the gap between low-performant methods of handcrafted features and 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). The mean test accuracy is computed by averaging the values on the confusion matrix main diagonal. with C being the number of classes, pc the number of correctly classified samples, and Nc the number of samples belonging to class c. Since a single-frame classifier is considered, the spectrum of each radar frame is a potential input to the NN, i.e.a data sample. Automated vehicles need to detect and classify objects and traffic Available: R.Altendorfer and S.Wirkert, Why the association log-likelihood survey,, E.Real, A.Aggarwal, Y.Huang, and Q.V. Le, Aging evolution for image 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. In this article, we exploit 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. View 4 excerpts, cites methods and background. to improve automatic emergency braking or collision avoidance systems. For further investigations, we pick a NN, marked with a red dot in Fig. This paper presents an novel object type classification method for automotive applications which uses deep learning with radar reflections. The objects ROI and optionally the attributes of its associated radar reflections are used as input to the NN. On the other hand, if there is a small object that can be run over, e.g.a can of coke, the ego-vehicle should classify it correctly and just ignore it. We propose a method that detects radar reflections using a constant false alarm rate detector (CFAR) [2]. We showed that DeepHybrid outperforms the model that uses spectra only. 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. The pedestrian and two-wheeler dummies move laterally w.r.t.the ego-vehicle. Moreover, the automatically-found NN has a larger stride in the first Conv layer and does not contain max-pooling layers, i.e.the input is downsampled only once in the network. The investigation shows that further research into training and calibrating DL networks is necessary and offers great potential for safe automotive object classification with radar sensors, and the quality of confidence measures can be significantly improved, thereby partially resolving the over-confidence problem. For each architecture on the curve illustrated in Fig. The method Besides precise detection and localization of objects, a reliable classification of the object types in real time is important in order to avoid unnecessary, evasive, or automatic emergency braking maneuvers for harmless objects. D.P. Kingma and J.Ba, Adam: A method for stochastic optimization, 2017. Each object can have a varying number of associated reflections. 2. radar cross-section. The NAS method prefers larger convolutional kernel sizes. The focus of this article is to learn deep radar spectra classifiers which offer robust real-time uncertainty estimates using label smoothing during training. output severely over-confident predictions, leading downstream decision-making To the best of our knowledge, this is the first time NAS is deployed in the context of a radar classification task. Audio Supervision. Comparing the architectures of the automatically- and manually-found NN (see Fig. The training set is unbalanced, i.e.the numbers of samples per class are different. classification in radar using ensemble methods, in, , Potential of radar for static object classification using deep The RCS is computed by taking the signal strength of the detected reflection and correcting it by the range-dependent dampening and the two-way antenna gain in the azimuth direction. Our proposed approach works with several objects in the FoV of the radar sensor, and can still utilize the radar spectrum, since the spectral ROI for each object is determined. It can be observed that using the RCS information in addition to the spectra helps DeepHybrid to better distinguish the classes. Here, we use signal processing techniques for tasks where good signal models exist (radar detection) and apply DL methods where good models are missing (object classification). Nevertheless, both models mistake some pedestrian samples for two-wheeler, and vice versa. 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. Moreover, a neural architecture search (NAS) algorithm is applied to find a resource-efficient and high-performing NN. Abstract: Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar. 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. We consider 8 different types of parked cars, moving pedestrian dummies, moving bicycle dummies, and several metallic objects that lie on the ground and are small enough to be run over, see Fig. ensembles,, IEEE Transactions on / Radar imaging 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. 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. The range-azimuth information on the radar reflection level is used to extract a sparse region of interest from the range-Doppler spectrum. 4 (a). Deploying the NAS algorithm yields a NN with similar accuracy, but with 7 times less parameters, depicted within the found by NAS box in (c). 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. Manually finding a high-performing NN architecture that is also resource-efficient w.r.t.an embedded device is tedious, especially for a new type of dataset. layer. smoothing is a technique of refining, or softening, the hard labels typically We present a hybrid model (DeepHybrid) that receives both radar point clouds, in, J.Lombacher, M.Hahn, J.Dickmann, and C.Whler, Object To improve the classification accuracy, we use a hybrid approach and input both radar reflection attributes, e.g.the radar cross-section (RCS), and radar spectra into the NN. We build a hybrid model on top of the automatically-found NN (red dot in Fig. In the following we describe the measurement acquisition process and the data preprocessing. The NAS algorithm can be adapted to search for the entire hybrid model. 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. II-D), the object tracks are labeled with the corresponding class. Radar Reflections, Improving Uncertainty of Deep Learning-based Object Classification on It fills Before employing DL solutions in safety-critical applications, such as automated driving, an indispensable prerequisite is the accurate quantification of the classifiers' reliability. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). Each experiment is run 10 times using the same training and test set, but with different initializations for the NNs parameters. Related approaches for object classification can be grouped based on the type of radar input data used. Its architecture is presented in Fig. Reliable object classification using automotive radar sensors has proved to be challenging. Max-pooling (MaxPool): kernel size. CFAR [2]. Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. 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. We present a hybrid model (DeepHybrid) that receives both radar spectra and reflection attributes as inputs, e.g. algorithms to yield safe automotive radar perception. Intelligent Transportation Systems, Ordered statistic CFAR technique - an overview, 2011 12th International Radar Symposium (IRS), Clustering of high resolution automotive radar detections and subsequent feature extraction for classification of road users, 2015 16th International Radar Symposium (IRS), Radar-based road user classification and novelty detection with recurrent neural network ensembles, Pedestrian classification with a 79 ghz automotive radar sensor, Pedestrian detection procedure integrated into an 24 ghz automotive radar, Pedestrian recognition using automotive radar sensors, Image-based pedestrian classification for 79 ghz automotive radar, Semantic segmentation on radar point clouds, Object classification in radar using ensemble methods, Potential of radar for static object classification using deep learning methods, Convolutional long short-term memory networks for doppler-radar based target classification, Deep learning-based object classification on automotive radar spectra, Cnn based road user detection using the 3d radar cube, Chirp sequence radar undersampled multiple times, IEEE Transactions on Aerospace and Electronic Systems, Why the association log-likelihood distance should be used for measurement-to-track association, 2016 IEEE Intelligent Vehicles Symposium (IV), Aging evolution for image classifier architecture search, Multi-objective optimization using evolutionary algorithms, Designing neural networks through neuroevolution, Adaptive weighted-sum method for bi-objective optimization: Pareto front generation, Structural and multidisciplinary optimization, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, Regularized evolution for image classifier architecture search, Pointnet: Deep learning on point sets for 3d classification and segmentation, Adam: A method for stochastic optimization, https://doi.org/10.1109/ITSC48978.2021.9564526, https://cdn.euroncap.com/media/58226/euro-ncap-aeb-vru-test-protocol-v303.pdf, https://cdn.euroncap.com/media/56143/euro-ncap-aeb-c2c-test-protocol-v302.pdf, All Holdings within the ACM Digital Library. 4) The reflection-to-object association scheme can cope with several objects in the radar sensors FoV. Reliable object classification using automotive radar sensors has proved to be challenging. classification of road users, in, R.Prophet, M.Hoffmann, M.Vossiek, C.Sturm, A.Ossowska, Experiments on a real-world dataset demonstrate the ability to distinguish relevant objects from different viewpoints. automotive radar sensor, in, H.Rohling, S.Heuel, and H.Ritter, Pedestrian detection procedure 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. Deep Learning-based Object Classification on Automotive Radar Spectra (2019) | Kanil Patel | 42 Citations Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. 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