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Comparative study on e-bikes' decision-making behaviors under flashing green and green countdown

JOURNAL OF INTELLIGENT & FUZZY systems 2021年第3期41卷 4407-4414页

作者： Meng, Haoyang Dong, Sheng Zhou, Jibiao Zhang, Shuichao Li, Zhenjiang Ningbo Univ Technol Sch Civil & Transportat Engn Fenghua Rd 201 Ningbo Zhejiang Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

Green flash light (FG) and green countdown (GC) are the two most common signal formats applied in green-red transition that provides drivers additional alert before termination of green phase. Due to their importance and function in stop-pass decision-making process, proper use of them has become a critical issue to greatly improve the safety and efficiency of signalized intersections. Gradually e-bike riders have become more important commuters in China, however, the influence of FG or GC on them is not clear yet and need pay more attention to it. This study chooses two almost identical intersections to obtain highly accurate trajectory data of e-bike riders to study their decision-making behaviors under FG or GC. The e-bike riders' behavior is classified into four categories and is to identify their stop-pass decision points using the acceleration trend. Two binary-logit models were built to predict the stop-pass decision behaviors for the different e-bike rider groups, explaining that the potential time to the stop-line is the dominant independent factor of the different behaviors of GC and FG. Furthermore empirical analysis of decision points indicated that GC provides the earlier stop-pass decision point and longer decision making duration on the one side while results in more complexity of decision making and greater risk of stop-line crossing than FG on the other side.

关键词： Driving behavior flashing green green countdown electric bicycle stop-pass decision

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Terrain Mapping for Autonomous Trucks in Surface Mine 25

Terrain Mapping for Autonomous Trucks in Surface Mine

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IEEE 25th International Conference on Intelligent Transportation systems (ITSC)

作者： Wang, Junhui Tian, Bin Zhu, Yachen Yao, Tingting Pan, Ziyu Chen, Long Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Waytous Inc Beijing Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Maps with different representations play an essential role in the development of automotive intelligence. In order to enhance the capacity of autonomous trucks in surface mine, an extensible terrain mapping system based on LiDAR is proposed in this paper. Point cloud map, 2.5D grid map, and mesh map are integrated into a unified and extensible map-building framework. In order to adapt to the unique characteristics of surface mine, terrain mapping methods are proposed based on existing approaches. Each map-building method builds a local robot-centered map for time-sensitive tasks. Local maps are fused into global maps in the cloud for non-time-sensitive tasks. The construction method of the point cloud map can avoid the loss of information when updating the map by computing convex hulls. The 2.5D grid map can model the unstructured and rugged terrain of mines. The mesh map is built based on Poisson reconstruction, which is conducive to human-truck interaction. In addition, the map maintenance method in the framework is proposed. Experiments are conducted with datasets collected in real-world scenes.

关键词： Point cloud compression Surface reconstruction Terrain mapping Maintenance engineering Surface roughness Path planning Rough surfaces

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COMBINING CROWD AND MACHINE INTELLIGENCE TO DETECT FALSE NEWS ON SOCIAL MEDIA

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MIS QUARTERLY 2022年第2期46卷 977-1008页

作者： Wei, Xuan Zhang, Zhu Zhang, Mingyue Chen, Weiyun Zeng, Daniel Dajun Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Shanghai Int Studies Univ Sch Business & Management Shanghai Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

The explosive spread of false news on social media has severely affected many areas such as news ecosystems, politics, economics, and public trust, especially amid the COVID-19 infodemic. Machine intelligence has met with limited success in detecting and curbing false news. Human knowledge and intelligence hold great potential to complement machine-based methods. Yet they are largely underexplored in current false news detection research, especially in terms of how to efficiently utilize such information. We observe that the crowd contributes to the challenging task of assessing the veracity of news by posting responses or reporting. We propose combining these two types of scalable crowd judgments with machine intelligence to tackle the false news crisis. Specifically, we design a novel framework called CAND, which first extracts relevant human and machine judgments from data sources including news features and scalable crowd intelligence. The extracted information is then aggregated by an unsupervised Bayesian aggregation model. Evaluation based on Weibo and Twitter datasets demonstrates the effectiveness of crowd intelligence and the superior performance of the proposed framework in comparison with the benchmark methods. The results also generate many valuable insights, such as the complementary value of human and machine intelligence, the possibility of using human intelligence for early detection, and the robustness of our approach to intentional manipulation. This research significantly contributes to relevant literature on false news detection and crowd intelligence. In practice, our proposed framework serves as a feasible and effective approach for false news detection.

关键词： False news fake news wisdom of crowds hybrid intelligence graphical model

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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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Coordination and vibration control for two sets of flexible satellites with input constraints and actuator failures

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JOURNAL OF VIBRATION AND control 2021年第11-12期27卷 1281-1296页

作者： Ji, Ning Yang, Hongjun Liu, Jinkun Beihang Univ Sch Automat Sci & Elect Engn Xueyuan Rd 37 Beijing 100191 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China

In this study, we considered the coordination and vibration control for two sets of flexible satellite systems with input constraints and actuator failures. The parameters of both systems were kept uniform, and one was called the master flexible satellite, and the other one was called slave flexible satellite. The two sets of the flexible satellite systems were modelled as partial differential equations. Coordination control laws were designed to turn the master flexible satellite system to the desired angle and then turn the slave system to the angle of the master satellite system. Furthermore, the hyperbolic tangent function was adopted to realize the control input constraint. The adaptive control laws were designed to ensure that the systems work normally even when the actuator fails. Lyapunov's direct method was adopted and stability analysis was given. Finally, numerical simulations were conducted to validate the results.

关键词： Coordination control two sets of flexible satellites hyperbolic tangent function input constraint actuator failure

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Parallel Healthcare: Robotic Medical and Health Process Automation for Secured and Smart Social Healthcares

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2020年第3期7卷 581-586页

作者： Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

Welcome to the new issue of the IEEE Transactions on Computational Social systems (TCSS). I am grateful to report that, as of April 9, 2020,the Citescore of TCSS has reached to 5.26, a new high. Many thanks to all of ...

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Enhancing Feedback Steering controllers for Autonomous Vehicles With Deep Monte Carlo Tree Search

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 10438-10445页

作者： Tian, Fangyin Wang, Fei-Yue Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Res Inst Tsinghua Pearl River Delta 519090 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China Tsinghua Univ BNRist Dept Automat Beijing 100084 Peoples R China

Steering control is a vital function for autonomous vehicles, whose performance largely determines the driving safety. The widely used feedback steering controllers degrade significantly when vehicles drive at high speeds. This is mainly because these controllers can not effectively exploit nonlinear vehicle dynamics and future road information. Hence, the steering actions generated by these controllers can not achieve accurate and stable path tracking performance. To overcome this limitation, we propose a deep learning based Monte Carlo Tree Search (DeepMCTS) method to enhance the feedback controllers. Due to the accuracy of the learning-based model in characterizing nonlinear vehicle dynamics and the lookahead search ability of MCTS, the control performance of the feedback controllers can be enhanced significantly. Different from MPC methods, which leverage vehicle dynamics and future road information via online optimization, our method relies on the efficient tree search process. Hence, our method can avoid heavy computational burden. Testing results validate the proposed method can achieve better tracking accuracy and stability in high speeds scenarios.

关键词： Autonomous vehicle navigation intelligent transportation systems AI-based methods

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A Pathway Forward: The Evolution of Intelligent Vehicles Research on IEEE T-IV

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2022年第4期7卷 918-928页

作者： Wang, Haoran Wang, Xiao Li, Xuan Wu, Xingtang Hu, Jia Chen, Yuanyuan Liu, Zhongmin Li, Ying Tongji Univ Minist Educ Key Lab Rd & Traff Engn Shanghai Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China North Automat Control Technol Inst Taiyuan Taiyuan 030000 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

This paper presents a bibliographic and collaboration pattern analysis of the IEEE Transactions on Intelligent Vehicles (T-IV). In this paper, a journal analysis framework is proposed. The most productive/influential authors, institutions, and countries/regions are identified. The research group structure is generated. Hot research topics and trends are discussed. Analysis results find that: i) The U.S. is now dominating the IV research field. It contributes 32% of papers in the IEEE T-IV;ii) Vehicle planning, vehicle perception, and energy management are the top-three hottest research topics from 2016 to 2021;iii) The Cooperative Automation (CA) is a research trend that includes vehicle group control, platoon control, intersection management, cooperation security, and cooperative perception;iv) The new "3224 " processing policy is demonstrated to reduce processing durations by 90% and attracts nine times more manuscripts.

关键词： Bibliographics Intelligent vehicles Productivity Market research Autonomous vehicles Collaboration Pattern analysis Bibliographic analysis collaboration pattern intelligent vehicles (IV) cooperative automation (CA)

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A Novel Heterogeneous Actor-critic Algorithm with Recent Emphasizing Replay Memory

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International Journal of Automation and computing 2021年第4期18卷 619-631页

作者： Bao Xi Rui Wang Ying-Hao Cai Tao Lu Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Reinforcement learning(RL) algorithms have been demonstrated to solve a variety of continuous control tasks. However,the training efficiency and performance of such methods limit further applications. In this paper, we propose an off-policy heterogeneous actor-critic(HAC) algorithm, which contains soft Q-function and ordinary Q-function. The soft Q-function encourages the exploration of a Gaussian policy, and the ordinary Q-function optimizes the mean of the Gaussian policy to improve the training efficiency. Experience replay memory is another vital component of off-policy RL methods. We propose a new sampling technique that emphasizes recently experienced transitions to boost the policy training. Besides, we integrate HAC with hindsight experience replay(HER) to deal with sparse reward tasks, which are common in the robotic manipulation domain. Finally, we evaluate our methods on a series of continuous control benchmark tasks and robotic manipulation tasks. The experimental results show that our method outperforms prior state-of-the-art methods in terms of training efficiency and performance, which validates the effectiveness of our method.

关键词： Reinforcement learning(RL) actor-critic experience replay training efficiency manipulation skill learning

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A Visual Leader-Following Approach With a T-D-R Framework for Quadruped Robots

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2021年第4期51卷 2342-2354页

作者： Pang, Lei Cao, Zhiqiang Yu, Junzhi Guan, Peiyu Rong, Xuewen Chai, Hui Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Coll Engn Dept Mech & Engn Sci BIC ESATState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China

The quadruped robot imitates the motions of four-legged animals with a superior flexibility and adaptability to complex terrains, compared with the wheeled and tracked robots. Its leader-following ability is unique to help a human to accomplish complex tasks in a more convenient way. However, long-term following is severely obstructed due to the high-frequency vibration of the quadruped robot and the unevenness of terrains. To solve this problem, a visual approach under a novel T-D-R framework is proposed. The proposed T-D-R framework is composed of a visual tracker based on correlation filter, a person detector with deep learning, and a person re-identification (re-ID) module. The result of the tracker is verified by the detector to improve tracking performance. Especially, the re-ID module is introduced to handle distractions and occlusion caused by other persons, where the convolutional correlation filter (CCF) is employed to discriminate the leader among multiple persons through recording the appearance information in the long run. By comparing the results of the tracker and the detector as well as their similarity scores with the leader identified by the re-ID module, a stable and real-time tracking of the leader can be guaranteed. Experiments reveal that our approach is effective in handling distractions, appearance changes, and illumination variations. A long-distance experiment on a quadruped robot indicates the validity of the proposed approach.

关键词： Visualization Target tracking Mobile robots Correlation Robot kinematics Cameras Detector leader following long-term tracking person re-identification (re-ID) quadruped robot

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