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Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 164卷

作者： Wu, Jingda Huang, Chao Huang, Hailong Lv, Chen Wang, Yuntong Wang, Fei-Yue Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Nanyang 639798 Singapore Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) emerges as a pivotal tool in crafting these BP strategies. This paper offers a comprehensive review of RL-based BP strategies, spotlighting advancements from 2021 to 2023. We completely organize and distill the relevant literature, emphasizing paradigm shifts in RL-based BP. Introducing a novel categorization, we trace the trajectory of efforts aimed at surmounting practical challenges encountered by autonomous vehicles through innovative RL techniques. To guide readers, we furnish a quantitative analysis that maps the volume and diversity of recent RL configurations, elucidating prevailing trends. Additionally, we delve into the imminent challenges and potential directions for the future of RL-driven BP in AD. These directions encompass addressing safety vulnerabilities, fostering continual learning capabilities, enhancing data efficiency, championing collaborative vehicular cloud networks, integrating large language models, and enhancing ethical considerations.

关键词： Autonomous driving Reinforcement learning Behavior planning Decision Autonomous vehicle

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A Review on state-of-the-Art Power Line Inspection Techniques

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020年第12期69卷 9350-9365页

作者： Yang, Lei Fan, Junfeng Liu, Yanhong Li, En Peng, Jinzhu Liang, Zize Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Robot Percept & Control Engn Lab Zhengzhou 450001 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

With the fast development of smart grid, the power line mileage and power equipments get rapid growth. The contradiction between the large number of maintenance equipments and the small number of maintenance workers becomes increasingly prominent. Meanwhile, the traditional maintenance mode has the disadvantages of over or under maintenance, which will lead to the increasing failure risk of power transmission system. Faced with these issues, to enhance the intelligence and automation level of power line inspection, many researchers have devoted much effort to the research of automatic power line inspection and some state-of-the-art techniques about power line inspection are proposed to improve the inspection efficiency and quality, such as unmanned aerial vehicle (UAV), image processing, deep architecture, and so on. In this article, we analyzed and summarized the state-of-the-art techniques on power line inspection to provide a valuable reference for the researchers engaged in the smart grid. First, the common inspection tasks on power line inspection are reviewed. Second, the existing inspection platforms are examined in this article. Also, the advantages and disadvantages of different platforms are analyzed accordingly. Third, faced with different inspection tasks, different inspection sensors are integrated into the inspection platforms for data collection. Therefore, the common sensors on inspection platforms are discussed. Finally, to realize automatic power line inspection, different inspection methods are proposed or improved, and these advanced inspection methods are surveyed and discussed.

关键词： Inspection Poles and towers Task analysis Insulators Smart grids Feature extraction Inspection methods inspection platforms inspection sensors inspection task power line inspection

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V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection with BEV Global Non-Maximum Suppression 35

V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection ...

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IEEE Intelligent Vehicles Symposium (IV)

作者： Zhang, Caiji Tian, Bin Meng, Shi Qi, Shuangying Sun, Yang Ai, Yunfeng Chen, Long Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Natl Key Lab Multimodal Artificial Intelligence S Inst Automat Beijing 100190 Peoples R China Chongqing Iron & Steel Grp Min Co Ltd Chongqing 400050 Peoples R China Hebei Univ Engn Sch Mech & Equipment Engn Handan 056038 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Waytous Inc Qingdao 266109 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9798350348811;9798350348828

In recent years, research on Vehicle-to-Everything (V2X) cooperative perception algorithms mainly focuses on the fusion of intermediate features from LiDAR point clouds. Since the emergence of excellent single-vehicle visual perception models like BEVFormer, collaborative perception schemes based on camera and late-fusion have become feasible approaches. This paper proposes a V2X-collaborative 3D object detection structure in Bird's Eye View (BEV) space, based on global non-maximum suppression and late-fusion (V2X-BGN), and conducts experiments on the V2X-Set dataset. Focusing on complex road conditions with extreme occlusion, the paper compares the camera-based algorithm with the LiDAR-based algorithm, validating the effectiveness of pure visual solutions in the collaborative 3D object detection task. Additionally, this paper highlights the complementary potential of camera-based and LiDAR-based approaches and the importance of object-to-ego vehicle distance in the collaborative 3D object detection task.

关键词： Object detection

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Finding Patterns in Adversarial Training

Finding Patterns in Adversarial Training

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Chinese Automation Congress (CAC)

作者： Wang, Yutong Wang, Fei-Yue Chinese Acad Sci Univ Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9781728176871

Adversarial training has become an universally accepted robust method to train networks defending against adversarial attacks. However, feeding networks with adversarial examples is a cumbersome process, and there is no clear explanation for the differences between networks with the same architecture that are learned by adversarial training and learned by natural training method. So in this paper, we focus on the patterns of network learned by adversarial training. For comparison, we visualize the weights and feature maps of networks learned by these two training methods on MNIST, and find some patterns of adversarially trained model that are important for defending against adversarial examples and that are unique to naturally trained model. First, we find that adversarially trained network denoises the noisy data caused by adversarial images and enhances the outlines of semantically informative content in feature maps. Second, we find that adversarial training performs model compression, making some of the channels of a convolutional layer have no activation on input images. Further observation shows that different from naturally trained model, which have different most activation channel corresponding to different input, adversarially trained model often has the same most activation channel regardless of input. This phenomenon also leads to a sparse network reducing 50% number of parameters and 87% computation costs, and the pruned network performs as good as original network after pruning the least activation channels without retraining. But the naturally trained counterpart loses more than 40% accuracy using the same pruning strategy.

关键词： adversarial training feature map denoising channel pruning

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On the Gaussian Filtering for Nonlinear Dynamic systems Using Variational Inference

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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International Conference on Information Fusion

作者： Yi Liu Xi Li Le Yang Lyudmila Mihaylova Ji Li Department of Electrical and Computer Engineering University of Canterbury Christchurch NZ State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems National University of Defense Technology Changsha China Department of Automatic Control and System Engineering University of Sheffield Sheffield UK Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements.

关键词： Target tracking Smoothing methods Filtering Estimation Filtering algorithms Prediction algorithms Minimization Nonlinear dynamical systems Bayes methods Iterative methods

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An Earth Observation Satellite Mission Planning Method Based on Deep Q-Learning 6

An Earth Observation Satellite Mission Planning Method Based...

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6th International Symposium on Computer and Information Processing Technology, ISCIPT 2021

作者： Chen, Tianyuan Wang, Hongfei Liu, Hao Wu, Peng University of Chinese Academy of Sciences Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (纸本)9781665441377

With the increasing number of earth observation satellites and observation demands, satellite mission planning is facing huge challenges. For traditional satellite mission planning methods, there are problems such as difficulty in modeling, high computational complexity, and low scalability. This paper proposes a satellite mission planning method based on Deep Q-Learning. Firstly, a hybrid satellite mission planning model is established that combines the constraint-satisfaction model, the 0-1 knapsack model and the Markov decision model. Then, the decision algorithm based on Deep Q-Learning is used to solve the problem, so that the satellite can make full use of the past mission planning experience to learn the optimal planning strategy. Finally, several traditional satellite mission planning methods-branch and bound algorithm, genetic algorithm, simulated annealing algorithm are compared, and various performances of the algorithms are tested and analyzed through simulation experiments. The superiority of this method is verified. © 2021 IEEE.

关键词： Satellites

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Variable Impedance control of Cable Actuated Continuum Manipulators

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INTERNATIONAL JOURNAL OF control AUTOMATION AND systems 2020年第7期18卷 1839-1852页

作者： He, Guangping Fan, Yanan Su, Tingting Zhao, Lei Zhao, Quanliang North China Univ Technol Dept Mech & Elect Engn Beijing 100144 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

Continuum manipulators are a class of special compliant robots that have important potential applications in the field of human-machine interactive operations, or work in cluttered and constrained environments. In these application scenarios, the most popular operation tasks are those with coupling force-position constraints. To simultaneously stabilize the desired operation force and the position of the manipulator, variable impedance control issues of the cable driven continuum manipulators are investigated in this paper. On the basis of constructing a novel Lyapunov function, a variable impedance control law is presented and the stability of the closed-loop system has also been analyzed. Then the operation space variable impedance control for a single segment cable driven continuum manipulator is realized by the aid of a pseudo-rigid-body model. Some numerical simulations also demonstrate the stability of the variable impedance control system.

关键词： Continuum manipulators control robots stability variable impedance

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Coordinated variable impedance control for multi-segment cable-driven continuum manipulators

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MECHANISM AND MACHINE THEORY 2020年 153卷 103969-103969页

作者： Su, Tingting Niu, Lianzhen He, Guangping Liang, Xu Zhao, Lei Zhao, Quanliang North China Univ Technol Dept Mech & Elect Engn Beijing 100144 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Continuum manipulators typically work in a constrained environment and rely on body compliance to conform around and interact with obstacles. The force operation capability of the continuum manipulators presents significant challenges for practical applications. In order to realize stable operations at multi-position contacts under coupling position-force constraints and improve the operational flexibility of multi-segment cable-driven continuum manipulators, coordinated variable impedance control at multiple contacting points along the continuum manipulators is proposed in this paper. By the aids of an external load estimator and a variable impedance controller in configuration space, the multi-segment cable-driven continuum manipulators based on pseudo-rigid-body modeling method can achieve coordinated variable impedance control in operation space. The stability of the external load estimator and the controller is analyzed. Numerical simulations and experiments also demonstrate the feasibility of the proposed control scheme for the multi-segment cable-driven continuum manipulators. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Variable impedance control Coordinated control Continuum manipulators Dynamics Cable-driven

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Learning from the Negativity: Deep Negative Correlation Meta-Learning for Adversarial Image Classification 27th

Learning from the Negativity: Deep Negative Correlation Meta...

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Zheng, Wenbo Yan, Lan Wang, Fei-Yue Gou, Chao School of Software Engineering Xi’an Jiaotong University Xi’an China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030678319

Adversarial images are commonly viewed negatively for neural network training. Here we present an opposite perspective: adversarial images can be used to investigate the problem of classifying adversarial images themselves. To this end, we propose a novel framework termed as Deep Negative Correlation Meta-Learning. In particular, we present a deep relation network to capture and memorize the relation among different samples. We further formulate a deep negative correlation learning, and design a novel meta-learning-based classifier to prevent overfitting and learn discriminative features. The final classification is derived from our relation network by learning to compare the features of samples. Experimental results demonstrate that our approach achieves significantly higher performance compared with other state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

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D?K? I: Data-Knowledge-Driven Group Intelligence Framework for Smart Service in Education Metaverse

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2056-2061页

作者： Zheng, Wenbo Yan, Lan Zhang, Wenwen Ouyang, Liwei Wen, Ding Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan 430070 Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha 410082 Peoples R China Hunan Univ Natl Supercomp Ctr Changsha 410082 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China North Automat Control Technol Inst Taiyuan Peoples R China

Metaverse is the fusion of cyber-physical-social intelligence, and the fusion becomes the core and fundamental property of the metaverse. As an important part of social operationalization, the education domain leads to the birth of the education metaverse. This article answers three basic questions about smart services in the education metaverse: 1) learning scene;2) technical framework;and 3) initial expansion. Specifically, four key elements constitute the learning scene in the education metaverse: 1) the learner;2) its time;3) space;and 4) learning event. In this learning scene, we propose a novel data-knowledge-driven group intelligence framework, aiming to transform data in the education metaverse into knowledge, and intersect and integrate intelligence with knowledge;based on this framework, we apply it to specific services, i.e., transaction and management services. We hope that our work opens the door to research on smart services in the education metaverse and more scholars will work for these challenges.

关键词： Education Artificial intelligence Artificial intelligence (AI) data education metaverse fusion knowledge

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