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IEEE Symposium on Intelligent Vehicle

作者： Caiji Zhang Bin Tian Shi Meng Shuangying Qi Yang Sun Yunfeng Ai Long Chen School of Artificial Intelligence University of Chinese Academy of Sciences and With the National Key Laboratory for Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Bin Tian is With the National Key Laboratory for Multi-Modal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and With the School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China National Key Laboratory for Multi-Modal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and With the School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Chongqing Iron and Steel Group Mining Co. Ltd. Chongqing China School of Mechanical and Equipment Engineering Hebei University of Engineering Handan China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Waytous Inc. Qingdao China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Waytous Ltd. Qingdao China

ISBN: (数字)9798350348811

ISBN: (纸本)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： Point cloud compression Visualization Three-dimensional displays Laser radar Roads Collaboration Object detection

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Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives

Learning Pre-Grasp Manipulation of Flat Objects in Cluttered...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Jiaxi Wu Haoran Wu Shanlin Zhong Quqin Sun Yinlin Li State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China Department of Automation University of Science and Technology of China Heifei China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China Science and Technology on Thermal Energy and Power Laboratory Wuhan China

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.

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Spiking Adaptive Dynamic Programming with Poisson Process 12th

Spiking Adaptive Dynamic Programming with Poisson Process

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12th International Conference on Advances in Swarm intelligence, ICSI 2021

作者： Wei, Qinglai Han, Liyuan Zhang, Tielin The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Research Center for Brain-Inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783030788100

A new iterative spiking adaptive dynamic programming (SADP) algorithm based on the Poisson process for optimal impulsive control problems is investigated with convergence discussion of the iterative process. For a fixed time interval, a 3-tuple can be computed, and then the iterative value functions and control laws can be obtained. Finally, a simulation example verifies the effectiveness of the developed algorithm. © 2021, Springer Nature Switzerland AG.

关键词： Dynamic programming

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Revisiting Marr in Face: The Building of 2D-2.5D-3D Representations in Deep Neural Networks

arXiv

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arXiv 2024年

作者： Zhu, Xiangyu Yu, Chang Zhao, Jiankuo Zhang, Zhaoxiang Li, Stan Z. Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China AI Lab. Research Center for Industries of the Future Westlake University China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong

David Marr’s seminal theory of vision proposes that the human visual system operates through a sequence of three stages, known as the 2D sketch, the 2.5D sketch, and the 3D model. In recent years, Deep Neural Networks (DNN) have been widely thought to have reached a level comparable to human vision. However, the mechanisms by which DNNs accomplish this and whether they adhere to Marr’s 2D–2.5D–3D construction theory remain unexplored. In this paper, we delve into the face perception task to explore these questions and find evidence supporting Marr’s theory. We introduce a graphics probe, a sub-network crafted to reconstruct the original face image from the network’s intermediate layers. The key to the graphics probe is its flexible architecture that supports image reconstruction in both 2D and 3D formats, as well as in a transitional state between them. By injecting graphics probes into neural networks, and analyzing their behavior in reconstructing images, we find that DNNs initially encode images as 2D representations in low-level layers, and finally construct 3D representations in high-level layers. Intriguingly, in mid-level layers, DNNs exhibit a hybrid state, building a geometric representation that captures surface normals within a narrow depth range, akin to the appearance of a low-relief sculpture. This stage resembles the 2.5D representations, providing a view of how DNNs evolve from 2D to 3D in the perception process. The graphics probe therefore serves as a tool for peering into the mechanisms of DNN, providing empirical support for Marr’s theory. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Consistent Extended Kalman Filter Design for Maneuvering Target Tracking and Its Application on Hand Position Tracking

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Guidance, Navigation and Control 2022年第2期2卷 33-58页

作者： Lin Tian Yang Xu Wenchao Xue Long Cheng The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China School of Arti-cial Intelligence University of Chinese Academy of SciencesBeijing 100049P.R.China The Key Laboratory of Systems and Control National Center for Mathematics and Interdisciplinary SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190P.R.China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049P.R.China

This paper propees the consistent extended Kalman flter(CEKF)for the maneuvering target tracking(MTT)with nonlinear uncertain dynamics,and applies it on hand poition *** general modlel of the MTT system i presented with unmodleled dynamics in terms of nonlinear unknown function of *** CEKF is propoeed to ensure that the bounds of the estimation error's covariance matrix are av ailable through the flter *** a result,the creponding accuracy of the flter approach can be achieved ***,a CEKF-baaed MTT algorithm is constructed via the tumning aw of the critical parameter matrix QE Finally,the efectiveness of CEKF i verified by MTT numerical simulations and hand tacking expeiments under dilferent ***,two indices are employed to compare the CEKF with extended Kalman filter(EKF):the mean square errors(MSEa)and the bounded percentage,ie the percentage of the rang w bere the estimation error is encboed by the bound calculated by *** MSEs of CEKF are smaller than thoee of EKF,where the worst MSEa of CEKF and EKF are0.14 and 418 in the simulation,a8 well 80.11 and 059 in the expeiments,respectively;all bounded percentages of CEKF are larger than thoee of EKF,where the wonst average bounded percentages of CEKF and EKF ame 87.86%and 14.58%,8 well as 97.41%and 41.79%in the experiments,reapectively.

关键词： Maneuvering target tracking hand position tracking extended Kalman¯lter(EKF) consistency

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Ruijie Xu Shichao Chen Wenqiao Sun Yisheng Lv Jialiang Luo Ying Tang The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems The Center of National Railway Intelligent Transportation System Engineering and Technology Institute of Automation Chinese Academy of Sciences China Academy of Railway Sciences Corporation Limited Beijing China China Academy of Railway Sciences Corporation Limited Transportation and Economics Research Institute and the Center of National Railway Intelligent Transportation System Engineering and Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing Beijing China Department of Electrical and Computer Engineering Rowan University Glassboro USA

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance.

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Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1752-1762页

作者： Xianglei Zhu Wen Hu Zejian Deng Jinwei Zhang Fengqing Hu Rui Zhou Keqiu Li Fei-Yue Wang the College of Intelligence and Computing Tianjin UniversityTianjin 300350 the China Automotive Technology and Research Center Co.Ltd. Tianjin 300300China the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterlooON N2L3G1Canada the School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China the Macao University of Science and Technology MacaoChina the Waytous Inc. Qingdao 266000China IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by *** improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic *** integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and *** dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference *** on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax ***,the comparative analysis is performed to validate the proposed method using the naturalistic driving *** results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

关键词： Cut-in behavior interaction-aware mixed traffic risk assessment trajectory prediction

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Traj-LLM: A New Exploration for Empowering Trajectory Prediction With Pre-Trained Large Language Models

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Lan, Zhengxing Liu, Lingshan Fan, Bo Lv, Yisheng Ren, Yilong Cui, Zhiyong School of Transportation Science and Engineering Beihang University Beijing China Beijing Key Laboratory of Traffic Engineering College of Metropolitan Transportation Beijing University of Technology Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Transportation Science and Engineering State Key Lab of Intelligent Transportation System Beihang University Beijing China

Predicting the future trajectories of dynamic traffic actors is a cornerstone task in autonomous driving. Though existing notable efforts have resulted in impressive performance improvements, a gap persists in scene cognitive and understanding of complex traffic semantics. This paper proposes Traj-LLM, the first to investigate the potential of using pre-trained Large Language Models (LLMs) without explicit prompt engineering to generate future motions from vehicular past trajectories and traffic scene semantics. Traj-LLM starts with sparse context joint encoding to dissect the agent and scene features into a form that LLMs understand. On this basis, we creatively explore LLMs' strong understanding capability to capture a spectrum of high-level scene knowledge and interactive information. To emulate the human-like lane focus cognitive function and enhance Traj-LLM's scene comprehension, we introduce lane-aware probabilistic learning powered by the Mamba module. Finally, a multi-modal Laplace decoder is designed to achieve scene-compliant predictions. Extensive experiments manifest that Traj-LLM, fueled by prior knowledge and understanding prowess of LLMs, together with lane-aware probability learning, transcends the state-of-the-art methods across most evaluation metrics. Moreover, the few-shot analysis serves to substantiate Traj-LLM's performance, as even with merely 50% of the dataset, it surpasses the majority of benchmarks relying on complete data utilization. This study explores endowing the trajectory prediction task with advanced capabilities inherent in LLMs, furnishing a more universal and adaptable solution for forecasting agent movements in a new way. IEEE

关键词： Semantics

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Towards Autonomous Robotic Ultrasound Scanning Using the Reinforcement Learning-Based Volumetric Data Navigation Method

Towards Autonomous Robotic Ultrasound Scanning Using the Rei...

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WRC Symposium on Advanced Robotics and automation (WRC SARA)

作者： Cuifeng Shen Zhaokun Deng Jinzhi Wang Shuangyi Wang Chen Chen Department of Mechanics and Engineering Science College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing P. R. China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong China

Ultrasound scanning is an indispensable diagnostic tool in modern medicine, but obtaining an accurate standard plane relies heavily on the sonographer’s skill and expertise. This paper presents a novel approach toward autonomous robotic ultrasound scanning using a reinforcement learning-based navigation method with volumetric ultrasound data. The proposed method enables an ultrasound robot to autonomously acquire high-quality 2D standard planes from patients, thereby reducing the need for human intervention. The core of the approach is a deep reinforcement learning algorithm that detects 2D standard planes within the 3D ultrasound volume and guides the ultrasound probe to extract high-quality standard planes. To assess the effectiveness of the 2D standard plane detection method, an evaluation was conducted using a realworld ultrasound dataset, and a comparison was made between its performance and that of other state-of-the-art autonomous approaches. The simulation experiment was also conducted to validate the autonomous robotic ultrasound acquisition workflow. Results demonstrate that the reinforcement learning-based volumetric data navigation method outperforms existing techniques in terms of standard plane accuracy and is very effective for volumetric data-based robotic navigation.

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Making a Higher Hit Ratio Cryptanalytic Time-Memory Trade-Off Attack on Passwords

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Chinese Journal of Electronics 2023年第4期22卷 671-676页

作者： ZOU Jing LIN Dongdai HAO Chunhui LI Zheuqi WANG Wenhao LU Yao State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Graduate University of Chinese Academy of Sciences Beijing China Huaiyin Normal University Huai'an China Laboratory of Complex Systems and Intelligence Science Institute Automation Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China

Most of implementations of the cryptanalytic time-memory trade-off attacks such as Hellman's original method, Rivest's distinguished points cracking and Oechslin's rainbow attack are also considered as an exhaustive attack to passwords in a limited length range on a certain charset. However, the distributions of structures and strings making up real human memorable passwords do not appear random. Based upon these, we propose a method to generate passwords in those cryptanalytic timememory trade-off methods. It achieves a higher hit ratio in attacking actual passwords and reduces search space drastically with requirement of only a little extra memory. It makes time-memory trade-off more practical. Even to attack long length passwords, the results of experiments show that our approach has a higher hit ratio compared with Oechslin's method. In addition, this method can be used in the distributed and parallel attack.

关键词： Dictionaries Memory management Passwords

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