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Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving

Engineering Applications of Artificial Intelligence 2024年 135卷

作者： Azam, Shoaib Munir, Farzeen Kyrki, Ville Kucner, Tomasz Piotr Jeon, Moongu Pedrycz, Witold Department of Electrical Engineering and Automation Aalto University Espoo Finland Finnish Center for Artificial Intelligence Finland School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

Learning contextual and spatial environmental representations enhances autonomous vehicle's hazard anticipation and decision-making in complex scenarios. Recent perception systems enhance spatial understanding with sensor fusion but often lack global environmental context. Humans, when driving, naturally employ neural maps that integrate various factors such as historical data, situational subtleties, and behavioral predictions of other road users to form a rich contextual understanding of their surroundings. This neural map-based comprehension is integral to making informed decisions on the road. In contrast, even with their significant advancements, autonomous systems have yet to fully harness this depth of human-like contextual understanding. Motivated by this, our work draws inspiration from human driving patterns and seeks to formalize the sensor fusion approach within an end-to-end autonomous driving framework. We introduce a framework that integrates three cameras (left, right, and center) to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. The sensor data is fused and encoded using a self-attention mechanism, leading to an auto-regressive waypoint prediction module. We treat feature representation as a sequential problem, employing a vision transformer to distill the contextual interplay between sensor modalities. The efficacy of the proposed method is experimentally evaluated in both open and closed-loop settings. Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset. In closed-loop evaluations on CARLA's Town05 Long and Longest6 benchmarks, the proposed method enhances driving performance, route completion, and reduces infractions. © 2024 The Authors

关键词： Autonomous vehicles

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Improving Hospital Preparedness for Future Pandemics with Machine Learning and Queue Management Solutions

Improving Hospital Preparedness for Future Pandemics with Ma...

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International Conference on Intelligent Computing in Data sciences (ICDS)

作者： Khalil Bouramtane Said Kharraja Jamal Riffi Omar EL Beqqali Saïd Boujraf University of Jean Monnet Saint-Etienne IUT of Roanne LASPI RU ROANNE FRANCE Faculty of science Dhar El Mahraz Laboratory of Computer Science Signals Automation and Cognitivism (LISAC) University Sidi Mohamed Ben Abdellah Fez Morocco Dept. Biophysics & Clinical MRI Methods Faculty of Medicine Clinical Neuroscience Laboratory University of Sidi Mohamed Ben Abdellah Fez Morocco

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

The COVID-19 pandemic has underscored the critical need for resilient and efficient hospital workflows capable of managing sudden increases in patient volume. This study presents a comprehensive approach that integrates advanced machine learning algorithms with queue management techniques to enhance hospital preparedness and response during pandemics. By Employing predictive analytics and classification systems, our methodology improves patient triage, optimizes resource allocation, and reduces wait times, thereby enhancing overall patient care. The study includes a detailed analysis of machine learning models used for patient prioritization and an exploration of queue management strategies to streamline patient flow. Our findings indicate substantial improvements in hospital efficiency and the ability to handle peak demands during health crises. This research provides actionable insights and a robust framework for healthcare administrators aiming to fortify their facilities against future pandemic scenarios, ultimately contributing to better healthcare outcomes.

关键词： Machine learning algorithms Pandemics Hospitals Heuristic algorithms Machine learning Data models Resource management Servers Predictive analytics Queueing analysis

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Hybrid Water Quality Prediction with Graph Attention and Spatio-Temporal Fusion

Hybrid Water Quality Prediction with Graph Attention and Spa...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Lin, Yongze Qiao, Junfei Bi, Jing Yuan, Haitao Gao, Han Zhou, MengChu Beijing University of Technology Faculty of Information Technology Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Chinese Academy of Environmental Planning Beijing100012 China New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Spatio-temporal prediction has a wide range of applications in many fields, e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is also one of spatio-temporal prediction tasks. However, it faces the following challenges: 1) Water quality in river networks has complex spatial dependencies;2) There are complex nonlinear relations in water quality time series;and 3) It is difficult to realize long-term forecasting. To address these challenges, this work proposes a spatio-temporal prediction model called a Graph Attention-based Spatio-Temporal (GAST) neural network. GAST investigates spatial and temporal dependencies of water quality time series. First, we introduce a temporal attention mechanism to capture time series dependencies, which can effectively handle nonlinear relationships in time series. Second, we adopt a spatial attention mechanism to extract spatial dependencies of river networks and fuse temporal features of spatial nodes. Third, we adopt a temporal convolution residual mechanism based on the spatio-temporal fusion, which improves the accuracy of long-term series prediction. This work adopts two real-world datasets to evaluate the proposed GAST and experiments demonstrate that GAST outperforms several state-of-the-art methods in terms of prediction accuracy. © 2022 IEEE.

关键词： Time series

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Data-Driven DLT3 Federated Deep Reinforcement Learning for Secure and Efficient Autonomous Driving

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Rong Li, Qianxi Wang, Kan Khan, Fazlullah Alturki, Ryan Lan, Dapeng Xi’an University of Technology School of Computer and Engineering Xi’an710000 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Ningbo315104 China Umm Al-Qura University College of Computing Department of Software Engineering Makkah21955 Saudi Arabia Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110003 China

As autonomous vehicles (AVs) become increasingly widespread, the intelligent driving control and safety concerns have emerged. Recent advents in Internet of Things (IoTs) and 6G technologies have vastly boosted AVs’ sensing and communication capabilities, thereby enabling the high-speed and real-time data exchange. However, efficiently utilizing data while safeguarding the privacy remains challenges. Thus, we propose a federated learning (FL) framework with the privacy protection to facilitate the deep reinforcement learning (DRL) for longitudinal vehicle safety tracking control. First, the proposed framework enables the efficient information sharing and policy updates via encrypted policy gradient transmission. Then, the twin delayed deep deterministic policy gradient (TD3) method makes the velocity decision in the local model, while the dual long short-term memory (LSTM) network captures the temporal trajectory information. Finally, stacked denoising autoencoders (SDAE) is used to extract high-level environmental features, and the dual-LSTM-TD3 (DLT3)-SDAE model is introduced to enhance the policy decision efficiency and accuracy. Experimental results demonstrate that within the FL framework supported by homomorphic encryption, the DLT3-SDAE-based AV agent surpasses other DRL methods in terms of collision avoidance and average rewards, in the LAF environment. The safety, efficiency and comfort of the DLT3-SDAE AV agent are also evaluated and demonstrated. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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Deep Edge Defense for Industrial Internet Based on Customized Hardware and UOS Architecture 8th

Deep Edge Defense for Industrial Internet Based on Customize...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Wan, Ming Xu, Xinlu Zhao, Jianming Yao, Jiangyuan Lin, Xiu Liu, Tingting Cao, Xingcan School of Information Liaoning University Shenyang110036 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China School of Computer Science and Technology Hainan University Haikou570228 China Faculty of Arts The University of British Columbia VancouverBCV6T 1Z4 Canada

ISBN: (纸本)9783031067631

With the rocketing development of Industrial Internet, the edge control and acquisition networks have become one integral role in determining high-efficiency intelligent manufacturing and flexible production, and the edge security requirements have increasingly drawn widespread attention around the world. In order to strengthen industrial security capabilities, this paper designs a multifunctional deep edge defense system, which is developed on the customized hardware and popular UOS architecture. Furthermore, this system not only supports the basic data routing and forwarding between distinct network boundaries, but also provides some deep content checking and filtering services, which can block malicious attack traffics by setting effective security strategies. Additionally, the multi-scale log audit and authority management can enable the immediate analysis and accurate positioning on abnormal communication behaviors, and further improve the high availability and convenience. The long-term testing shows that this system has fine stability and robust defense capabilities under the comprehensive testing which integrates with different types of data traffics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network security

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DGLNet: A Cross-Modal Global-Local Collaborative Network for Chest Radiology Report Generation

SSRN

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SSRN 2022年

作者： Liu, Lijun Wang, Lizhen Liu, Li Peng, Wei Fu, Xiaodong Huang, Qingsong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Department of Computer Science and Engineering School of Information Science and Engineering Yunnan University Kunming650228 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

Automatic chest radiology report generation is crucial for improving diagnostic efficiency, enhancing diagnostic quality, and reducing misdiagnoses or missed diagnoses. Existing approaches either resize the high-resolution radiology image into smaller sizes or segment it into multiple patches for separate processing, which may lose fine local details or global contextual information, and the accuracy of report generation will be limited. In addition, the direct fusion of visual features with sentence features reduces the accuracy of aligning fine-grained visual features with text features. In this paper, we focus on designing a cross-modal global-local collaborative network to capture more local fine details and global contextual information, which comprises a global-to-local network and a local-to-global network that takes the resized entire image and segmented image patches as respective inputs. The former handles the extraction of global visual features, the fusion of global and local visual features, and the fusion of visual features into report sentences. The latter implements the extraction of local visual features, the fusion of local to global visual features, and the fusion of visual features to report words. Experimental results on two publicly available datasets, IU X-Ray and MIMIC-CXR, show that our proposed network outperforms state-of-the-art image captioning-based methods in most evaluation metrics. © 2022, The Authors. All rights reserved.

关键词： Extraction

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Integrating FACTS technologies into renewable energy systems: potential and challenges

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International Journal of Ambient Energy 2024年第1期45卷

作者： Nazir, Muhammad Shahzad Ullah, Hayat Larik, Nauman Ali Chu, Zhang Tian, Peng Sohail, Hafiz M. Raute, Reiko Faculty of Automation Huaiyin Institute of Technology Huai’an China Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada Concordia University Montreal Canada School of Electrical Power South China University of Technology Guangzhou China School of Economics and Management South China Normal University Guangzhou China Department of Electrical Engineering Faculty of Engineering University of Malta Msida Malta

The proliferation of renewable energy systems into smart grids is becoming increasingly vital as the globe continues to shift toward sustainable energy sources. However, renewable energy’s intermittent nature can cause power quality complications such as voltage swings and frequency irregularities. Interestingly, FACTS technologies can provide improved control facilities for power flow and voltage regulation. The global FACTS market worthed $1.18 billion in 2020 and is predicted to be $1.91 billion by 2026, expanding at 7.5%. This study entertains FACTS technologies and their integration into renewable energy systems. This study deliberates the function of several FACTS devices, including Static Var Compensators, Static Synchronous Compensators, and Unified Power Flow Controllers, in enhancing power quality. The study also expanded to the problems and opportunities that come with integrating FACTS technologies into renewable energy systems. This study offers the current state of FACTS technologies and their prospective applications for improving power quality integrated renewable energy systems issues. The review will be an interesting useful source for industry and academia researchers in power systems and renewable energy. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： FACTS technologies power quality green energy utilisation clean energy smart grid renewable energy sources

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Lower Extremity Exoskeleton Simulation Model with Variable Stiffness Actuation for Vertical Locomotion 3

Lower Extremity Exoskeleton Simulation Model with Variable S...

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3rd International Conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2021

作者： Kondratyev, Sergey Cruypeninck, Lennert Tsareva, Anastasia Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia Katholieke Universiteit Leuven Department of Mechanical Engineering Leuven Belgium ITMO University International Research Center SCAMT Saint-Petersburg Russia

ISBN: (纸本)9781665439817

This paper proposes a lower extremity exoskeleton simulation model for more effective control in vertical locomotion when patients need to move from static seating configuration to walking-ready configuration. Variable impedance actuators (VIA) or variable stiffness actuators (VSA) are considered as the primary source of generating and translating controlled torque values with elastic compliance properties to movement elements of a discussed system using PID control with dynamic compensation. The model-applied torque can be divided into two torques, one about gravity compensation and another about acceleration. The simulink environment with simscape library was used to create a simulation process of the working mechanism. Data on angles, torques, and velocities were obtained and considered for correctness in natural human locomotion. Calculations provide upper limit for the required torque to stand up, which can be used as a basis to select a proper actuator. © 2021 IEEE.

关键词： Actuators

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A Soft Micro-Robotic Catheter for Aneurysm Treatment: A Novel Design and Enhanced Euler-Bernoulli Model with Cross-Section Optimization

A Soft Micro-Robotic Catheter for Aneurysm Treatment: A Nove...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Emanuele Nicotra Chi Cong Nguyen James Davies Phuoc Thien Phan Trung Thien Hoang Bibhu Sharma Adrienne Ji Kefan Zhu Trung Dung Ngo Van Anh Ho Hung Manh La Nigel H. Lovell Thanh Nho Do Graduate School of Biomedical Engineering Faculty of Engineering UNSW Sydney Kensington Campus Australia More-Than-One Robotics Laboratory Faculty of Sustainable Design Engineering University of Prince Edward Island Charlottetown Canada Soft Haptics Laboratory School of Materials Science Japan Advanced Institute of Science and Technology Kawaguchi Saitama Japan Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Aneurysms, balloon-like bulges in blood vessels, present a significant health risk due to their potential to rupture, leading to life-threatening internal bleeding. Current treatments often involve delivering embolic materials or metal coils to fill these bulges, occluding them from the pressure of blood flow. However, clinical micro-catheters that deploy embolic materials used today face limitations, primarily their rigidity and the lack of active control over the bending tip of the catheter. This paper introduces a new soft micro-robotics catheter, with diameter of only 0.8 mm, equipped with a hollow channel. With this new design, the new device can induce bending motions at its tip for active steerability to reach desired aneurysm targets and then perform the delivery of embolic materials and tools. To enhance the control and precise navigation during procedures, a robust mathematical model and image processing techniques are also introduced and validated. Experiments are also performed to characterise and validate the model’s accuracy and the steerability and navigation capabilities of the new micro-catheter.

关键词： Performance evaluation Navigation Metals Aneurysm Bending Mathematical models Rigidity

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A New Deep Fuzzy Based MSER Model for Multiple Document Images Classification 3rd

A New Deep Fuzzy Based MSER Model for Multiple Document Imag...

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3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Biswas, Kunal Shivakumara, Palaiahnakote Sivanthi, Sittravell Pal, Umapada Lu, Yue Liu, Cheng-Lin Ayub, Mohamad Nizam Bin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy Beijing China

ISBN: (纸本)9783031090363

Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate. © 2022, Springer Nature Switzerland AG.

关键词： Image classification

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