Recent works have shown the superior robustness of four-dimensional (4D) Radar-based three-dimensional (3D) object detection in adverse weather conditions. However, processing 4D Radar data remains a challenge due to the large data size, which require substantial amount of memory for computing and storage. In previous work, an online density reduction is performed on the 4D Radar Tensor (4DRT) to reduce the data size, in which the density reduction level is chosen arbitrarily. However, the impact of density reduction on the detection performance and memory consumption remains largely unknown. In this paper, we aim to address this issue by conducting extensive hyperparamter tuning on the density reduction level. Experimental results show that increasing the density level from 0.01% to 50% of the original 4DRT density level proportionally improves the detection performance, at a cost of memory consumption. However, when the density level is increased beyond 5%, only the memory consumption increases, while the detection performance oscillates below the peak point. In addition to the optimized density hyperparameter, we also introduce 4D Sparse Radar Tensor (4DSRT), a new representation for 4D Radar data with offline density reduction, leading to a significantly reduced raw data size. An optimized development kit for training the neural networks is also provided, which along with the utilization of 4DSRT, improves training speed by a factor of 17.1 compared to the state-of-the-art 4DRT-based neural networks. All codes are available at: https://github.com/kaist-avelab/K-Radar.
Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor
Graduate School of Advanced Security Science and Technology, Korea Advanced Institute of Science and Technology（韩国科学技术院高级安全科学与技术研究生院）；CCS Graduate School of Mobility, Korea Advanced Institute of Science and Technology（韩国科学技术院移动技术研究生院）；Centre Inria d’Univeristé Côte d’Azur（尼斯大学Inria中心）
RADE-Net: Robust Attention Network for Radar-Only Object Detection in Adverse Weather
Infineon Technologies AG（英飞凌科技有限公司）；Institute of Visual Computing（视觉计算研究所）；Graz University of Technology（格拉茨工业大学）；Virtual Vehicle Research GmbH（虚拟车辆研究有限公司）
Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection
Thrust of Artificial Intelligence, Hong Kong University of Science and Technology (Guangzhou)（人工智能 thrust，香港科技大学（广州））；Momoniai AI；Department of Mechanical Engineering, Massachusetts Institute of Technology（机械工程系，麻省理工学院）；School of Information Engineering, Yancheng Institute of Technology（信息工程学院，盐城科技学院）；College of Information Science and Electronic Engineering, Zhejiang University（信息科学与电子工程学院，浙江大学）；Institute for Math & AI, Wuhan University（数学与人工智能研究所，武汉大学）；College of Science and Engineering and the Centre for AI and Data Science Innovation, James Cook University（科学与工程学院及人工智能与数据科学创新中心，詹姆斯库克大学）；School of Transportation, Southeast University（交通运输学院，东南大学）
University of Ottawa（渥太华大学）；Sensorcortek Inc（Sensorcortek 公司）
Object detection using automotive radars has not been explored with deep learning models in comparison to the camera based approaches. This can be attributed to the lack of public radar datasets. In this paper, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map. To build the dataset, we propose an instance-wise auto-annotation method. Furthermore, a novel Range-Azimuth-Doppler based multi-class ob…
Autonomous radar has been an integral part of advanced driver assistance systems due to its robustness to adverse weather and various lighting conditions. Conventional automotive radars use digital signal processing (DSP) algorithms to process raw data into sparse radar pins that do not provide information regarding the size and orientation of the objects. In this paper, we propose a deep-learning based algorithm for radar object detection. The algorithm takes in radar data in its raw tensor representation and places probabilistic oriented bounding boxes around the detected objects in bird's-e…
LTCI, Télécom Paris, Institut Polytechnique de Paris（巴黎综合理工学院电信学院LTCI实验室）
Understanding the scene around the ego-vehicle is key to assisted and autonomous driving. Nowadays, this is mostly conducted using cameras and laser scanners, despite their reduced performances in adverse weather conditions. Automotive radars are low-cost active sensors that measure properties of surrounding objects, including their relative speed, and have the key advantage of not being impacted by rain, snow or fog. However, they are seldom used for scene understanding due to the size and complexity of radar raw data and the lack of annotated datasets. Fortunately, recent open-sourced datase…
DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather
Infineon Technologies AG, Austria（英飞凌科技公司，奥地利）；Graz University of Technology, Austria（格拉茨技术大学，奥地利）；Virtual Vehicle Research GmbH, Austria（虚拟车辆研究有限公司，奥地利）
Télécom Paris（巴黎电信学院）；Valeo North America Inc.（法雷奥北美公司）
With their robustness to adverse weather conditions and ability to measure speeds, radar sensors have been part of the automotive landscape for more than two decades. Recent progress toward High Definition (HD) Imaging radar has driven the angular resolution below the degree, thus approaching laser scanning performance. However, the amount of data a HD radar delivers and the computational cost to estimate the angular positions remain a challenge. In this paper, we propose a novel HD radar sensing model, FFT-RadNet, that eliminates the overhead of computing the range-azimuth-Doppler 3D tensor, …
Quantum-Inspired Robust and Scalable SAR Object Classification
Tensor AI Solutions GmbH（Tensor AI解决方案有限公司）；Ulm University（乌尔姆大学）；Institute for Complex Quantum Systems（复杂量子系统研究所）；Hensoldt Sensors GmbH（亨索尔特传感器有限公司）；German Aerospace Center (DLR)（德国航空航天中心（DLR））；Microwaves and Radar Institute（微波与雷达研究所）
Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks
Duke University（杜克大学）；Information Systems Laboratories Inc.（信息系统实验室公司）；University of Dayton Research Institute（代顿大学研究院）；Colorado State University（科罗拉多州立大学）；AFRL（美国空军研究实验室）
Duke University（杜克大学）；Information Systems Laboratories Inc.（信息系统实验室公司）；University of Dayton（代顿大学）；Colorado State University（科罗拉多州立大学）；Air Force Research Laboratory（空军研究实验室）
Recent works exploring data-driven approaches to classical problems in adaptive radar have demonstrated promising results pertaining to the task of radar target localization. Via the use of space-time adaptive processing (STAP) techniques and convolutional neural networks, these data-driven approaches to target localization have helped benchmark the performance of neural networks for matched scenarios. However, the thorough bridging of these topics across mismatched scenarios still remains an open problem. As such, in this work, we augment our data-driven approach to radar target localization …
Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review
School of Information Engineering, Yancheng Institute Technology（盐城理工学院信息工程学院）；Thrust of Artificial Intelligence and Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）人工智能与智能交通研究所）；Faculty of Science and Engineering, University of Liverpool（利物浦大学科学与工程学院）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学高级技术学院）；School of Information Science and Technology, Nantong University（南通大学信息科学与技术学院）
ShrinkBox: Backdoor Attack on Object Detection to Disrupt Collision Avoidance in Machine Learning-based Advanced Driver Assistance Systems
eBRAIN Lab, New York University Abu Dhabi (NYUAD), UAE（eBRAIN实验室，纽约大学阿布扎赫尔分校（NYUAD），阿联酋）；AI and Digital Science Research Center, Technology Innovation Institute (TII), Abu Dhabi, UAE（人工智能与数字科学研究中心，技术创新研究所（TII），阿布扎赫尔，阿联酋）
