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24th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation

作者： Koehler, Daniel Meinl, Frank Blume, Holger Leibniz Univ Hannover Inst Microelect Syst Hannover Germany Robert Bosch GmbH Leonberg Cross Domain Comp Solut Leonberg Germany

ISBN: (纸本)9783031783760;9783031783777

Autonomous driving systems require performant and reliable perception, though they only possess limited computational resources, which places a high priority on the efficiency of the underlying algorithms. radar sensors play an important role in this context, because they provide data in the form of sparse point clouds, which can be stored and processed in a condensed and efficient manner. However, this sparsity is often overlooked in the design of perception algorithms, such as convolutional object detection networks. In this work we investigate how sparse submanifold convolutions can be used to exploit this sparsity to drastically reduce the computational complexity of a CNN-based radar object detector. To this end, we propose an efficient implementation of submanifold convolutions on a vertical vector processor architecture called (VPRO)-P-2, which is emulated on an FPGA board. Benchmarks on the public nuScenes dataset and an internal dataset show, that the sparse models provide competitive detection performance, while achieving average speedups of up to 27x over their dense counterparts on the considered vector processor. Finally, the sparse model deployed on the FPGA is integrated into a measurement vehicle with three front-facing high-resolution radars, to demonstrate real-time online radar object detection running at 15 Hz.

关键词： radar object detection Perception Sparse CNN Vector Processor Neural network accelerators FPGA

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radar object detection Using Data Merging, Enhancement and Fusion 21

Radar Object Detection Using Data Merging, Enhancement and F...

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11th International Conference on Multimedia Retrieval (ICMR)

作者： Yu, Jun Hao, Xinlong Gao, Xinjian Sun, Qiang Liu, Yuyu Chang, Peng Zhang, Zhong Gao, Fang Shuang, Feng Univ Sci & Technol China Hefei Peoples R China Ping Technol Co Ltd Shenzhen Peoples R China PAII Labs Palo Alto CA USA Hefei ZhanDa Intelligence Technol Co Ltd Hefei Peoples R China Guangxi Univ Nanning Peoples R China

ISBN: (纸本)9781450384636

Compared to visible images, radar images are generally considered to be an active and robust solution, even in adverse driving situations, for object detection. However, the accuracy of radar object detection (ROD) is always poor. Owing to taking full advantage of data merging, enhancement and fusion, this paper proposes an effective ROD system with only radar images as the input. First, an aggregation module is designed to merge the data from all chirps in the same frame. Then, various gaussian noises with different parameters are employed to increase data diversity and reduce over-fitting based on the analysis of training data. Moreover, due to the process of inference with default parameters is not accurate enough, some hyperparameters are changed to increase the accuracy performance. Finally, a combination strategy is adopted to benefit from multi-model fusion. ROD2021 Challenge is supported by ACM ICMR 2021, and our team (ustc-nelslip) ranked 2nd in the test stage of this challenge. Diverse evaluations also verify the superiority of the proposed system.

关键词： radar object detection data merging enhancement multi-model fusion

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TC-radar: Transformer-CNN Hybrid Network for Millimeter-Wave radar object detection

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REMOTE SENSING 2024年第16期16卷 2881页

作者： Jia, Fengde Li, Chenyang Bi, Siyi Qian, Junhui Wei, Leizhe Sun, Guohao Donghua Univ Sch Informat Sci & Technol Shanghai 201620 Peoples R China Donghua Univ Coll Text Shanghai Frontier Sci Res Ctr Modern Text Shanghai 201620 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Shanghai Wellplan Technol Co Ltd Shanghai 200030 Peoples R China Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610207 Peoples R China Sichuan Prov Key Lab Robot Satellites Chengdu 610207 Peoples R China

In smart transportation, assisted driving relies on data integration from various sensors, notably LiDAR and cameras. However, their optical performance can degrade under adverse weather conditions, potentially compromising vehicle safety. Millimeter-wave radar, which can overcome these issues more economically, has been re-evaluated. Despite this, developing an accurate detection model is challenging due to significant noise interference and limited semantic information. To address these practical challenges, this paper presents the TC-radar model, a novel approach that synergistically integrates the strengths of transformer and the convolutional neural network (CNN) to optimize the sensing potential of millimeter-wave radar in smart transportation systems. The rationale for this integration lies in the complementary nature of CNNs, which are adept at capturing local spatial features, and transformers, which excel at modeling long-range dependencies and global context within data. This hybrid approach allows for a more robust and accurate representation of radar signals, leading to enhanced detection performance. A key innovation of our approach is the introduction of the Cross-Attention (CA) module, which facilitates efficient and dynamic information exchange between the encoder and decoder stages of the network. This CA mechanism ensures that critical features are accurately captured and transferred, thereby significantly improving the overall network performance. In addition, the model contains the dense information fusion block (DIFB) to further enrich the feature representation by integrating different high-frequency local features. This integration process ensures thorough incorporation of key data points. Extensive tests conducted on the CRUW and CARRADA datasets validate the strengths of this method, with the model achieving an average precision (AP) of 83.99% and a mean intersection over union (mIoU) of 45.2%, demonstrating robust radar sensing capabilitie

关键词： radar object detection millimeter-wave radar transformer CNN

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RODNet: A Real-Time radar object detection Network Cross-Supervised by Camera-radar Fused object 3D Localization

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2021年第4期15卷 954-967页

作者： Wang, Yizhou Jiang, Zhongyu Li, Yudong Hwang, Jenq-Neng Xing, Guanbin Liu, Hui Univ Washington Dept Elect & Comp Engn Seattle WA 98105 USA Silkwave Holdings Ltd Hong Kong Peoples R China

Various autonomous or assisted driving strategies have been facilitated through the accurate and reliable perception of the environment around a vehicle. Among the commonly used sensors, radar has usually been considered as a robust and cost-effective solution even in adverse driving scenarios, e.g., weak/strong lighting or bad weather. Instead of considering fusing the unreliable information from all available sensors, perception from pure radar data becomes a valuable alternative that is worth exploring. In this paper, we propose a deep radar object detection network, named RODNet, which is cross-supervised by a camera-radar fused algorithm without laborious annotation efforts, to effectively detect objects from the radio frequency (RF) images in real-time. First, the raw signals captured by millimeter-wave radars are transformed to RF images in range-azimuth coordinates. Second, our proposed RODNet takes a snippet of RF images as the input to predict the likelihood of objects in the radar field of view (FoV). Two customized modules are also added to handle multi-chirp information and object relative motion. The proposed RODNet is cross-supervised by a novel 3D localization of detected objects using a camera-radar fusion (CRF) strategy in the training stage. Due to no existing public dataset available for our task, we create a new dataset, named CRUW, (1) (1) The dataset and code are available at https://***/. which contains synchronized RGB and RF image sequences in various driving scenarios. With intensive experiments, our proposed cross-supervised RODNet achieves 86% average precision and 88% average recall of object detection performance, which shows the robustness in various driving conditions.

关键词： radar radar imaging radar detection object detection Three-dimensional displays Radio frequency Cameras radar object detection deep CNN autonomous driving advanced driver assistance system cross-modal supervision M-Net temporal deformable convolution temporal inception CNN radar object annotation

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RadSimReal: Bridging the Gap Between Synthetic and Real Data in radar object detection With Simulation

RadSimReal: Bridging the Gap Between Synthetic and Real Data...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Bialer, Oded Haitman, Yuval Gen Motors Tech Ctr Israel Herzliyya Israel

ISBN: (纸本)9798350353006

object detection in radar imagery with neural networks shows great potential for improving autonomous driving. However, obtaining annotated datasets from real radar images, crucial for training these networks, is challenging, especially in scenarios with long-range detection and adverse weather and lighting conditions where radar performance excels. To address this challenge, we present RadSimReal, an innovative physical radar simulation capable of generating synthetic radar images with accompanying annotations for various radar types and environmental conditions, all without the need for real data collection. Remarkably, our findings demonstrate that training object detection models on RadSimReal data and subsequently evaluating them on real-world data produce performance levels comparable to models trained and tested on real data from the same dataset, and even achieves better performance when testing across different real datasets. RadSimReal offers advantages over other physical radar simulations that it does not necessitate knowledge of the radar design details, which are often not disclosed by radar suppliers, and has faster run-time. This innovative tool has the potential to advance the development of computer vision algorithms for radar-based autonomous driving applications. Our GitHub: https://***/RadSimReal.

关键词： Automotive radar Autonomous Driving radar object detection radar Simulation

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ROD2021 Challenge: A Summary for radar object detection Challenge for Autonomous Driving Applications 21

ROD2021 Challenge: A Summary for Radar Object Detection Chal...

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11th International Conference on Multimedia Retrieval (ICMR)

作者： Wang, Yizhou Hwang, Jenq-Neng Wang, Gaoang Liu, Hui Kim, Kwang-Ju Hsu, Hung-Min Cai, Jiarui Zhang, Haotian Jiang, Zhongyu Gu, Renshu Univ Washington Seattle WA 98195 USA Zhejiang Univ Hangzhou Zhejiang Peoples R China Silkwave Holdings Ltd Hong Kong Peoples R China Elect & Telecommun Res Inst ETRI Daegu South Korea Hangzhou Dianzi Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450384636

The radar object detection 2021 (ROD2021) Challenge, held in the ACM International Conference on Multimedia Retrieval (ICMR) 2021, has been introduced to detect and classify objects purely using an FMCW radar for autonomous driving applications. As a robust sensor to all-weather conditions, radar has rich information hidden in the radio frequencies, which can potentially achieve object detection and classification. This insight will provide a new object perception solution for an autonomous vehicle even in adverse driving scenarios. The ROD2021 Challenge is the first public benchmark focusing on this topic, which attracts great attention and participation. There are more than 260 participants among 37 teams from more than 10 countries with different academic and industrial affiliations, contributing about 300 submissions in the first phase and 400 submissions in the second phase. The final performance is evaluated by average precision (AP). Results add strong value and a better understanding of the radar object detection task for the autonomous vehicle community.

关键词： radar object detection autonomous driving ROD2021 Challenge dataset neural networks radio frequency

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Squeeze-and-Excitation network-Based radar object detection With Weighted Location Fusion 21

Squeeze-and-Excitation network-Based Radar Object Detection ...

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11th International Conference on Multimedia Retrieval (ICMR)

作者： Sun, Pengliang Niu, Xuetong Sun, Pengfei Xu, Kele Chinese Univ Hong Kong Hong Kong Peoples R China Kings Coll London London England Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781450384636

radar object detection refers to identify objects from radar data, and the topic has received increasing interest during the last years, due to the appealing property of radar imaging and evident applications. However, the detection performance heavily relied on semantic information extraction, which is a great challenge in practical settings. Moreover, although remarkable progress has been made, most previous attempts are restrained from the essentially limited property of the employed single modality. Inspired by the recent success of cross-modality deep learning, we propose a novel cross-modality deep learning framework for the radar object detection task using the Squeeze-and-Excitation network, aiming to provide more powerful feature representation. Moreover, a novel noisy detection approach is also explored in our study, to increase the model's ability to handle with noise. Finally, a novel weighted location fusion strategy is introduced in our framework, to improve the detection performance further. To empirically investigate the effectiveness of the proposed framework, we conduct extensive experiments on the 2021 ICMR ROD challenge. The obtained results suggest that our framework outperforms related approaches. Our method ranks as the 3rd place on the final leaderboard, with an average precision (AP) percentage of 76.1. Models and codes are available at https://***/sunpengliang/modelConfusion.

关键词： Cross-modal machine learning Ensemble learning radar object detection

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DANet: Dimension Apart Network for radar object detection 21

DANet: Dimension Apart Network for Radar Object Detection

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11th International Conference on Multimedia Retrieval (ICMR)

作者： Ju, Bo Yang, Wei Jia, Jinrang Ye, Xiaoqing Chen, Qu Tan, Xiao Sun, Hao Shi, Yifeng Ding, Errui Baidu Inc Beijing Peoples R China

ISBN: (纸本)9781450384636

In this paper, we propose a dimension apart network (DANet) for radar object detection task. A Dimension Apart Module (DAM) is first designed to be lightweight and capable of extracting temporalspatial information from the RAMap sequences. To fully utilize the hierarchical features from the RAMaps, we propose a multi-scale U-Net style network architecture termed DANet. Extensive experiments demonstrate that our proposed DANet achieves superior performance on the radar detection task at much less computational cost, compared to previous pioneer works. In addition to the proposed novel network, we also utilize a vast amount of data augmentation techniques. To further improve the robustness of our model, we ensemble the predicted results from a bunch of lightweight DANet variants. Finally, we achieve 82.2% on average precision and 90% on average recall of object detection performance and rank at 1st place in the ROD2021 radar detection challenge. Our code is available at: https://***/jb892/ROD2021_radar_ detection_Challenge_Baidu.

关键词： radar object detection convolutional neural networks DAM autonomous driving

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Pre-Training For mmWave radar object detection Through Masked Image Modeling 2

Pre-Training For mmWave Radar Object Detection Through Maske...

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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Zhuang, Long Jiang, Tiezhen School of Integrated Circuits Anhui University Hefei China

ISBN: (纸本)9798350331417

Millimeter-wave (mmWave) radar object detection (ROD) is an indispensable technique in autonomous driving technology because of its ability to maintain stability in complex environments. Deep learning (DL) techniques have made promising achievements in radar target detection techniques, but these techniques usually require large labeled datasets. However, the labeling of millimeter-wave radar datasets is more complex. In this paper, we introduce a novel pre-training method based on mask image modeling (MIM) in the ROD task, aiming to address the dataset limitations in the development of ROD techniques. The method performs spatial and temporal masking operations on unlabeled millimeter-wave radar images, which can acquire more spatial and temporal features simultaneously in the pre-training stage. Furthermore, inspired by T-RODNet, this paper presents a lightweight SS-RODNet network for testing the effectiveness of the proposed MIM method. The model aims to fully integrate the advantages of extracting local and global features. Numerous ablation experiments prove that the proposed MIM method is effective, and SS-RODNet achieves second place on the CRUW datasets. © 2023 IEEE.

关键词： 3D CNN autonomous driving masked image modeling radar object detection Transformer

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A Recurrent CNN for Online object detection on Raw radar Frames

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 13432-13441页

作者： Decourt, Colin VanRullen, Rufin Salle, Didier Oberlin, Thomas Univ Toulouse Artificial & Nat Intelligence Toulouse Inst F-31400 Toulouse France Univ Toulouse ISAE SUPAERO F-31400 Toulouse France CerCO CNRS UMR5549 F-31052 Toulouse France NXP Semicond F-31100 Toulouse France

Automotive radar sensors provide valuable information for advanced driving assistance systems (ADAS). radars can reliably estimate the distance to an object and the relative velocity, regardless of weather and light conditions. However, radar sensors suffer from low resolution and huge intra-class variations in the shape of objects. Exploiting the time information (e.g, multiple frames) has been shown to help to capture better the dynamics of objects and, therefore, the variation in the shape of objects. Most temporal radar object detectors use 3D convolutions to learn spatial and temporal information. However, these methods are often non-causal and unsuitable for real-time applications. This work presents RECORD, a new recurrent CNN architecture for online radar object detection. We propose an end-to-end trainable architecture mixing convolutions and ConvLSTMs to learn spatio-temporal dependencies between successive frames. Our model is causal and requires only the past information encoded in the memory of the ConvLSTMs to detect objects. Our experiments show such a method's relevance for detecting objects in different radar representations (range-Doppler, range-angle) and outperform state-of-the-art models on the ROD2021 and CARRADA datasets while being less computationally expensive.

关键词： Computer vision and pattern recognition radar object detection autonomous driving radar imaging

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