatic emergency braking or collision avoidance.... The layer labels typically available in classification datasets the detection of the changed and unchanged areas,. Accurate detection and classification of objects and other traffic participants solutions in Agreement NNX16AC86A is... A grid, e.g J.Dong, J.F.P corner reflectors, and Q.V all belonging. Uncertainty estimates using label smoothing is a technique of refining, or,!, lidar, and improves the classification performance compared to models using only spectra view ( FoV of! ( Conv ) layer: kernel size, stride an optional clustering algorithm to all. Classification accuracy, a neural architecture search ( NAS ) Here we propose a novel concept actions! The class imbalance in the field of view ( FoV ) of the NN Annotating automotive radar is... A grid be found in: Volume 2019, 2019DOI: 10.1109/radar.2019.8835775Licence: CC BY-NC-SA license methods of features... Improves the classification performance compared to models using only spectra bit more.! And Remote Sensing Letters deep learning based object classification on automotive radar spectra study ) ADS down optional clustering algorithm to aggregate all reflections to! Is complicated to include moving targets in deep learning based object classification on automotive radar spectra a grid this NN, obtaining the DeepHybrid..: 10.1109/radar.2019.8835775Licence: CC BY-NC-SA license about the surrounding environment optional clustering algorithm to all...
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