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Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autononous Driving

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arXiv 2023年

作者： Zhang, Xinyu Wang, Li Chen, Jian Fang, Cheng Yang, Lei Song, Ziying Yang, Guangqi Wang, Yichen Zhang, Xiaofei Li, Jun Li, Zhiwei Yang, Qingshan Zhang, Zhenlin Ge, Shuzhi Sam State Key Laboratory of Automotive Safety and Energy the School of Vehicle and Mobility Ts-inghua University Beijing100084 China School of Mechanical and Electrical Engineering China University of Mining and Technology-Beijing Beijing100083 China School of Artificial Intelligence China University of Mining and Technology-Beijing Beijing100083 China College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China Beijing Jingwei Hirain Technologies Co. Inc. Beijing100191 China China Automotive Innovation Cooperation Nanjing211113 China Department of Electrical and Computer Engineering Interactive Digital Media Institute Social Robotics Laboratory National University of Singapore Singapore

Radar has stronger adaptability in adverse scenarios for autonomous driving environmental perception compared to widely adopted cameras and LiDARs. Compared with commonly used 3D radars, latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception. However, due to the much higher noise than LiDAR, manufacturers choose different filtering strategies, resulting in a direct ratio between point cloud density and noise level. There is still a lack of comparative analysis on which method is beneficial for deep learning-based perception algorithms in autonomous driving. One of the main reasons is that current datasets only adopt one type of 4D radar, making it difficult to compare different 4D radars in the same scene. Therefore, in this paper, we introduce a novel large-scale multi-modal dataset featuring, for the first time, two types of 4D radars captured simultaneously. This dataset enables further research into effective 4D radar perception algorithms. Our dataset consists of 151 consecutive series, most of which last 20 seconds and contain 10,007 meticulously synchronized and annotated frames. Moreover, our dataset captures a variety of challenging driving scenarios, including many road conditions, weather conditions, nighttime and daytime with different lighting intensities and periods. Our dataset annotates consecutive frames, which can be applied to 3D object detection and tracking, and also supports the study of multi-modal tasks. We experimentally validate our dataset, providing valuable results for studying different types of 4D radars. This dataset is released on https://***/adeptthu/Dual-Radar. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

Efficient adaptation for end-to-end vision-based robotic manipulation

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arXiv 2020年

作者： Julian, Ryan Swanson, Benjamin Sukhatme, Gaurav S. Levine, Sergey Finn, Chelsea Hausman, Karol Google Research Robotics at Google Team Department of Computer Science University of Southern California Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Department of Computer Science Stanford University

One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments. Despite this potential, most of the robot learning systems today are deployed as a fixed policy and they are not being adapted after their deployment. Can we efficiently adapt previously learned behaviors to new environments, objects and percepts in the real world? In this paper, we present a method and empirical evidence towards a robot learning framework that facilitates continuous adaption. In particular, we demonstrate how to adapt vision-based robotic manipulation policies to new variations by fine-tuning via off-policy reinforcement learning, including changes in background, object shape and appearance, lighting conditions, and robot morphology. Further, this adaptation uses less than 0.2% of the data necessary to learn the task from scratch. We find that the simple approach of fine-tuning pre-trained policies leads to substantial performance gains over the course of fine-tuning, and that pre-training via RL is essential: training from scratch or adapting from supervised ImageNet features are both unsuccessful with such small amounts of data. We also find that these positive results hold in a limited continual learning setting, in which we repeatedly fine-tune a single lineage of policies using data from a succession of new tasks. Our empirical conclusions are consistently supported by experiments on simulated manipulation tasks, and by 52 unique fine-tuning experiments on a real robotic grasping system pre-trained on 580,000 grasps. For video results and an overview of the methods and experiments in this study, see the project website at https://***/continual-fine-tuning. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

SECTOR-COUPLED ENERGY SYSTEMS USING THE FLEXIBILITY OF SMART DSM

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SECTOR-COUPLED ENERGY SYSTEMS USING THE FLEXIBILITY OF SMART...

CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution

作者： C. Wiezorek S. Werner M. B. Firvida T. Vögele K. Strunz Department of Electrical Engineering and Computer Sciences Technical University of Berlin (TUB) Berlin Germany Easy Smart Grid GmbH (ESG) Karlsruhe Germany Robotics Innovation Centre German Research Centre for Artificial Intelligence (DFKI) Bremen Germany

Linked to the 3D challenges of the energy transition (decarbonisation, decentralization and digitization) are the integrated planning and operation of distribution networks and supply infrastructure. Increasing shares of renewable energies are forcing the use of new flexibilities for maintaining the resilience and stability of the power grid. Important components of such flexibility options are the use of multi-commodity systems (sector coupling) and Smart DSM. Activating the flexibility of smart demand side applications is key for a sustainable, reliable energy system. In FUSE (FUture Smart Energy), a German-Finnish R&D project, AI-based DSM methods are developed to increase the resilience of distribution grids, considering multi-energy applications and local network conditions. The methods are evaluated in a Smart Grid laboratory, principles are integrated into an optimization framework for planning system design and operation. The project is ongoing, hence results are subject to update. The DSM methods are based on automated generation of soft-control signals enabling dynamic, decentralized control of consumers based on energy availability. Consumers include either buffer storage or process flexibility, or both, with the latter being transformed into the former. First results of the controller testing in the power hardware/software Smart Grid laboratory are promising, with further investigation needed.

关键词： demand side management renewable energy sources smart power grids power distribution planning power system stability research and development laboratory techniques optimisation artificial intelligence power distribution control

Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

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Science China(Information Sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

Risk-averse MPC via visual-inertial input and recurrent networks for online collision avoidance

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arXiv 2020年

作者： Schperberg, Alexander Chen, Kenny Tsuei, Stephanie Jewett, Michael Hooks, Joshua Soatto, Stefano Mehta, Ankur Hong, Dennis The Robotics and Mechanisms Laboratory Department of Mechanical and Aerospace Engineering United States The Laboratory for Embedded Machines and Ubiquitous Robots Department of Electrical and Computer Engineering United States The UCLA Vision Lab Department of Computer Science United States The University of California Los Angeles Los AngelesCA90095 United States

In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates through each step of our MPC's finite time horizon. The RNN model is trained on a dataset that comprises of robot and landmark poses generated from camera images and inertial measurement unit (IMU) readings via a state-of-the-art visual-inertial odometry framework. To detect and extract object locations for avoidance, we use a custom-trained convolutional neural network model in conjunction with a feature extractor to retrieve 3D centroid and radii boundaries of nearby obstacles. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms, demonstrating autonomous behaviors that can plan fast and collision-free paths towards a goal point. Copyright © 2020, The Authors. All rights reserved.

关键词： Model predictive control

Ethics of Foundation Models in Computational Pathology: Overview of Contemporary Issues and Future Implications

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Du, Rui Fei Carbonell, Eduard Lloret Huang, Jiaxuan Liu, Sheng Wang, Xiaohang Shen, Dinggang Ke, Jing Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Dulwich College Shanghai Shanghai201111 China Tongji University School of Medicine Department of Thyroid and Breast Surgery Shanghai Fourth People’s Hospital Shanghai200434 China Shanghai Jiao Tong University School of Medicine Department of Radiation Oncology Ren Ji Hospital Shanghai200127 China ShanghaiTech University School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai201203 China Shanghai Clinical Research and Trial Center Shanghai200231 China Shanghai United Imaging Intelligence Co. Ltd Shanghai201807 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

Artificial intelligence (AI) has profoundly transformed our lives, reshaping industries and impacting nearly every aspect of society over the past few decades. It has recently become even more influential, primarily due to the rise of foundation models representing a new paradigm in AI development. These models, characterized by their large-scale training on vast datasets, have unique capabilities such as emergence and transference, enabling them to generalize across diverse tasks. Since their introduction, foundation models have been increasingly applied in fields such as autonomous driving, computer vision, marketing, finance, industrial robotics, and healthcare. Pathologists worldwide use computational methods to analyze diseases that profoundly impact human well-being, including cancer diagnosis and staging, genetic mutation prediction, and treatment and prognosis forecasting. In this article, we discuss how, despite the promise of foundation models in various applications, their development and application in computational pathology remain challenging due to inherent characteristics such as emergence, homogenization, hallucination, transference, compositionality, and explainability. While powerful, these traits introduce numerous ethical concerns and challenges, impacting safety and reliability, patient privacy, accountability, and equity and fairness in healthcare access. We examine these ethical issues, focusing on key concerns like algorithmic discrimination and misuse, accuracy, privacy breaches, transparency, public accessibility, and accountability. Furthermore, potential solutions to these challenges are analyzed, offering future perspectives on promoting the development and application of more ethical AI and foundation models in computational pathology. These insights aim to guide foundation models toward responsible integration of AI in healthcare. © 2025 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Diseases

Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections

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arXiv 2022年

作者： Bai, Zhengwei Hao, Peng Shangguan, Wei Cai, Baigen Barth, Matthew J. The Department of Electrical and Computer Engineering University of California at Riverside RiversideCA92507 United States The Center for Environmental Research and Technology University of California at Riverside RiversideCA92507 United States The State Key Laboratory of Rail Traffic Control and Safety The Beijing Engineering Research Center of EMC GNSS Technology for Rail Transportation School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system. In this study, we proposed a hybrid reinforcement learning (HRL) framework which combines the rule-based strategy and the deep reinforcement learning (deep RL) to support connected eco-driving at signalized intersections in mixed traffic. vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I) communications to achieve higher mobility and energy efficiency in mixed connected traffic. The HRL framework has three components: a rule-based driving manager that operates the collaboration between the rule-based policies and the RL policy;a multi-stream neural network that extracts the hidden features of vision and V2I information;and a deep RL-based policy network that generate both longitudinal and lateral eco-driving actions. In order to evaluate our approach, we developed a Unity-based simulator and designed a mixed-traffic intersection scenario. Moreover, several baselines were implemented to compare with our new design, and numerical experiments were conducted to test the performance of the HRL model. The experiments show that our HRL method can reduce energy consumption by 12.70% and save 11.75% travel time when compared with a state-of-the-art model-based Eco-Driving approach. © 2022, CC BY-NC-ND.

关键词： Reinforcement learning

vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9288-9294页

作者： Christoforos Kanellakis Petros Karvelis Sina Sharif Mansouri Ali-akbar Agha-mohammadi George Nikolakopoulos The authors are with the Robotics Team Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden The author is with Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109

This work establishes a novel robocentric Non-linear Model Predictive Control (NMPC) framework for fast fully autonomous navigation of quadrotors in featureless dark tunnel environments. Additionally, this work leverages the processing of a single camera to generate direction commands along the tunnel axis, while regulating the platform’s altitude. The extracted visual dynamics are coupled in the sequel with the NMPC problem, structured around the Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization method that is not sensitive to ill conditioning and is suitable for embedded NMPC implementations. Multiple fully realistic simulation results demonstrate the effectiveness of the proposed method in challenging environments.

关键词： Autonomous Vehicles Robot Navigation Non linear model predictive control MAV

Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication

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arXiv 2023年

作者： Gao, Zhen Ke, Malong Mei, Yikun Qiao, Li Chen, Sheng Ng, Derrick Wing Kwan Poor, H. Vincent The MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China The Wireless Product Division Ruijie Network Co. Ltd. Fuzhou350108 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom The School of Electrical Engineering and Telecommunications University of New South Wales Sydney2052 Australia The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08542 United States

The advent of the sixth-generation (6G) of wireless communications has given rise to the necessity to connect vast quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things for which massive access is critical. This paper surveys the most recent advances toward massive access in both academic and industry communities, focusing primarily on the promising compressive sensing-based grant-free massive access paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a compressive sensing-based grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input multiple-output systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss the key challenges and open issues to shed light on the potential future research directions of grant-free massive access. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things