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Prospective Role of Foundation Models in Advancing Autonomous Vehicles

Research 2025年第1期7卷 667-691页

作者： Jianhua Wu Bingzhao Gao Jincheng Gao Jianhao Yu Hongqing Chu Qiankun Yu Xun Gong Yi Chang H Eric Tseng Hong Chen Jie Chen School of Automotive Studies Tongji UniversityShanghai 201804China Frontiers Science Center for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China SAIC Intelligent Technology Shanghai 201805China College of Artificial Intelligence Jilin UniversityChangchun 130012China Research and Advanced Engineering Ford Motor CompanyDearbornMI 48124USA College of Electronic and Information Engineering Tongji UniversityShanghai 201804China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai 201210China

With the development of artificial intelligence and breakthroughs in deep learning,large-scale foundation models(FMs),such as generative pre-trained transformer(GPT),Sora,etc.,have achieved remarkable results in many fields including natural language processing and computer *** application of FMs in autonomous driving holds considerable *** example,they can contribute to enhancing scene understanding and *** pre-training on rich linguistic and visual data,FMs can understand and interpret various elements in a driving scene,and provide cognitive reasoning to give linguistic and action instructions for driving decisions and ***,FMs can augment data based on the understanding of driving scenarios to provide feasible scenes of those rare occurrences in the long tail distribution that are unlikely to be encountered during routine driving and data *** enhancement can subsequently lead to improvement in the accuracy and reliability of autonomous driving *** testament to the potential of FMs'applications lies in world models,exemplified by the DREAMER series,which showcases the ability to comprehend physical laws and *** from massive data under the paradigm of self-supervised learning,world models can generate unseen yet plausible driving environments,facilitating the enhancement in the prediction of road users'behaviors and the off-line training of driving *** this paper,we synthesize the applications and future trends of FMs in autonomous *** utilizing the powerful capabilities of FMs,we strive to tackle the potential issues stemming from the long-tail distribution in autonomous driving,consequently advancing overall safety in this domain.

关键词： autonomous driving computer visionthe artificial intelligence natural language processing deep learninglarge scale autonomous vehicles understand interpr enhancing scene understanding reasoningby

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Spatial data intelligence and city metaverse: A review

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Fundamental Research 2025年第3期5卷 1169-1193页

作者： Meng, Xiaofeng Li, Yong Liu, Ke Liu, Yu Yang, Bin Song, Xuan Liao, Guoqiong Wang, Senzhang Yu, Ziqiang Chen, Longbiao Pan, Xiao Lin, Yuming School of Information Renmin University of China Beijing 100872 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Information Sciences National Natural Science Foundation of China Beijing 100085 China Institute of Remote Sensing and Geographic Information Systems School of Earth and Space Sciences Peking University Beijing 100871 China School of Data Science and Engineering East China Normal University Shanghai 200062 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China School of Information Management Jiangxi University of Finance and Economics Nanchang 330013 China School of Computer Science and Engineering Central South University Changsha 410017 China School of Computer and Control Engineering Yantai University Yantai 264005 China Department of Computer Science Xiamen University Xiamen 361000 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang 050043 China

Spatial Data Intelligence (SDI) encompasses acquiring, storing, analyzing, mining, and visualizing spatial data to gain insights into the physical world and uncover valuable knowledge. These understandings and knowledge play a crucial role in connecting physical and virtual realms, such as in developing a City Metaverse (CM) aimed at enhancing and optimizing modern urban environments. The advancement of CM holds immense potential to benefit urban dwellers, making research on SDI an increasingly prominent area of focus. This paper contributes significantly by organizing the relevant research and technologies within a coherent framework. Firstly, we identify SDI technologies capable of collecting real-world information to construct a virtual CM. Subsequently, we delve into the technologies that can be compositely integrated with SDI to facilitate interaction with and management of actual cities from the virtual perspective. Additionally, we emphasize the effectiveness and potential of these methods in practical applications. Lastly, we conclude our survey by discussing emerging challenges associated with technological progress, the industrial chain, legal and regulatory aspects, and ethical and moral considerations. © 2024

关键词： Application prospect City metaverse Smart city Spatial data intelligence Virtual-real interaction

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A novel deep domain-adversarial method based on DRN-DATL for rolling bearing 43

A novel deep domain-adversarial method based on DRN-DATL for...

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43rd Chinese Control Conference, CCC 2024

作者： Li, Zhuorui Wang, Xiaodong Ma, Bao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9789887581581

In recent years, a variety of rolling bearing fault diagnosis methods based on deep learning has become an emerging research orientation. However, there is still a gap between the existing diagnostic model and the practical application, i.e., 1) due to the variability of working conditions, the domain shift problem between source domain and the target domain have led to the obvious degradation of the diagnostic performance. 2) It is time-consuming and expensive to label a mass of monitoring data in practical situation. To address this problem, a domain-adversarial transfer learning method based on deep residual network (DRN-DATL) for rolling bearing fault diagnosis is proposed. Firstly, DRN is designed to extract features from raw vibration signal, which can avoid gradient problems. Then, the APReLU activation function is adopted to improved diagnostic accuracy under different working conditions. Meanwhile, domain-adversarial training is utilized to minimize distribution differences between source domain and target domain. Finally, seven comparison methods are selected for systematic analysis, and the results show that the proposed method can effectively improve the overall performance of the diagnosis model, which has achieved higher classification accuracy. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Adversarial machine learning

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Separation of chitin from shrimp shells enabled by transition metal salt aqueous solution and ionic liquid

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Chinese Journal of Chemical engineering 2023年第1期53卷 133-141页

作者： Mi Feng Bin He Xinyan Chen Junli Xu Xingmei Lu Cai Jia Jian Sun Key Laboratory of Molecular Medicine and Biotherapy Ministry of Industry and Information TechnologySchool of Life SciencesBeijing Institute of TechnologyBeijing 100081China Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering Ministry of Industry and Information TechnologyAdvanced Research Institute of Multidisciplinary ScienceBeijing Institute of TechnologyBeijing 100081China CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex SystemsState Key Laboratory of Biochemical EngineeringBeijing Key Laboratory of Ionic Liquids Clean ProcessInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China School of Chemical and Engineering University of Chinese Academy of SciencesBeijing 100049China Innovation Academy for Green Manufacture Chinese Academy of SciencesBeijing 100190China

Chitin is a widely used important industrial polymer mainly from shrimp shells, but its commercial preparation is under the great challenge of serious pollution due to the requirement of HCl and Na ***, we demonstrated that high purity chitin can be obtained from waste shrimp shells(WSSs) by cascade separation with transition metal salt aqueous solution and ionic liquid(IL). Firstly, calcium carbonate of WSSs was effectively removed in the metal salt aqueous solution driven by the ion exchange interaction. Subsequently, 1-butyl-3-methylimidazolium chloride([Bmim]Cl) had bifunctional abilities to remove residual protein and introduced metal salts simultaneously by hydrogen bonding and coordination interactions. The key experimental factors affecting the separation process were systematically studied, including the type of metal salts, temperature, and [Bmim]Cl loading. After sequential treatment with a 20%(mass) Ni SO4aqueous solution at 130 ℃ and [Bmim]Cl at 150 ℃, the purity of a-chitin can be up to 96.5%(mass) that meets commercial requirements. The use of metal salts with higher coordination ability makes the preparation of chitin no longer depend on the commonly acid-base reaction, which is conducive to the preservation of chitin structure.

关键词： Chitin Ionic liquids Transition metal salts Separation Sustainability

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An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

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Computers, Materials & Continua 2022年第5期71卷 4151-4166页

作者： Maha Farouk S.Sabir Irfan Mehmood Wafaa Adnan Alsaggaf Enas Fawai Khairullah Samar Alhuraiji Ahmed S.Alghamdi Ahmed A.Abd El-Latif Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversitySaudi Arabia Centre for Visual Computing Faculty of Engineering and InformaticsUniversity of BradfordBradfordU.K Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahP.O.Box 23713Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia University32511Egypt

Today,due to the pandemic of COVID-19 the entire world is facing a serious health *** to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of *** governmental bodies of different countries imposed that wearing a face mask is compulsory in public ***,it is very difficult to manually monitor people in overcrowded *** research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of *** paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or *** proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked ***,this work applies to real-time and can be implemented in any public service area.

关键词： COIVD-19 deep learning faster-RCNN object detection transfer learning face mask

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An Explicit Construction of a Coded Caching Scheme with Heterogeneous Cache Sizes 18

An Explicit Construction of a Coded Caching Scheme with Hete...

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18th International Symposium on information Theory and Its Applications, ISITA 2024

作者： Nagaya, Akihito Koga, Hiroki Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki305-8573 Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki305-8573 Japan

ISBN: (纸本)9784885523519

In the coded caching scheme proposed by Maddah-Ali and Niesen, we usually consider the setup where K users have respective cache memories of equal size and request arbitrary one of N files to a server. The server broadcasts a signal to the K users so that each user can reproduce the requested file from the transmitted signal together with the contents in their cache memory. Finding the memory-rate tradeoff is one of the fundamental problems in the coded caching problem. In this paper, we extend the coded caching problem to the case where K users have cache memories of unequal sizes. We succeed in giving a new upper bound of the memory-rate tradeoff under a certain assumption. We establish the upper bound by developing a new scheme in which N files are divided into sub files of unequal sizes according to the sizes of cache memories. The validity of this scheme is established by using combinatoric arguments. We also show that adding new users with no cache memory to the K users can reduce the rate of the transmitted signal. © 2024 The Institute of Electronics, information and Communication Engineers.

关键词： Cache memory

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： Computer vision

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Gated Recurrent Unit-Based Network Traffic Prediction for Load Balance 16

Gated Recurrent Unit-Based Network Traffic Prediction for Lo...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Chen, Jiabao Li, Shuiqiang Chen, Jinyi Sun, Jiachen Wang, Dongyang Fang, Chao University of Technology School of Computer Beijing Beijing China Beijing Institute of Astronautical Systems Engineering Beijing China Beijing University of Technology School of Information Science and Technology Beijing China Purple Mountain Laboratories Nanjing China

ISBN: (纸本)9798331506322

To improve the reasonable allocation of network resources, and reduce service interruption or overwhelming delay for software-defined networking (SDN) due to excessive network load, this paper proposes a network load balancing scheme based on gated recurrent units (GRU) aiming at optimizing load balancing strategies. First, a link prediction and priority adjustment model is constructed to describe the network load balance optimization problem. Second, a GRU-based network traffic prediction algorithm is proposed to capture network traffic features. Specifically, a graph convolution network (GCN) is utilized to learn the spatial features in the network topology, and GRU is used to capture the long-term dependencies in the traffic data sequences. Finally, a logical ordering and threshold filtering strategy is designed to achieve priority ordering of links and determine the preferred links for services to improve network load balancing. The experimental results show that the GRUbased network traffic prediction algorithm exhibits 3% higher prediction accuracy compared to the existing baseline methods, and the network load balancing scheme based on GRU performs 4 x network load balancing. © 2024 IEEE.

关键词： Network theory (graphs)

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

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Computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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