Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges

Srivastav, Arvind; Mandal, Soumyajit

doi:10.1109/access.2023.3312382

Cited by 7 publications

(2 citation statements)

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“…Radar sensors actively perceive the surroundings and analyze the reflected waves to determine the position and the speed of objects by constantly emitting radio waves. The world of radar signal processing for autonomous vehicles offers a rich tapestry of techniques to extract meaningful information from the sensor's output, for example, doppler processing [26], occupancy grid maps [27], multi-input multi-output (MIMO) radar [28], and representations using radar point clouds [29]. The work presented in this paper focuses on radar point-cloud data representation for radar signal processing using the proposed deep neural network for 3D object detection.…”

Section: Radar Point Cloud and Camera-based Sensor Fusion For Object ...mentioning

confidence: 99%

NeXtFusion: Attention-Based Camera-Radar Fusion Network for Improved Three-Dimensional Object Detection and Tracking

Kalgaonkar,

El-Sharkawy

2024

Future Internet

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Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets. Deep camera–radar fusion neural networks offer a promising solution for reliable AV perception under any weather and lighting conditions. Cameras provide rich semantic information, while radars act like an X-ray vision, piercing through fog and darkness. This work proposes a novel, efficient camera–radar fusion network called NeXtFusion for robust AV perception with an improvement in object detection accuracy and tracking. Our proposed approach of utilizing an attention module enhances crucial feature representation for object detection while minimizing information loss from multi-modal data. Extensive experiments on the challenging nuScenes dataset demonstrate NeXtFusion’s superior performance in detecting small and distant objects compared to other methods. Notably, NeXtFusion achieves the highest mAP score (0.473) on the nuScenes validation set, outperforming competitors like OFT (35.1% improvement) and MonoDIS (9.5% improvement). Additionally, NeXtFusion demonstrates strong performance in other metrics like mATE (0.449) and mAOE (0.534), highlighting its overall effectiveness in 3D object detection. Furthermore, visualizations of nuScenes data processed by NeXtFusion further demonstrate its capability to handle diverse real-world scenarios. These results suggest that NeXtFusion is a promising deep fusion network for improving AV perception and safety for autonomous driving.

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Section: Radar Point Cloud and Camera-based Sensor Fusion For Object ...mentioning

confidence: 99%

NeXtFusion: Attention-Based Camera-Radar Fusion Network for Improved Three-Dimensional Object Detection and Tracking

Kalgaonkar,

El-Sharkawy

2024

Future Internet

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“…S EMANTIC segmentation is a core task that forms the basis of many deep learning-based applications, such as autonomous driving [9], [31], [32], [34], aerial imagery analytics [27], [30], [38], [40], [44], [47], and medical image analytics [8], [13], [16], [28], [29]. This is a per-pixel classification problem in which each pixel of the image is classified.…”

Section: Introductionmentioning

confidence: 99%

Class-Wise Adaptive Strategy for Semi Supervised Semantic Segmentation

Ju,

Noh,

Wang

et al. 2024

IEEE Access

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Semi-supervised semantic segmentation learns a model for classifying pixels into specific classes using a few labeled samples and numerous unlabeled images. The recent leading approach is consistency regularization by self-training with pseudo-labeling pixels having high confidences for unlabeled images. However, using only high-confidence pixels for self-training may result in losing much of the information in the unlabeled sets due to poor confidence calibration of modern deep learning networks. In this paper, we propose a class-wise adaptive strategy for semi supervised semantic segmentation (CASS) to cope with the loss of most information that occurs in existing high-confidence-based pseudo-labeling methods. Unlike existing semi-supervised semantic segmentation frameworks, CASS constructs a validation set on a labeled set, to leverage the calibration performance for each class. On this basis, we propose a calibration aware class-wise adaptive thresholding and class-wise adaptive over-sampling using the analysis results from the validation set. Our proposed CASS achieves state-of-the-art performance on the full data partition of the base PASCAL VOC 2012 dataset and on the 1/4 data partition of the Cityscapes dataset with significant margins of 83.0% and 80.4%, respectively.

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