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31st IEEE Intelligent vehicles Symposium (Iv)

作者： Loehdefink, Jonas Baer, Andreas Schmidt, Nico M. Hueger, Fabian Schlicht, Peter Fingscheidt, Tim Tech Univ Carolo Wilhelmina Braunschweig Inst Commun Technol Schleinitzstr 22 D-38106 Braunschweig Germany Volkswagen Grp Innovat Berliner Ring 2 D-38440 Wolfsburg Germany

ISBN: (纸本)9781728166735

Cooperative perception with many sensors involved greatly improves the performance of perceptual systems in autonomous vehicles. However, the increasing amount of sensor data leads to a bottleneck due to limited capacity of vehicle-to-X (v2X) communication channels. We leverage lossy learned image compression by means of an autoencoder with adversarial loss function to reduce the overall bitrate. Our key contribution is to focus image compression on regions of interest (ROIs) governed by a binary mask. A transmitter-sided semantic segmentation network extracts semantically important classes being the basis for the generation of a ROI. A second key contribution is that the mask is not transmitted as side information, only the quantized bottleneck data is transmitted. To train the network, we use a loss function operating only on the pixels in the ROI. We report peak-signal-to-noise ratio (PSNR) both in the entire image and only in the ROI, evaluating various fusion architectures and fusion operations involving input image and mask. Showing the high generalizability of our approach, we achieve consistent improvements in the ROI in all experiments on the Cityscapes dataset.

关键词： Image compression

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visual sensing technologies in robotic welding: Recent research developments and future interests

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sensorS AND ACTUATORS A-PHYSICAL 2021年 320卷 112551-112551页

作者： Xu, Yanling Wang, Ziheng Shanghai Jiao Tong Univ Sch Mat Sci & Engn Shanghai Key Lab Mat Laser Proc & Modificat Shanghai 200240 Peoples R China Univ Penn Sch Engn & Appl Sci Philadelphia PA 19104 USA

vision-based sensors play an essential role in robotic welding, which attract increasing attention by scholars because of its non-contact characteristic, high precision, fast detection, and strong adaptability. This paper summarizes the recent research developments of vision sensor-based intelligent robotic welding and analyzes their characteristics and application scenarios in the welding process. The applications of vision sensing technologies are illustrated, especially in visual calibration, initial welding position guidance, weld seam tracking, weld pool monitoring, correlation deformation detection, and defect identification. At the end of this paper, the promising development directions of intelligent robotic welding technologies are discussed, including the hardware developments of vision sensors, optimization of image processing algorithms, the combination of passive vision and active vision, multi-sensor information fusion, and applications of artificial intelligence. (c) 2021 Elsevier B.v. All rights reserved.

关键词： Robotic welding vision sensor Research developments Initial welding position guidance Seam tracking Weld pool monitoring

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Data association between perception and v2v communication sensors

arXiv

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arXiv 2021年

作者： Cantas, Mustafa Ridvan Chand, Arpita Zhang, Hao Surnilla, Gopi Chandra Guvenc, Levent Automated Driving Laboratory Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States Ford Motor Company DearbornMI48124 United States Automated Driving Laboratory Department of Mechanical and Aerospace Engineering Department of Electrical and Computer Engineering Ohio State University ColumbusOH43210 United States

The connectivity between vehicles, infrastructure, and other traffic participants brings a new dimension to automotive safety applications. Soon all the newly produced cars will have vehicle to Everything (v2X) communication modems alongside the existing Advanced Driver Assistant Systems (ADAS). It is essential to identify the different sensor measurements for the same targets (Data Association) to use connectivity reliably as a safety feature alongside the standard ADAS functionality. Considering the camera is the most common sensor available for ADAS systems, in this paper, we present an experimental implementation of a Mahalanobis distance-based data association algorithm between the camera and the vehicle to vehicle (v2v) communication sensors. The implemented algorithm has low computational complexity and the capability of running in real-time. One can use the presented algorithm for sensor fusion algorithms or higher-level decision-making applications in ADAS modules. Copyright © 2021, The Authors. All rights reserved.

关键词： vehicle to vehicle communications

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Deep Neural Based Beamforming Techniques for Direction of Arrival (DOA) Estimation

Deep Neural Based Beamforming Techniques for Direction of Ar...

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International Symposium on Ocean Electronics (SYMPOL)

作者： Blessy C Simon Supriya M H Jayanthi v S Cochin University of Science and Technology Kochi India Rajagiri School of Engineering and Technology Kochi India

ISBN: (纸本)9781665436182

Most of the applications with SONAR are relied on the Direction of Arrival (DOA) estimation. It is an inevitable part in the scenarios of target detection and localization where the location of target is retrieved from the received sensor data. The paper models novel deep learning architectures for DOA es-timation and compares their performance with the existing DOA estimation algorithms like conventional beamforming, MvDR beamforming and MMSE estimation. The deep learning frame-works like Deep Neural Network (DNN) and Deep Convolutional Neural Network (DCNN) are deployed to recover the signal radiated from passive targets using multiple sensor arrays. The estimation is carried out for the SNR conditions ranging from −5dB to 20dB and the results are analyzed using MSE v/s SNR plot, waterfall diagrams and the time taken for execution.

关键词： Deep learning Marine technology Direction-of-arrival estimation Array signal processing Neural networks Sonar applications Estimation

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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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Dynamic probabilistic model checking for sensor validation in industry 4.0 applications 4

Dynamic probabilistic model checking for sensor validation i...

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4th IEEE International Conference on Smart Internet of Things, SmartIoT 2020

作者： Xin, Xin Keoh, Sye Loong Sevegnani, Michele Saerbeck, Martin University of Glasgow School of Computing Science Glasgow United Kingdom Tüv Süd Asia Pacific Digital Service Centre of Excellence Singapore Singapore

ISBN: (纸本)9781728165141

Industry 4.0 adopts Internet of Things (IoT) and service-oriented architectures to integrate Cyber-Physical Systems and Enterprise Planning into manufacturing operations. This kind of integration consists of a combination of connected sensors and run-Time control algorithms. Consequential control decisions are driven by sensor-generated data. Hence, the trustworthiness of the sensor network readings is increasingly crucial to guarantee the performance and the quality of a manufacturing task. However, existing methodologies to test such systems often do not scale to the complexity and dynamic nature of today's sensor networks. This paper proposes a novel run-Time verification framework combining sensor-level fault detection and system-level probabilistic model checking. This framework can rigorously quantify the trustworthiness of sensor readings, hence enabling formal reasoning for system failure prediction. We evaluated our approach on an industrial turn-mill machine equipped with a sensor network to monitor its main components continuously. The results indicate that the proposed verification framework involving the quantified sensor's trustworthiness enhances the accuracy of the system failure prediction. © 2020 IEEE.

关键词： Industry 4.0

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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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Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous vehicles

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sensorS 2021年第24期21卷 8272-8272页

作者： Minea, Marius Dumitrescu, Catalin Marian Costea, Ilona Madalina Univ Politehn Bucuresti Dept Telemat & Elect Transports Bucharest 060042 Romania

Background: The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution. Methods: This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram-EEG, electrocardiogram-ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine. Results: The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance. Conclusion: The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring v

关键词： automated vehicles connected vehicles driver condition monitoring safety stopping emergency automated call wireless sensor network

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Photoelectric dual mode sensing system based on one-step fabricated heterojunction artificial synapses device

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Materials Science and Engineering R: Reports 2025年 165卷

作者： Yang, Dong-Ping Li, Rui-Hao Tang, Xin-Gui Li, Dong-Liang Sun, Qi-Jun School of Physics and Optoelectronic Engineering & Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications Guangdong University of Technology Guangzhou510006 China

With the growing demand for intelligent perception systems in the IoT and big data era, multimodal sensing technologies face challenges including complex device architectures and low energy efficiency. This study proposes a TiO2-Nb: SrTiO3 heterojunction-based optoelectronic artificial synaptic device fabricated via one-step magnetron sputtering, enabling monolithic integration of sensing-memory-processing functionalities. Experimental results demonstrate that the device exhibits multilevel resistive state stability exceeding 10,000 seconds under electrical stimulation and achieves over seven non-volatile conductance states through optical modulation. A sensor-memristor fusion architecture with optoelectronic cooperative regulation was developed for intelligent transportation and emotion recognition applications. The system successfully realizes multidimensional vehicle monitoring (dynamic/speed/direction) and accomplishes three-category emotion classification by integrating facial features and acoustic signals, validating the effectiveness of multimodal information fusion. The device achieves audiovisual bimodal fusion perception through optoelectronic synergy, featuring CMOS-compatible fabrication that offers a scalable pathway for neuromorphic sensing systems. This work breaks the von Neumann energy-efficiency bottleneck via device-architecture co-innovation, providing a low-power solution for edge computing scenarios in smart transportation and human-machine interaction. © 2025 Elsevier B.v.

关键词： Information fusion

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Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study

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GEODERMA 2019年 341卷 111-128页

作者： Ji, Wenjun Adamchuk, viacheslav I. Chen, Songchao Su, Ahmad S. Mat Ismail, Ashraf Gan, Qianjun Shi, Zhou Biswas, Asim McGill Univ Dept Bioresource Engn Montreal PQ H9X 3V9 Canada Swedish Univ Agr Sci SLU Dept Soil & Environm Precis Agr & Pedometr SE-53223 Skara Sweden INRA Unite Infosol F-45075 Orleans France Univ Putra Malaysia Dept Agr Technol Serdang 43400 Selangor Malaysia McGill Univ Dept Food Sci & Agr Chem Montreal PQ H9X 3V9 Canada Zhejiang Univ Inst Appl Remote Sensing & Informat Technol Hangzhou 310058 Zhejiang Peoples R China Univ Guelph Sch Environm Sci Guelph ON N1G 2W1 Canada

In this research, proximal soil sensor data fusion was defined as a multifaceted process which integrates geospatially correlated data, or information, from multiple proximal soil sensors to accurately characterize the spatial complexity of soils. This has capability of providing improved understanding of soil heterogeneity for potential applications associated with crop production and natural resource management. To assess the potential of data fusion for the purpose of improving thematic soil mapping over the single sensor approach, data from multiple proximal soil sensors were combined to develop and validate predictive relationships with laboratory-measured soil physical and chemical properties. The work was conducted in an agricultural field with both mineral and organic soils. The integrated data included: topography records obtained using a real-time kinetic (RTK) global navigation satellite system (GNSS) receiver, apparent soil electrical conductivity (ECa) obtained using an electromagnetic induction sensor, and content of several naturally occurring radioisotopes detected using a mobile gamma-ray spectrometer. In addition, the soil profile data were collected using a commercial ruggedized multi-sensor platform carrying a visible and near-infrared (vis-NIR) optical sensor and a galvanic contact soil ECa sensor. The measurements were carried out at predefined field locations covering the entire study area identified from sensor measured a priori information on field elevation, ECa and gamma-ray count. The information was used to predict: soil organic matter (SOM), pH, lime buffer capacity (LBC), as well as concentration of phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and aluminum (Al). Partial least squares regressions (PLSRs) were used to predict soil properties from individual sensors and different sensor combinations (sensor data fusion). By integrating the data from all of the proximal soil sensors, SOM, pH, LBC, Ca, Mg, and Al were predic

关键词： vis-NIR spectroscopy Gamma-ray Apparent electrical conductivity sensor fusion Data fusion Partial least square regression Data mining algorithms

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