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The Future of Driving: A Review on the Combination of Artificial Intelligence and Autonomous Vehicles

The Future of Driving: A Review on the Combination of Artifi...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International conference on

作者： Nikita Zade Mayur Gawande Prateek Verma Swapnil Gundewar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The convergence of Artificial Intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and Machine Learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability.

关键词： Ethics Machine learning algorithms Reviews Navigation Decision making sensor fusion Safety Artificial intelligence Socioeconomics Automotive engineering

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Enhanced GMapping Algorithm for Static Glass Detection

Enhanced GMapping Algorithm for Static Glass Detection

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IEEE International conference on Unmanned Systems (ICUS)

作者： Bowen Qi Qian Mo Bo Zhu School of Information and Intelligence Engineering Guangzhou Xinhua University Guangzhou China Beijing Baidu Netcom Science Technology Co. Ltd. Beijing China Center for Advanced Control and Smart Operations Nanjing University Suzhou China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

Detecting glass targets and obstacles has significant practical importance in modern society. However, the state-of-art GMapping algorithms suffer from inaccuracies in identifying and mapping their positions when used to deal with highly transparent and reflective glass obstacles under the conditions with only laser sensors. This finally results in incomplete maps and potential navigation errors in robot SLAM applications. To overcome the limitation, the paper presents an enhanced GMapping algorithm by incorporating a glass detection method to fuse laser and ultrasonic data. By exploiting the disparity in distances measured by these two sensors, the method effectively detects glass and refines the algorithm by substituting erroneous distance data with accurate ones. The indoor experimental results demonstrate the improvement of mapping accuracy and reduction of navigation errors owing to the application of the proposed sensor fusion method.

关键词： Accuracy Simultaneous localization and mapping Navigation Autonomous systems Ultrasonic variables measurement Heuristic algorithms Neural networks Measurement by laser beam Glass sensor fusion

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KF K-means: A High Performance K-means Implementation using Kernel fusion

KF K-means: A High Performance K-means Implementation using ...

引用

2023 IEEE International conference on Big Data, BigData 2023

作者： Ouyang, Kaiming Tran, Vincent Liu, Jinyang Wong, Bryan M. Chen, Zizhong Nvidia Corporation Santa ClaraCA United States University of California RiversideCA United States

ISBN: (纸本)9798350324457

The K-means algorithm is one of the simplest and most universal clustering algorithms. Significant work has been carried out over several years to improve its performance in both academic and industrial applications. Researchers have optimized K-means not only on the algorithm level but also on the architecture level. Notably, GEMM, a rigorously studied matrix multiplication operation, has been used to speed up the Euclidean-distance calculations in the K-means algorithm. The Intel DAAL library currently provides a fast K-means implementation based on the Intel Math Kernel Library GEMM subroutine and low-level architecture information. However, in spite of utilizing the MKL GEMM subroutine and architecture properties, the performance of the state-of-the-art K-means implementation is still far from its hardware peak performance. This paper presents a faster fused-matrix K-means kernel that is superior to current K-means designs. Based on our experimental results, the fused matrix K-means kernel runs around 76% faster than the state-of-the-art Intel DAAL K-means algorithm and is able to achieve nearly double floating point performance on Intel x86-84 Ivy micro-architectures. © 2023 IEEE.

关键词： and association rules classification Clustering Computations on matrices Macro and assembly languages

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Image fusion For Improved Situational Awareness In Military Operations Using Machine Learning

Image Fusion For Improved Situational Awareness In Military ...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International conference on

作者： Jeffin Gracewell R Santhosh V Sabarish dept. of Electronics and Communication Engineering Saveetha Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Image fusion is an essential technique used in various military applications to provide a comprehensive view of the target area by integrating multiple images from various sensors. This technique helps in detecting threats, identifying targets, and analyzing the situation to make informed decisions. The use of image fusion in military areas enhances the performance of the systems by providing high-resolution images that can be used to obtain valuable insights. The application of image fusion in military areas involves the use of various sensors such as thermal, infrared, and visual sensors, which capture images with different spectral and spatial resolutions. The images are then fused to create a single image that provides more detailed information about the target area. Image fusion techniques involve various algorithms, including multi-resolution analysis, wavelet transforms, and others, which are used to combine the images. In military areas, image fusion is used for a variety of tasks such as surveillance, reconnaissance, and target detection. The use of image fusion enables military personnel to detect threats more efficiently and provides them with the ability to analyze the situation with more clarity. Furthermore, image fusion allows for better situational awareness and facilitates quick decision-making. In conclusion, the application of image fusion in military areas has significant benefits in detecting threats and other resolution work-based performance.

关键词： Wavelet transforms Military computing Infrared image sensors sensor fusion Thermal sensors sensor phenomena and characterization sensor systems Personnel Optical sensors Image fusion

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Research on the fusion of Autonomous Navigation algorithms and Environmental Perception in Intelligent Ship Technology

Research on the Fusion of Autonomous Navigation Algorithms a...

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Power Electronics, Computer applications (ICPECA), IEEE International conference on

作者： Zhijie Zhang Yonggang Zong Ocean Department Binzhou Polytechnic Binzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

The research focus of this paper is on the organic combination of autonomous navigation algorithm and environmental perception technology, with the aim of enhancing the autonomous navigation capability of intelligent ships in complex marine environments. Autonomous navigation relies on the close coordination of multiple perception systems and algorithms, among which the simultaneous localization and mapping (SLAM) algorithm is particularly critical. The SLAM algorithm generates high-precision environmental maps in real time and provides accurate ship positioning by fusing multi-sensor information. This paper proposes and designs an autonomous navigation system that integrates environmental perception and SLAM technology. Its system architecture includes sensor data processing, path planning and control decision modules. The effectiveness of the proposed algorithm is verified through a series of model simulations, and experimental evaluation is carried out in multiple test scenarios. The simulation results show that compared with traditional navigation methods, the algorithm significantly improves the navigation accuracy and safety of ships in complex marine environments, and the positioning error rate is reduced by more than 15%. The research results of this paper provide a solid theoretical basis and technical support for the future research and development of autonomous navigation systems for intelligent ships.

关键词： Simultaneous localization and mapping Accuracy Navigation Heuristic algorithms Simulation Systems architecture Real-time systems Safety Marine vehicles Autonomous robots

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Robot Joint Tracking With Mobile Depth Cameras for Augmented Reality applications 25

Robot Joint Tracking With Mobile Depth Cameras for Augmented...

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25th International conference of Information fusion (fusion)

作者： Zea, Antonio Fennel, Michael Hanebeck, Uwe D. Karlsruhe Inst Technol KIT Inst Anthropomat & Robot Intelligent Sensor Actuator Syst Lab ISAS Karlsruhe Germany

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

Augmented reality (AR) in mobile devices (such as smartphones and tablets) is becoming more popular each day, and because of this many newer devices are starting to ship with embedded depth sensors. This presents a great opportunity for the field of extended object tracking, whose algorithms are well-suited for dealing with varying measurement quality while requiring little CPU usage. In this paper, we present an application in the field of robotics, based on the idea of reconstructing the dynamic state of a robot (joint positions and velocities) simply by observing it with an AR device, and using only the robot specification (its URDF file) as prior knowledge, without requiring a connection to the robot's control system. This can allow the mobile device to identify where a robot is, or viceversa, without requiring markers such as QR codes. Additionally, this can serve as a stepping stone for more sophisticated assistance systems that can interact with the robot without requiring any access to its internals, which could otherwise make it difficult to deploy the AR app in sensitive systems. Using the iPad Pro 2020 as an example device, we examine the challenges involved in processing mobile depth images, how to develop a robust shape model and the corresponding estimator, and how the app can ask the user to help in its initialization using AR. We will also provide an evaluation with real data that shows how the proposed system can track a moving robot robustly even if measurement quality is reduced significantly.

关键词： Shape Robot vision systems Tablet computers Cameras Time measurement sensors Object tracking

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SurroundSense: Event-Driven User Personalization with Ambient Context Using Ultra-Wideband Radar 22

SurroundSense: Event-Driven User Personalization with Ambien...

引用

22nd IEEE Consumer Communications and Networking conference, CCNC 2025

作者： Ranjan, Prajwal Ppallan, Jamsheed Manja Damam, Yellappa Badiger, Sakshi Madhan Raj, K. Mangla, Raghav Thakur, Rajip Kim, Chiho Connectivity R&d India Samsung Electronics Connectivity R&d Group Suwon Korea Republic of

ISBN: (纸本)9798331508050

In this paper, we propose SurroundSense, a novel event-driven framework that generates contextual information based on the user's surroundings using the Impulse Radio Ultra-Wideband (IR-UWB) Radar integrated into smartphones. By leveraging the channel impulse response (CIR) data obtained from the IR-UWB radar, SurroundSense constructs a detailed contextual understanding of the user's environment, allowing for tailored recommendations based on the surrounding conditions. The system processes the CIR data and combines it with acoustic sensing information, specifically noise levels and ambient light information to generate the surrounding information. Further-more, utilizing the IMU sensors enables an expanded field-of-view (FOV), providing a comprehensive 360° environmental awareness. This enhanced surrounding information enhances the accuracy and pertinence of personalized recommendations. Our approach showcases the potential of incorporating IR-UWB technology into personalization algorithms, ultimately improving user experiences and facilitating adaptive, context-aware applications. © 2025 IEEE.

关键词： Channel Impulse Response (CIR) Contextual Information Impulse Radio Ultra-Wideband (IR-UWB) Radar Personalized Recommendations sensor fusion

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A General Dataset Generator for Industrial Internet of Things Using Multi-sensor Information fusion 9

A General Dataset Generator for Industrial Internet of Thing...

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9th International conference on Dependable Systems and Their applications, DSA 2022

作者： Wang, Mian Sun, Jinlong Lu, Zhiyi Wang, Yu Zhang, Yifan Zhang, Jie Zhang, Zhe Gui, Guan College of Telecommunications and Information Engineering Njupt Nanjing China Nanjing Great Information Technology Co. Ltd Nanjing China Nanjing Run-Time Electric Automation Co. Ltd Nanjing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

The development of the fifth-generation (5G) communication technology is promoting the overall improvement of the Industrial Internet of Things (IIoT), and is accelerating the pace of the fourth industrial revolution. There lie problems in the IIoT, such as complex industrial field environment and diverse transmission information. It is difficult for us to collect and process massive real-time data for various devices in a timely, reliable, and efficient manner. In order to investigate and mitigate the above problems, we construct a general IIoT Dataset Generator based on a multi-sensor transmission and fusion architecture. The Dataset Generator has advantages such as good privacy, simple development and strong mobility. As an example, we use sound sensors and accelerometers to collect data for industrial scenes. The architecture is also equipped with a mature data preprocessing and visualization scheme. We demonstrate the effectiveness of the IIoT Dataset Generator and the processing algorithms under the considered scenario. © 2022 IEEE.

关键词： Internet of things

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14th International conference on Sensing Technology, ICST 2022

14th International Conference on Sensing Technology, ICST 20...

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14th International conference on Sensing Technology, ICST 2022

ISBN: (纸本)9783030988852

The proceedings contain 35 papers. The special focus in this conference is on Sensing Technology. The topics include: An Accurate Model of Breather for Moisture Estimation for Transformer Health Monitoring;vibration Measurement as Feedback from a Pneumatic Knife;addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives;lossy Mode Resonances Supported by Nanoparticle-Based Thin-Films;design and Development of an IoT-Enabled sensor Node for Agricultural and Modelling applications;real-Time Object Detection and Tracking from Videos;IoT Enabled PoC Medical Diagnostic MEMS-Based sensor Device for Kidney Healthcare;epileptic Seizure Detection Using Continuous Wavelet Transform and Deep Neural Networks;Methods Tested to Optimize the Performance of WEBGL applications;a Linear Process Analysis and sensor applications of a Pilot Water Treatment Plant;Deep Learning based Detection of Foot Lift Event Using a Single Accelerometer for Accurate Firing of FES;a Lightweight Security Scheme for Failure Detection in Microservices IoT-Edge Networks;Comparison of the Routing algorithms Based on Average Location Error and Accuracy in WSN;the Development of a Portable IoT-Enabled Aqueous Sulphur sensor;fusion of Radar Data Domains for Human Activity Recognition in Assisted Living;a C-Band Intermodulation Radar System for Target Motion Discrimination;application of Photoacoustic Sensing in Depicting Viscosity Information of Lubrication Oil;UV Laser-Induced Graphene Electrode for Supercapacitor and Electrochemical Sensing applications;A NIRS Based Device for Identification of Acute Ischemic Stroke by Using a Novel Organic Dye in the Human Blood Serum;Filament Supply Sensing and Control for FFF/FDM 3D Printing Technology;preface;planar Capacitive Touch sensors—A Comparative Study.

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A Distributed Storage and fusion Management Method of Massive Vehicle Multi-Source sensor Data

A Distributed Storage and Fusion Management Method of Massiv...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International conference on

作者： Meng Wang Yuan Ji Junfeng Qian Wei Xia Haoting Ye Guizhou Power Grid Co Ltd Guizhou China Southern Power Grid Digital Grid Technology (Guangdong) Co. Ltd Guangdong China The Chinese University of Hong Kong Shenzhen Shenzhen China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

With the advancement of sensor technology, an increasing amount of multi-source vehicle sensor data has emerged, offering significant value in areas such as high-precision navigation and intelligent driving. In this article, a fused organization expression model for vehicle multi-source sensor data is proposed, name V-MSFOEM. Based on the V-MSFOEM and combined with a distributed database, query algorithms such as spatial nearest-neighbor point query, spatio-temporal range query, and fused point data based on interest sections are proposed. The public dataset KITTI is used to compare the proposed algorithm with the traditional query methods, and the experimental results demonstrate that the proposed algorithms exhibit higher query efficiency, providing flexible and efficient foundational data support for upper-layer applications.

关键词： Computer science Navigation Scalability Distributed databases Organizations Data models Reliability Monitoring

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