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检索条件"任意字段=Conference on Radar Sensor Technology"

共 5986 条记录，以下是71-80 订阅

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Design and Implementation of a Highly Efficient LIDAR-Based Teleoperated Driving System

Design and Implementation of a Highly Efficient LIDAR-Based ...

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Consumer Communications and Networking conference, CCNC IEEE

作者： Johann Nikolai Hark Julian Pfeifer Jorin Kouril Bernd Schäufele Ilja Radusch Daimler Center for Automotive Information Technology Innovations (DCAITI) Berlin Germany Fraunhofer Institute for Open Communication Technologies (FOKUS) Berlin Germany

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Automated driving has advanced significantly, but challenges remain in scenarios like urban areas and parking garages due to technical and legal constraints. As automated vehicles rise, connected vehicle technologies gain focus. Tele-operated driving serves as a bridge technology for situations where automated driving is limited. This paper presents a novel teleoperated driving system to extend the Operational Design Domain of automated vehicles, such as in underground parking, using Direct Short Range Communication (IEEE 802.11p). The approach emphasizes efficient sensor data transmission from LIDAR, preprocessing raw data to filter point clouds and detect freespace and objects, enhancing responsiveness and reliability.

关键词： Point cloud compression Bridges Laser radar Connected vehicles Law Urban areas Reliability Data communication

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Novel Multi-Scan LiDAR System for Enhanced Shadow Removal

Novel Multi-Scan LiDAR System for Enhanced Shadow Removal

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IEEE International conference on Consumer Electronics (ICCE)

作者： Jung-Min Kim Dae Yu Kim Dept. of Electrical and Computer Engineering Inha university Incheon Republic of Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

In autonomous driving, sensor data is critical for ensuring safety in dynamic road environments. The conventional LiDAR systems face challenges when obstacles create shadow zones leading to incomplete mapping and potential detection failures. This paper presents a novel LiDAR system employing beam-splitting technology to minimize shadow regions in 3D mapping. Through multi-angle scanning, the proposed system effectively eliminates shadow regions, ensuring continuous detection in complex driving environments. Prototype testing demonstrates a reduction in shadow-induced detection errors achieving measurement accuracy within 3%. The research highlights the viability of this technology for safer autonomous driving systems.

关键词： Laser radar Accuracy Three-dimensional displays Pedestrians Roads Measurement uncertainty Prototypes Safety Autonomous vehicles Faces

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Review on the Real-Time Mapping of Autonomous Vehicles: Technologies and Challenges

Review on the Real-Time Mapping of Autonomous Vehicles: Tech...

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Visual Analytics and Data Visualization (ICVADV), International conference on

作者： C Paveithra U Jaivasanthan K Kirubasankar Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Autonomous vehicles rely on real-time mapping systems to ensure effective navigation, avoid obstacles, and plan their paths accurately. This paper attempts a detailed review of the technologies, and challenges involved in real-time mapping for AVs. The core technologies comprise Lidar, radar, cameras, GNSS, and IMU as made use of in aggregate to build high-definition maps of the environment. Techniques such as SLAM and high-definition mapping can update the map continuously when deployed over dynamic environments. It is precisely this capability that qualifies AVs as appropriately responsive to reality changes. Yet, such is an extremely challenging task that falls within the broad scope of latent issues, resource utilization through computation, adaptability, and sensor reliability, then discussed several promising future directions for improving the real-time mapping capabilities of AVs, namely, edge computing and vehicle to everything (V2X) communication. These can bring a great advantage to improving the performance of AVs by processing vast quantities of real-time data extremely quickly.

关键词： Simultaneous localization and mapping Laser radar Reviews Navigation Visual analytics Real-time systems Vehicle dynamics Autonomous vehicles Vehicle-to-everything Edge computing

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Ultra-High-Frequency Harmony: mmWave radar and Event Camera Orchestrate Accurate Drone Landing 25

Ultra-High-Frequency Harmony: mmWave Radar and Event Camera ...

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Proceedings of the 23rd ACM conference on Embedded Networked sensor Systems

作者： Haoyang Wang Jingao Xu Xinyu Luo Xuecheng Chen Ting Zhang Ruiyang Duan Yunhao Liu Xinlei Chen Shenzhen International Graduate School Tsinghua University Shenzhen Guangdong China Carnegie Mellon University Pittsburgh USA Tsinghua University Shenzhen Guangdong China Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University Shenzhen Guangdong China Meituan Technology Shenzhen Guangdong China Tsinghua University beijing China Pengcheng Laboratory Shenzhen Guangdong China RISC-V International Open Source Laboratory Shenzhen Guangdong China

ISBN: (纸本)9798400714795

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we replace traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for drone landings. To fully leverage the temporal consistency and spatial complementarity between these modalities, we propose two innovative modules, consistency-instructed collaborative tracking and graph-informed adaptive joint optimization, for accurate drone measurement extraction and efficient sensor fusion. Real-world experiments in landing scenarios from a drone delivery company demonstrate that mmE-Loc outperforms SOTA methods in both accuracy and latency.

关键词： drone ground localization

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Design and Validation of Learning Aware HMI for Learning-Enabled Increasingly Autonomous Systems

Design and Validation of Learning Aware HMI for Learning-Ena...

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Annual IEEE Systems conference

作者： Parth Ganeriwala Michael Matessa Siddhartha Bhattacharyya Randolph M. Jones Jennifer Davis Parneet Kaur Simone Fulvio Rollini Natasha Neogi Computer Engineering and Sciences Florida Institute of Technology Melbourne FL USA Applied Research and Technology Collins Aerospace Cedar Rapids IA USA Applied Cognitive Systems Soar Technology Ann Arbor MI USA Applied Research and Technology Collins Aerospace Cedar Rapids IA U.S.A. Applied Research and Technology Collins Aerospace Rome Italy Langley Research Center NASA Hampton VA USA

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

With the rapid advancements in Artificial Intelligence (AI), autonomous agents are increasingly expected to manage complex situations where learning-enabled algorithms are vital. However, the integration of these advanced algorithms poses significant challenges, especially concerning safety and reliability. This research emphasizes the importance of incorporating human-machine collaboration into the systems engineering process to design learning-enabled increasingly autonomous systems (LEIAS). Our proposed LEIAS architecture emphasizes communication representation and pilot preference learning to boost operational safety. Leveraging the Soar cognitive architecture, the system merges symbolic decision logic with numeric decision preferences enhanced through reinforcement learning. A core aspect of this approach is transparency; the LEIAS provides pilots with a comprehensive, interpretable view of the system's state, encompassing detailed evaluations of sensor reliability, including GPS, IMU, and LIDAR data. This multi-sensor assessment is critical for diagnosing discrepancies and maintaining trust. Additionally, the system learns and adapts to pilot preferences, enabling responsive, context-driven decision-making. Autonomy is incrementally escalated based on necessity, ensuring pilots retain control in standard scenarios and receive assistance only when required. Simulation studies conducted in Microsoft's XPlane simulation environment to validate this architecture's efficacy, showcasing its performance in managing sensor anomalies and enhancing human-machine collaboration, ultimately advancing safety in complex operational environments.

关键词： Laser radar Human-machine systems NASA Collaboration Reinforcement learning Safety Reliability Logic Artificial intelligence Standards

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The Role of sensors in Autonomous Vehicles and its Impact on Smart Transportation Systems

The Role of Sensors in Autonomous Vehicles and its Impact on...

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Automation and Computation (AUTOCOM), International conference on

作者： Aneesh Wunnava Hitesh Kalra Ashutosh Pattanaik S. Lalithakumari Rakesh Kumar Yadav Lalit Khanna Department of Electronics and Communication Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Mechaical Engineering JAIN (Deemed to be University) Bangalore Karnataka Department of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Once at the forefront of a transformation in the transportation industry, autonomous vehicles have all but disappeared from our collective discourse. Most autonomous vehicles depend on sensors to detect what is around and make some decisions without human interference. sensors are paramount in functioning AVs since they give real-time information regarding the vehicle's location, rate, and surrounding objects. The sensors here are for all purposes; some use cameras, while others use lidar, radar, and several ultrasonic sensors. Combining both gives the vehicle a 360-degree view and helps it maneuver safely through even the most complex environments. AV-cum-smart transport systems can help improve traffic flow and road safety and increase overall transportation efficiency. sensors are critical to harnessing these benefits and allow the AVs to work by way of other vehicles or organization, such as circulation signals and road ciphers. This communication gets dynamic route planning so that the time of travel and fuel can be saved. Nevertheless, several challenges remain to be addressed with sensor reliability under any conditions; however adverse, they may still face serious opposition and cybersecurity. Further research and development in the area of sensor technology are imperative to bring AVs into common use, as well as for using smart transportation systems effectively.

关键词： Transportation industry Cameras sensor systems Road safety Smart transportation Reliability Computer security Vehicle dynamics Intelligent sensors Autonomous vehicles

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An Asynchronous 160×90 Flash LiDAR sensor with Dynamic Frame Rates of 5 to 250fps Based on Pixelwise ToF Validation via a Background-Light-Adaptive Threshold

An Asynchronous 160×90 Flash LiDAR Sensor with Dynamic Fram...

引用

IEEE International conference on Solid-State Circuits (ISSCC)

作者： Seonghyeok Park Su-Hyun Han Jongbeom Kim Jubin Kang Jung-Hoon Chun Jaehyuk Choi Seong-Jin Kim Ulsan National Institute of Science and Technology Ulsan Korea SolidVue Seongnam Korea Sungkyunkwan University Suwon Korea Sogang University Seoul Korea

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

Over the past few decades, LiDAR technology has become a cornerstone in various fields, including autonomous vehicles, augmented reality, and robotics. Unlike traditional scanning LiDAR systems, flash LiDAR technology captures the entire field of view in a single frame, enabling faster 3D mapping with fewer optical components. To manage the large influx of photons received by the sensor, on-chip histogramming time-to-digital converters (hTDC) are widely employed, facilitating time-correlated single-photon counting techniques [1]–[5]. However, their physical size imposes constraints on spatial resolution. Partial histogramming techniques [1]–[3] or shared hTDC architectures leveraging advanced 3D stacking technologies [4], [5] are introduced to shrink its size and improve spatial resolution, although they are susceptible to background light intensity [4] or present limitations in detection range and frame rates [5]. Meanwhile, conventional frame-based LiDAR sensors offer a suboptimal frame rate determined by the pixel with the worst signal-to-noise ratio (SNR) condition. An asynchronous event-driven readout scheme [6] is incorporated into a time-gated photon counting sensor with spiking neurons to support versatile operating modes and frame rates. However, essential control signals for pixel sensitivity and event rates are driven by external components, limiting the scalability.

关键词： Laser radar Three-dimensional displays Stacking Robot sensing systems System-on-chip Solid state circuits Spatial resolution Vehicle dynamics Signal to noise ratio Photonics

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Flood Prediction and Adaptive Farming Solutions Using IoT, Machine Learning, and Remote Sensing for Climate-Resilient Agriculture

Flood Prediction and Adaptive Farming Solutions Using IoT, M...

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Intelligent Control, Computing and Communications (IC3), International conference on

作者： Karthiyayini J Alok Jain Kashi Sai Prasad Tamam Ali Abd Ulabbas Abedi Yedida Prasanna Karthikeyan Murugesan Department of Information Science Engineering New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Information Technology GRIET Bachupally Hyderabad Telangana India Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Flood is one of the most disastrous natural disasters to the people of agriculture, people and infrastructure. Mitigation of these risks and promotion of climate resilient agriculture depends on accurate flood predictions and progressive farming strategies. This paper presents an integrated model consisting of Internet of Things (IoT) sensors, Machine Learning (ML) and Remote Sensing technologies to provide precise, real time flood prediction and adaptive farming options. Using advanced deep learning algorithms and real time sensor data to predict and risk assess floods in real time, the model leverages real time satellite based Synthetic Aperture radar (SAR). Another function of the model is to offer adaptive farming solutions including optimized irrigation, flood resilient crop selection, and disaster recovery recommendations for regions impacted by the disaster. A hybrid AI technique assimilating the social media data, and blockchain for integrity of data is more effective to make decisions for the local authorities and farmers. This study seeks to fill the gaps in interdisciplinary collaboration, data scarcity and governance and provide a solution for flood management and sustainable agriculture as a whole.

关键词： Adaptation models Accuracy Disasters Predictive models Real-time systems sensors Floods Remote sensing Synthetic aperture radar Farming

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Comprehensive Review of Cybersecurity Vulnerabilities and Safety Concerns in Autonomous Vehicles

Comprehensive Review of Cybersecurity Vulnerabilities and Sa...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE International conference on

作者： Uppalapati Sudheer Kumar Sukanta Halder Basant Kumar Sethi Department of Electrical Engineering Sardar Vallabhbhai National Institute of Technology Surat Surat India Department of Electrical Engineering Indian Institute of Technology(ISM) dhanbad Dhanbad India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

Autonomous Vehicles (AVs) are among the leading technologies enhancing our world with their advanced autonomous driving capabilities and functional awareness. These AVs rely on sophisticated technologies such as weight sensors, speed sensors, tire pressure sensors, 3D imaging cameras, satellite-based positioning systems, and advanced radars like light detection and ranging (LiDAR) and 3D radar. AVs utilize these components, along with highly complex control algorithms as their central processing units, to perform autonomous driving, making them vulnerable to cybersecurity attacks. These security attacks raise significant safety concerns for AVs. This article provides a comprehensive review of various cybersecurity threats associated with AVs and presents potential solutions. Additionally, it examines the vulnerabilities of the sophisticated equipment used in AVs. This article aims to enhance the understanding of the safety and security concerns associated with AVs and offers insights into the importance of cybersecurity in this evolving technological field. Furthermore, this article addresses the current state of cybersecurity measures in AVs, evaluating the effectiveness of existing protocols and identifying areas that require improvement. It also explores emerging technologies and strategies that can enhance the security of AVs, such as blockchain for secure data transactions, machine learning for threat detection, and robust encryption methods.

关键词： Image sensors radar imaging Threat assessment sensor systems Safety sensors Encryption Security Computer security Autonomous vehicles

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Perception System for Autonomous Vehicles: Object Detection, Motion Tracking, and Future Position Prediction

Perception System for Autonomous Vehicles: Object Detection,...

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IEEE International conference on Industrial technology (ICIT)

作者： Gustavo Andreas Günther Pedro Henrique Almeida da Cruz Joáo Pedro Barcelos de Assis Carlos N. Silla Marcelo Eduardo Pellenz Marco Antonio Simões Teixeira PPGia-PUCPR Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Paraná Brazil Halmstad University Halmstad Sweden

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents a multi-sensor fusion approach for detecting, clustering, and predicting the motion of dynamic objects by integrating camera and LiDAR data. The framework combines YOLO-based visual segmentation with LiDAR point clouds to enable accurate spatial localization and motion tracking. Ego-motion compensation is achieved using IMU and velocity data, while trajectory prediction is enhanced through centroidbased orientation estimation. Detected objects are segmented into coherent clusters using DBSCAN, facilitating visualization and motion forecasting. The framework was validated on a simulated KITTI dataset, demonstrating its effectiveness in dynamic object detection and trajectory prediction.

关键词： Point cloud compression Visualization Laser radar Accuracy Motion segmentation Dynamics Object detection Cameras Trajectory Vehicle dynamics

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