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作者： Fedoruk, Brandonlib. University of Michigan

Modern vehicles have evolved into supporting advanced internal networks and connecting System Based Chips (SBC), System in a Package (SiP) solutions or traditional micro controllers to foster an electronic ecosystem for high speed data transfers, precision and real-time control. The use of Controller Area Networks (CAN) is widely adopted as the backbone of internal vehicle communication infrastructure. Automotive applications such as ADAS, autonomous driving, battery management systems, power train systems, telematics and infotainment, all utilize CAN transmissions directly or through gateway management. The network transmissions lack robust integrity verification mechanisms to validate authentic data payloads, making it vulnerable to packet replay, spoofing, insertion, deletion and denial of service attacks. Additional methods exist to secure network data such as traditional cryptography. Utilizing this method will increase the computational complexity, processing latency and increase overall system cost. This thesis proposes a robust, light and adaptive solution to validate the authenticity of automotive sensor data using CAN network protocol. We propose using a two-dimensional Quantization Index Modulation (QIM) data hiding technique, to create a means of verification. Analysis of the proposed framework will be conducted in a sensor transmission scenario for RADAR sensors in an autonomous vehicle setting. The detection and effects of distortion on the application are tested through the implementation of sensor fusion algorithms and the results are observed and analyzed. The proposed framework offers a needed capability to maintain transmission integrity without the compromise of data quality and low design complexity. This framework could also be applied to different network architectures, as well as its operational scope could be modified to operate with more abstract types of data.

关键词： sensor networks RADAR Data integrity Data security ADAS Digital watermarking Connected vehicle Thesis

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On the Adaptivity of Unscented Particle Filter for GNSS/INS Tightly-Integrated Navigation Unit in Urban Environment

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IEEE ACCESS 2021年 9卷 144157-144170页

作者： Vouch, Oliviero Minetto, Alex Falco, Gianluca Dovis, Fabio Politecn Torino Dept Elect & Telecommun DET I-10129 Turin Italy LINKS Fdn Space & Nav Technol Area I-10138 Turin Italy

Tight integration algorithms fusing Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) have become popular in many high-accuracy positioning and navigation applications. Despite their reliability, common integration architectures can still run into accuracy drops under challenging navigation settings. The growing computational power of low-cost, embedded systems has allowed for the exploitation of several advanced Bayesian state estimation algorithms, such as the Particle Filter (PF) and its hybrid variants, e.g. Unscented Particle Filter (UPF). Although sophisticated, these architectures are not immune from multipath scattering and Non-Line-of-Sight (NLOS) signal receptions, which frequently corrupt satellite measurements and jeopardise GNSS/INS solutions. Hence, a certain level of modelling adaptivity should be granted to avoid severe drifts in the estimated states. Given these premises, the paper presents a novel Adaptive Unscented Particle Filter (AUPF) architecture leveraging two cascading stages to cope with disruptive, biased GNSS input observables in harsh conditions. A INS-based signal processing block is implemented upstream of a Redundant Measurement Noise Covariance Estimation (RMNCE) stage to strengthen the adaptation of observables' statistics and improve the state estimation. An experimental assessment is provided for the proposed robust AUPF that demonstrates a 10 % average reduction of the horizontal position error above the 75-th percentile. In addition, a comparative analysis both with previous adaptive architectures and a plain UPF is carried out to highlight the improved performance of the proposed methodology.

关键词： Global navigation satellite system Navigation Measurement uncertainty Noise measurement Adaptation models Satellites Position measurement Bayes methods sensor fusion inertial navigation satellite navigation systems particle filters adaptive estimation position accuracy

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Efficient fusion network with label generation and branch transformations for visible and infrared images fusion

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Infrared Physics & Technology 2025年 150卷

作者： Dongdong Sun Chuanyun Wang Tian Wang Qian Gao Zhaokui Li College of Artificial Intelligence Shenyang Aerospace University Shenyang 110136 China Institute of Artificial Intelligence Beihang University Beijing 100191 China School of Computer Science Shenyang Aerospace University Shenyang 110136 China

The information obtained from a single sensor is often insufficient for accurate environmental perception. In contrast, multi-sensor fusion significantly enhances perceptual accuracy, with the integration of infrared and visible light sensors being one of the most common approaches in multi-sensor fusion. Infrared and visible image fusion aims to combine the texture details of visible images with the thermal radiation information of infrared images to produce fused images with enhanced visual quality, which find broad applications in night vision, surveillance, and target detection. Existing fusion methods primarily focus on improving the quality of fused images, often at the expense of computational efficiency due to the reliance on complex, multi-layered architectures. This limitation makes them unsuitable for deployment on low-power terminal devices. To address these challenges, this paper proposes a novel image fusion framework, termed TSfusion, which leverages teacher-student learning to achieve both high-quality fusion and high computational efficiency. A teacher network, equipped with sequence-model-based feature extraction blocks and progressive integration modules, generates high-quality fusion labels by overcoming the limited receptive field of conventional convolutional networks. The lightweight student network adopts a reparameterisation-based Fast Inception Module, ensuring robust fitting capabilities while maintaining high-speed operation. Additionally, a novel multi-scale loss function is introduced to guide the student network in learning from the teacher at both semantic and pixel levels. Experimental results on three benchmark datasets demonstrate that the proposed TSfusion framework achieves superior visual and quantitative performance compared to state-of-the-art methods. Furthermore, TSfusion exhibits exceptional inference speed, outperforming all existing fusion algorithms, making it highly suitable for real-time applications. The implementatio

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MEFE: A Multi-fEature Knowledge fusion and Evaluation Method Based on BERT 20th

MEFE: A Multi-fEature Knowledge Fusion and Evaluation Method...

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20th International Conference on algorithms and architectures for Parallel Processing (ICA3PP)

作者： Ji, Yimu Hu, Lin Liu, Shangdong Xu, Zhengyang Liu, Yanlan Liu, Kaihang Tang, Shuning Liu, Qiang Xiao, Wan Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun InstHigh Performance Comp & Bigdata Nanjing 210003 Jiangsu Peoples R China Nanjing Ctr HPC China Nanjing 210003 Jiangsu Peoples R China Jiangsu HPC & Intelligent Proc Engineer Res Ctr Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Educ Sci & Technol Nanjing 210023 Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Knowledge fusion is an important part of constructing a knowledge graph. In recent years, with the development of major knowledge bases, the integration of multi-source knowledge bases is the focus and difficulty in the field of knowledge fusion. Due to the large differences in knowledge base structure, the efficiency and accuracy of fusion are not high. In response to this problem, this paper proposes MEFE (Multi-fEature Knowledge fusion and Evaluation Method) based on BERT. MEFE comprehensively considers the attributes, descriptions and category characteristics of entities to perform knowledge fusion on multi-source knowledge bases. Firstly, MEFE uses entity category tags to build a category dictionary. Then, it vectorizes the category tags based on the dictionary and clusters the entities according to the category tags. Finally it uses BERT (Bidirectional Encoder Representation from Transformers) to calculate the entity similarity for the entity pairs in the same group. We calculate entity redundancy rate and information loss rate of knowledge base according to the fusion result, so as to evaluate the quality of the knowledge base. Experiments show that MEFE effectively improves the efficiency of knowledge fusion through clustering, and the use of BERT promotes the accuracy of fusion.

关键词： Multi-source knowledge base Knowledge fusion Vectorization of category labels BERT Quality evaluation

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fusion of Data from Radar and Depth sensors Applied for Healthcare-Oriented Characterization of Persons' Gait 23

Fusion of Data from Radar and Depth Sensors Applied for Heal...

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23rd IEEE Conference on Signal Processing algorithms, architectures, Arrangements, and applications (SPA)

作者： Mazurek, Pawel Wagner, Jakub Morawski, Roman Z. Warsaw Univ Technol Fac Elect & Informat Technol Inst Radioelect & Multimedia Technol Nowowiejska 15-19 PL-00665 Warsaw Poland

ISBN: (纸本)9788362065363

The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors applied in a system for unobtrusive monitoring of elderly persons. The performance of monitoring systems, based on both types of sensors - when used separately and when used in combination - has been compared with respect to their potential for decreasing the uncertainty of estimation of several quantities, carrying information important for medical and healthcare services, viz. the state of being in motion, the direction of the movement, the travelled distance and the mean walking speed. The results of the numerical experimentation performed on real-world data - show that the fusion of data enables more robust and accurate estimation of those parameters, and therefore more reliable monitoring of elderly persons.

关键词： measurement data fusion impulse-radar sensor depth sensor healthcare

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面向变电站无人化巡检的多源融合即时定位与建图方法综述

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广东电力 2025年第3期38卷 55页

作者：姜寒王凯青岛大学电气工程学院山东青岛266071

随着智能电网技术的发展,变电站巡检对定位和建图精度的要求不断提高,但单一传感器难以满足复杂环境下的巡检需求。多传感器融合即时定位与建图(simultaneous localization and mapping,SLAM)技术在变电站巡检中显得尤为重要,为此对该... 详细信息

随着智能电网技术的发展,变电站巡检对定位和建图精度的要求不断提高,但单一传感器难以满足复杂环境下的巡检需求。多传感器融合即时定位与建图(simultaneous localization and mapping,SLAM)技术在变电站巡检中显得尤为重要,为此对该技术的发展进行综述。首先,回顾多传感器融合SLAM技术的发展历程,从传感器类型、融合架构和数据处理3个维度对技术进行分析;其次,将融合架构划分为松耦合、紧耦合2种模型,探讨它们在变电站巡检中的应用效果;同时,总结SLAM算法的评价标准,包括定位精度、建图效率、环境适应性和计算复杂度等关键指标,并分析当前技术面临的挑战。多传感器融合SLAM技术能够显著提升变电站巡检机器人的定位精度和环境感知能力。通过协同集成多种传感器信息,该技术可有效解决单一传感器的局限性问题,在变电站巡检中的应用正在逐步现实化。

关键词：即时定位与建图电力巡检多传感器融合松耦合紧耦合

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Neural Architecture Search for Automotive Grid fusion Networks Under Embedded Hardware Constraints

Neural Architecture Search for Automotive Grid Fusion Networ...

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International Conference on Machine Learning and applications (ICMLA)

作者： Gabor Balazs Walter Stechele Technische Universität München Munich Germany

ISBN: (数字)9781728184708

ISBN: (纸本)9781728184715

The goal of automotive sensor fusion is to generate an environmental model with low-cost, mass-produced sensors-typically camera and radar. The central fusion controller needs sophisticated post-processing algorithms to achieve this goal despite extensive pre-processing on the sensor side. Recent advances show the capabilities of deep convolutional auto-encoders for sensor fusion but are still limited to high-performance hardware. Limited memory and processing capabilities of embedded devices lead to the multi-objective optimization problem addressed in this work. The proposed evolutionary neural architecture search is configured to find novel sensor fusion network architectures optimized for embedded hardware. The discovered encoding and decoding cells improve fusion performance compared to that of corresponding cells from literature. These lightweight network architectures allow the deployment on constrained embedded devices, thus leverage environmental perception of level 2/2+ automated cars.

关键词： Training Computer architecture sensor fusion Hardware Encoding Decoding Automotive engineering

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RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving

RGB and LiDAR fusion based 3D Semantic Segmentation for Auto...

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IEEE Intelligent Transportation Systems Conference (IEEE-ITSC)

作者： El Madawi, Khaled Rashed, Hazem El Sallab, Ahmad Nasr, Omar Kamel, Hanan Yogamani, Senthil Valeo R&D Cairo Cairo Egypt Cairo Univ Giza Egypt Valeo Vis Syst Galway Ireland

ISBN: (纸本)9781538670248

LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or due to shadows where the performance of cameras is degraded. LiDAR perception is gradually becoming mature for algorithms including object detection and SLAM. However, semantic segmentation algorithm remains to be relatively less explored. Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation. Our main contribution is to convert the RGB image to a polar-grid mapping representation used for LiDAR and design early and mid-level fusion architectures. Additionally, we design a hybrid fusion architecture that combines both fusion algorithms. We evaluate our algorithm on KITTI dataset which provides segmentation annotation for cars, pedestrians and cyclists. We evaluate two state-of-the-art architectures namely SqueezeSeg and PointSeg and improve the mIoU score by 10% in both cases relative to the LiDAR only baseline.

关键词： Semantic Segmentation

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Multi-sensor-fusion System for People Counting applications 1

Multi-Sensor-Fusion System for People Counting Applications

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1st International Conference on Societal Automation (SA)

作者： Stec, Michal Herrmann, Viktor Stabernack, Benno Fraunhofer Heinrich Hertz Inst HHI Embedded Syst Grp Berlin Germany Univ Potsdam Embedded Syst Architectures Signal Proc Potsdam Germany

ISBN: (纸本)9781728133454

For economical as well as environmental reasons, logistical planning and efficient assignment of transport vehicles in public transportation need a precise knowledge of passenger utilization. To provide reliable figures sophisticated counting methods are demanded. Previously developed systems, mostly using a single 2D image sensor or a 3D depth sensor, can not fully achieve the required accuracy. In this paper, we present a robust people counting algorithm, based on multi sensor data fusion. Our solution runs on embedded systems with reasonable requirements with respect to computational power. 3D distance information, obtained from the ToF system, is used to perform the basic detection of objects. Once detected, the objects get additional classification features exploiting the data provided by a RGB camera and an IR thermal sensor. We describe the current state of fusing and processing of the collected data including the detection, classification (vital, non-vital), as well as sequential tracking and counting. We provide current counting results along with insights to future development concepts to improve the stated algorithms especially in terms of vital, nonvital classification and object recognition.

关键词： Multi sensor fusion 3D vision

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Soft body sensor networks: architectures and applications

Soft body sensor networks: Architectures and Applications

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IEEE International Conference on Networking, Sensing and Control

作者： Wentao Dong Xiao Cheng Daojin Yao School of Electrical and Automation Engineering East China Jiaotong University Nanchang China Rail Transportation Technology Innovation Center East China Jiaotong University Nanchang China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

Body sensor networks (BSNs) are designed to collect, process, and transmit the biological data from different parts of human body from wearable electronics with limited resources. Conventional BSN node is also designed and fabricated by the rigid sensors, printed circuit board (PCB), and integrated chip (IC), which are not compatible with the soft human skin. The concepts of soft body sensor networks (SBSNs) are given in the manuscript based on wireless sensor networks and flexible electronics. SBSN node is composed of soft sensors, flexible data modulation and process unit and flexible wireless communication unit, and all the components of SBSN node are designed to be bended and be compatible with soft skin surface by flexible electronic. The architecture of SBSNs are built with several layers: soft sensors, data collection, wireless communication, body could, data fusion algorithm and monitoring interface for further online healthy monitoring. Multisensory decision fusion algorithm has been presented with biometric and medical monitoring applications which is subsequently analyzed for medical diagnosis. Emotion recognitions are realized to predict healthy state based on SBSNs using the intelligent data fusion algorithms.

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