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25th International Conference on Pattern Recognition (ICPR)

作者： Wu, Jinting Zhang, Yujia Zhao, Xiaoguang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728188089

Hand gesture recognition plays a significant role in human-computer interaction for understanding various human gestures and their intent. However, most prior works can only recognize gestures of limited labeled classes and fail to adapt to new categories. The task of Generalized Zero-Shot Learning (GZSL) for hand gesture recognition aims to address the above issue by leveraging semantic representations and detecting both seen and unseen class samples. In this paper, we propose an end-to-end prototype-based GZSL framework for hand gesture recognition which consists of two branches. The first branch is a prototype-based detector that learns gesture representations and determines whether an input sample belongs to a seen or unseen category. The second branch is a zero-shot label predictor which takes the features of unseen classes as input and outputs predictions through a learned mapping mechanism between the feature and the semantic space. We further establish a hand gesture dataset that specifically targets this GZSL task, and comprehensive experiments on this dataset demonstrate the effectiveness of our proposed approach on recognizing both seen and unseen gestures.

关键词： Zero-shot learning

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A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios

A Reinforcement Learning Benchmark for Autonomous Driving in...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Liu, Yuqi Zhang, Qichao Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728190488

In recent years, control under urban intersection scenarios has become an emerging research topic. In such scenarios, the autonomous vehicle confronts complicated situations since it must deal with the interaction with social vehicles timely while obeying the traffic rules. Generally, the autonomous vehicle is supposed to avoid collisions while pursuing better efficiency. The existing work fails to provide a framework that emphasizes the integrity of the scenarios while deploying and testing reinforcement learning(RL) methods. Specifically, we propose a benchmark for training and testing RL-based autonomous driving agents in complex intersection scenarios, which is called RL-CIS. Then, a set of baselines consisting various algorithms are deployed. The test benchmark and baselines provide a fair and comprehensive training and testing platform for the study of RL for autonomous driving in the intersection scenario, advancing RL-based methods for autonomous driving control. The code of our proposed framework can be found at https://***/liuyuqi123/complexUrbanScenarios.

关键词： autonomous driving reinforcement learning intersection scenarios decision-making

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Detection of Driver Fatigue state using Deep Neural Network 16

Detection of Driver Fatigue State using Deep Neural Network

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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Anwar, Noreen Xiong, Gang Guo, Miao Ye, Peijun Ali, Hub Wei, Qinglai Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Beijing Univ Technol Sch Artificial Intelligence & Automat Beijing Peoples R China

ISBN: (纸本)9781665422482

Image-based driver fatigue detection remains a challenging problem due to occlusion of face, the variation of head poses and illuminations. This paper implements an effectual technique for investigating the driver's fatigue state by using infrared image of an eye in the open or closed condition. In this method we use the deep learning technique to monitor the change, i.e., open and closed conditions of eye state. We integrate ResNet and depthwise convolution network together and use as the core of the structure of the network to perform face and facial landmark detection tasks. After acquiring the eye region, we perform the eye state identification task by using its coordinates of feature points. To determine fatigue, we use PERCLOS method and the results confirm accuracy and effectiveness of the algorithm by comparing with other existing methods. We can reach an accuracy of 97.2% and the average time is 31.20 milliseconds, represent that this driver monitoring inference system has significant importance for both the traffic and driver's safety.

关键词： driver fatigue detection deep neural network ResNet depthwise convolution network PERCLOS

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Traffic Flow Synthesis Using Generative Adversarial Networks via Semantic Latent Codes Manipulation

Traffic Flow Synthesis Using Generative Adversarial Networks...

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IEEE Intelligent Transportation systems Conference (ITSC)

作者： Chen, Yuanyuan Lv, Yisheng Zhu, Fenghua Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Beijing 100190 Peoples R China

ISBN: (纸本)9781728191423

Traffic data analysis and mining are elemental functions of Intelligent Transportation systems. In recent year, tremendous sensors are deployed in order to collect big data, and equipment maintenance costs a lot. With the development of deep learning, especially especially Generative Adversarial Networks, we can generate realistic big artificial traffic flow data and use small real traffic data and synthesized traffic data in traffic data mining tasks. In this paper, we focus on discovering the semantics embedded in latent codes which are fed into Generative Adversarial Networks, and propose to use the interpolation of semantic latent code to generate semantic manipulation of traffic flow. We evaluate our approach using the publicly available data from Caltrans Performance Measurements systems (PeMS), and experimental results show the the effectiveness of the proposed method.

关键词： Training Measurement Interpolation Codes Semantics Maintenance engineering Generative adversarial networks

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Accident prediction in mesoscopic view: A CPSS-based social transportation approach 1

Accident prediction in mesoscopic view: A CPSS-based social ...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Sun, Chen Tan, Ruichen Deng, Jie Zhou, Rui Chen, Long Wang, Fei-Yue Cao, Dongpu University of Waterloo Dept. of MME Waterloo Canada Waytous Inc. Qingdao China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665433372

In modern society, traffic accidents are becoming an essential social safety issue that cannot be ignored. Along with the convenience of the high-speed development of modernization, the prosperity of vehicles has also led to severe traffic accidents, which cause massive loss of lives and properties every year. Accident analysis and prediction based on big data have become increasingly important in social transportation. However, the traditional data analysis and prediction methods only focused on either macroscope(transportation) or microscope(vehicle), and the relationships between the two layers are missing. This paper proposes a novel analysis framework by introducing the mesoscopic factors to bridge the transportation and vehicle operation domain under the parallel driving setting. The feature aggregation and mesoscopic factors extraction are included in the proposed prediction strategy to improve the performance of various commonly used decision tree-based classifiers. Methods are compared under the most recent 5-year accident database, which stores over three million records. Insights are discussed based on the future application of connected autonomous vehicles and intelligent road infrastructures. © 2021 IEEE.

关键词： Forecasting

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A Long-Tail Regularization Method for Traffic Sign Recognition Based on Parallel Vision

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 957-961页

作者： Wang, Jiangong Wang, Xiao Shen, Tianyu Wang, Yutong Tian, Yonglin Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

The long-tail effect is prevalent in the actual world, and the resulting long-tail problem hinders the development of intelligent vehicles towards a more reliable and safe direction. In previous studies, the theory of Long-tail Regularization (LoTR) based on parallel vision was proposed, and further research and experiments on the long-tail problem of traffic sign recognition are conducted in this paper. Specifically, in the process of long-tail regularization for traffic sign recognition, a novel generative adversarial network is designed and improved to generate images. By feeding the category-specific image from standard gallery into the proposed network, it can generate a large number of virtual images with the same category. Finally, the generated virtual images are added to the original dataset to regularize the long tail, and then the recognition accuracy is significantly improved.

关键词： Parallel vision long tail traffic sign regularization

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Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第12期23卷 23694-23707页

作者： Liang, Maohan Liu, Ryan Wen Zhan, Yang Li, Huanhuan Zhu, Fenghua Wang, Fei-Yue Wuhan Univ Technol Sch Nav Wuhan 430063 Peoples R China Natl Engn Res Ctr Water Transport Safety Wuhan 430063 Peoples R China Liverpool John Moores Univ Sch Engn Technol & Maritime Operat Liverpool L3 3AF Merseyside England Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The accurate and robust prediction of vessel traffic flow is gaining importance in maritime intelligent transportation system (ITS), such as vessel traffic services, maritime spatial planning, and traffic safety management, etc. To achieve fine-grained vessel traffic flow prediction, we will first generate the maritime traffic network (which is essentially a graph), and then propose a graph-driven neural network. In particular, to represent various correlations among spatio-temporal vessel traffic flow, we tend to extract the feature points (i.e., starting, way and ending points) by utilizing the knowledge of vessel positioning data. These feature points are essentially related to the geometrical structures of massive vessel trajectories collected from massive automatic identification system (AIS) records, contributing to the generation of maritime traffic network. We then propose a spatio-temporal multi-graph convolutional network (STMGCN)-based vessel traffic flow prediction method by exploiting multiple types of inherent correlations in the generated maritime graph. The proposed STMGCN mainly contains one spatial multi-graph convolutional layer and two temporal gated convolutional layers, beneficial for extracting spatial and temporal traffic flow patterns. The main benefit of our graph-driven prediction method is that it takes full advantage of the maritime graph and multi-graph learning. Comprehensive experiments have been implemented on realistic AIS dataset to compare our method with several state-of-the-art prediction methods. The fine-grained prediction results have demonstrated our superior performance in terms of both accuracy and robustness.

关键词： Traffic flow prediction maritime traffic network multi-graph convolutional network automatic identification system (AIS)

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An Open-Source, Fiducial-Based, Underwater Stereo Visual-Inertial Localization Method with Refraction Correction

An Open-Source, Fiducial-Based, Underwater Stereo Visual-Ine...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhang, Pengfei Wu, Zhengxing Wang, Jian Kong, Shihan Tan, Min Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex SystBIC ESAT Beijing 100871 Peoples R China

ISBN: (纸本)9781665417143

Underwater visual localization is an essential technique for the autonomous operation of underwater robots. However, the unique underwater image characteristics, including refraction, sparse features, and severe noise, pose an enormous challenge to it. For addressing these issues, this paper proposes an open-source fiducial-based underwater stereo visual-inertial localization method under the extended Kalman filter (EKF) framework, which is called FBUS-EKF. First, the refraction is corrected by the refractive camera model and akin triangulation. Second, the fiducial marker and a novel marker pose estimation method are applied to alleviate the adverse effect of sparse features. Third, the EKF is utilized to fuse the inertial and visual information so as to reject the serious noise. Finally, extensive experiments on a test bench demonstrate the effectiveness of the FBUS-EKF method, where the typical localization error is less than 3%, namely, the average error is lower than 3 cm within one meter. The obtained results reveal that the FBUS-EKF method has the prospect to be applied in the precise short-range operation and the localization for underwater robots, which offers a valuable insight for further autonomous underwater task.

关键词： Location awareness Meters Visualization Autonomous underwater vehicles Fuses Pose estimation Kalman filters

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Three-Dimensional Force Perception of Robotic Bipolar Forceps for Brain Tumor Resection

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JOURNAL OF MEDICAL DEVICES-TRANSACTIONS OF THE ASME 2021年第3期15卷 031013页

作者： Zhang, Xiu-Heng Zhang, Heng Li, Zhen Bian, Gui-Bin Shenyang Ligong Univ Sch Mech Engn Shenyang 110159 Liaoning Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Three-dimensional force perception is critically important in the enhancement of human force perception to minimize brain injuries resulting from excessive forces applied by surgical instruments in robot-assisted brain tumor resection. And surgeons are not responsive enough to interpret tool-tissue interaction forces. In previous studies, various force measurement techniques have been published. In neurosurgical scenarios, there are still some drawbacks to these presented approaches to forces perception. Because of the narrow, and slim configuration of bipolar forceps, three-dimensional contact forces on forceps tips are not easy to be traced in real-time. Five fundamental acts of handling bipolar forceps are poking, opposing, pressing, opening, and closing. The first three acts independently correspond to the axial force of z, x, y. So, in this paper, typical interactions between bipolar forceps and brain tissues have been analyzed. A three-dimensional force perception technique to collect force data on bipolar forceps tips by installing three fiber Bragg grating sensors (FBGs) on each prong of bipolar forceps in real-time is proposed. Experiments using a tele-neurosurgical robot were performed on an in vitro pig brain. In the experiments, three-dimensional forces were tracked in real-time. It is possible to experience forces at a minimum of 0.01 N. The three-dimensional force perception range is 0-4 N. The calibrating resolution on x, y, and z, is 0.01, 0.03, 0.1 N, separately. According to our observation, the measurement accuracy precision is over 95%.

关键词： Biological tissues Brain Sensors Tumors Surgery Calibration Robots Wavelength Robotics Stress

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A Survey on Attack Detection and Resilience for Connected and Automated Vehicles: From Vehicle Dynamics and control Perspective

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2022年第4期7卷 815-837页

作者： Ju, Zhiyang Zhang, Hui Li, Xiang Chen, Xiaoguang Han, Jinpeng Yang, Manzhi Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Eversec Technol Co Ltd Beijing 100098 Peoples R China Macao Univ Sci & Technol Macau 999078 Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Recent advances in attack/anomaly detection and resilience strategies for connected and automated vehicles (CAVs) are reviewed from vehicle dynamics and control perspective. Compared to traditional vehicles, CAVs are featured in the increasing number of perception sensors, advanced intra-vehicle communication technologies, capabilities of driving automation and connectivity between single vehicles. These features bring about safety issues which are not encountered in traditional vehicle systems. One main type of these issues is the attack or anomaly launched onto the perception sensors and the communication channels. With such a consideration, this survey summarizes and reviews the existing results on attack/anomaly detection and resilience of CAVs in control frameworks. This paper reviews the results according to the positions at which the attacks/anomalies occur. These positions are divided into three categories, namely, intra-vehicle communication network, perception sensors and inter-vehicle communication network. From this perspective, the recent attack/anomaly detection and resilience results are reviewed according to different positions attacked. After reviewing existing results, some potential research directions and challenges are identified.

关键词： Connected vehicles Anomaly detection Connected and automated vehicles attack anomaly detection resilience strategy safety and security

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