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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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A Survey on Supervised,Unsupervised,and Semi-Supervised Approaches in Crowd Counting

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computers, Materials & Continua 2024年第12期81卷 3561-3582页

作者： Jianyong Wang Mingliang Gao Qilei Li Hyunbum Kim Gwanggil Jeon School of Electrical and Electronic Engineering Shandong University of TechnologyZibo255000China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK Department of Embedded Systems Engineering Incheon National UniversityIncheon22012Republic of Korea

Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public *** counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and *** approaches vary in terms of supervision techniques,network architectures,and model ***,most crowd counting methods rely on fully supervised learning,which has proven to be ***,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often *** a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical *** paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision *** summarize and critically analyze the key methods in these two categories,highlighting their strengths and ***,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark *** believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.

关键词： Crowd counting density estimation convolutional neural network(CNN) un/semi-supervised learning

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S2S-Net: Addressing the Domain Gap of Heterogeneous Sensor systems in LiDAR-Based Collective Perception

arXiv

引用

arXiv 2025年

作者： Teufel, Sven Gamerdinger, Jörg Bringmann, Oliver University of Tübingen Faculty of Science Department of Computer Science Embedded Systems Germany

Collective Perception (CP) has emerged as a promising approach to overcome the limitations of individual perception in the context of autonomous driving. Various approaches have been proposed to realize collective perception;however, the Sensor2Sensor domain gap that arises from the utilization of different sensor systems in Connected and Automated Vehicles (CAVs) remains mostly unaddressed. This is primarily due to the paucity of datasets containing heterogeneous sensor setups among the CAVs. The recently released SCOPE datasets address this issue by providing data from three different LiDAR sensors for each CAV. This study is the first to tackle the Sensor2Sensor domain gap in vehicle to vehicle (V2V) collective perception. First, we present our sensor-domain robust architecture S2S-Net. Then an in-depth analysis of the Sensor2Sensor domain adaptation capabilities of S2S-Net on the SCOPE dataset is conducted. S2S-Net demonstrates the capability to maintain very high performance in unseen sensor domains and achieved state-of-the-art results on the SCOPE dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Automation

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Simulation and Detection of Bus-Off Attacks in CAN

Simulation and Detection of Bus-Off Attacks in CAN

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IEEE Symposium on Intelligent Vehicle

作者： Jo Laufenberg Heiner Graser Thomas Kropf Oliver Bringmann Department of Computer Science Embedded Systems Group Faculty of Science University of Tuebingen

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This work presents a simulation framework for CAN communication including attacks, with a particular focus on bus-off and WeepingCAN attacks. The simulation is validated against existing experiments, demonstrating its accuracy in replicating attack scenarios proposed by Cho et al. [11] and Bloom [2]. An intrusion detection system (IDS) is evaluated using the Hacking and Countermeasures Research Lab (HCRL) data set, and limitations in detecting WeepingCAN attacks are identified. To address these limitations, an extension to the IDS is proposed that incorporates an error frequency criterion to improve detection accuracy. The extended IDS is evaluated in different environments, demonstrating its effectiveness in reliably distinguishing between normal communications and WeepingCAN attacks. Overall, this work contributes to the understanding and detection of sophisticated attacks on CAN and and offers support to develop and evaluate further detection mechanisms.

关键词： Accuracy Intelligent vehicles Intrusion detection Reliability computer crime

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Collective Perception Datasets for Autonomous Driving: A Comprehensive Review

Collective Perception Datasets for Autonomous Driving: A Com...

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IEEE Symposium on Intelligent Vehicle

作者： Sven Teufel Jörg Gamerdinger Jan-Patrick Kirchner Georg Volk Oliver Bringmann Department of Computer Science Embedded Systems Faculty of Science University of Tübingen

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

To ensure safe operation of autonomous vehicles in complex urban environments, complete perception of the environment is necessary. However, due to environmental conditions, sensor limitations, and occlusions, this is not always possible from a single point of view. To address this issue, collective perception is an effective method. Realistic and large-scale datasets are essential for training and evaluating collective perception methods. This paper provides the first comprehensive technical review of collective perception datasets in the context of autonomous driving. The survey analyzes existing V2V and V2X datasets, categorizing them based on different criteria such as sensor modalities, environmental conditions, and scenario variety. The focus is on their applicability for the development of connected automated vehicles. This study aims to identify the key criteria of all datasets and to present their strengths, weaknesses, and anomalies. Finally, this survey concludes by making recommendations regarding which dataset is most suitable for collective 3D object detection, tracking, and semantic segmentation.

关键词： Surveys Training Three-dimensional displays Reviews Semantic segmentation Urban areas Collaboration

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MR3D-Net: Dynamic Multi-Resolution 3D Sparse Voxel Grid Fusion for LiDAR-Based Collective Perception

MR3D-Net: Dynamic Multi-Resolution 3D Sparse Voxel Grid Fusi...

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International Conference on Intelligent Transportation

作者： Sven Teufel Jörg Gamerdinger Georg Volk Oliver Bringmann Department of Computer Science Embedded Systems Faculty of Science University of Tübingen

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The safe operation of automated vehicles depends on their ability to perceive the environment comprehensively. However, occlusion, sensor range, and environmental factors limit their perception capabilities. To overcome these limitations, collective perception enables vehicles to exchange information. However, fusing this exchanged information is a challenging task. Early fusion approaches require large amounts of bandwidth, while intermediate fusion approaches face interchangeability issues. Late fusion of shared detections is currently the only feasible approach. However, it often results in inferior performance due to information loss. To address this issue, we propose MR3D-Net, a dynamic multi-resolution 3D sparse voxel grid fusion backbone architecture for LiDAR-based collective perception. We show that sparse voxel grids at varying resolutions provide a meaningful and compact environment representation that can adapt to the communication bandwidth. MR3D-Net achieves state-of-the-art performance on the OPV2V 3D object detection benchmark while reducing the required bandwidth by up to 94 % compared to early fusion. Code is available at https://***/ekut-esIMR3D-Net

关键词： Three-dimensional displays Codes Bandwidth Object detection Benchmark testing Environmental factors Vehicle dynamics Intelligent transportation systems Faces

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Collective Perception Datasets for Autonomous Driving: A Comprehensive Review

arXiv

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arXiv 2024年

作者： Teufel, Sven Gamerdinger, Jörg Kirchner, Jan-Patrick Volk, Georg Bringmann, Oliver University of Tübingen Faculty of Science Department of Computer Science Embedded Systems Germany

关键词： Autonomous vehicles

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LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection systems in Autonomous Driving

LSM: A Comprehensive Metric for Assessing the Safety of Lane...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jörg Gamerdinger Sven Teufel Stephan Amann Georg Volk Oliver Bringmann Department of Computer Science Embedded Systems Group University of Tübingen Faculty of Science

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Comprehensive perception of the vehicle’s environment and correct interpretation of the environment are crucial for the safe operation of autonomous vehicles. The perception of surrounding objects is the main component for further tasks such as trajectory planning. However, safe trajectory planning requires not only object detection, but also the detection of drivable areas and lane corridors. While first approaches consider an advanced safety evaluation of object detection, the evaluation of lane detection still lacks sufficient safety metrics. For assessing safety, additional factors such as the semantics of the scene with road type and road width, the detection range as well as the potential causes of missing detections, incorporated by vehicle speed, should be considered for the evaluation of lane detection. Therefore, we propose the Lane Safety Metric (LSM), which takes these factors into account in order to evaluate the safety of lane detection systems by determining an easily interpretable safety score. We evaluate our offline safety metric on various virtual scenarios using different lane detection approaches and compare it with state-of-the-art performance metrics.

关键词： Measurement Vehicular and wireless technologies Lane detection Trajectory planning Roads Semantics Object detection Safety Autonomous vehicles

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MR3D-Net: Dynamic Multi-Resolution 3D Sparse Voxel Grid Fusion for LiDAR-Based Collective Perception

arXiv

引用

arXiv 2024年

作者： Teufel, Sven Gamerdinger, Jörg Volk, Georg Bringmann, Oliver University of Tübingen Faculty of Science Department of Computer Science Embedded Systems Germany

关键词： Object detection

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Development of a Wireless Real-Time Computation System for Fast and Accurate Heart Rate Monitoring using Photoplethysmography (PPG) Signals 16

Development of a Wireless Real-Time Computation System for F...

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16th IEEE Malaysia International Conference on Communication, MICC 2023

作者： Lim, Ben Dong Chong, Kim Soon Tiang, Sew Sun Lim, Wei Hong Mokayef, Mastaneh Tan, Bee Sian UCSI University Department Electrical & Electronic Engineering Selangor Malaysia Tunku Abdul Rahman University of Management and Technology Department of Computer Science and Embedded Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798350304343

This manuscript presents the development of a wireless system for fast and accurate heart rate monitoring. The system utilizes Seeed XIAO BLE and MAX86150 components to capture and process the photoplethysmogram (PPG) signal. Real-Time computation of heart rate is achieved through the deployment of a modified HeartPy algorithm on the microcontroller. The accuracy of the system was evaluated through experiments involving six patients, comparing the results with those obtained from a commercial pulse oximeter (A310) by AEON Technology. The Mean Percentage Error values demonstrated a close agreement between the test system and the commercial device, with an error falling within 0.31%. Additionally, the system achieved a 30% to 40% shorter refresh time compared to the commercial pulse oximeter, allowing for more frequent and up-To-date heart rate monitoring. The development of this wireless system contributes to the advancement of healthcare technology, aligning with the Sustainable Development Goal of Good Health and Well-being. Future improvements include incorporating oxygen saturation (SpO2) and electrocardiogram (ECG) detection into the system and designing a compact and portable form factor. By combining fast and accurate heart rate monitoring with wireless data transmission, this system holds promise for enhancing healthcare applications and improving patient well-being. © 2023 IEEE.

关键词： Photoplethysmography

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