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Mediterranean Electrotechnical Conference (MELECON)

作者： Mohammad Zarei Andrey Solovov Vishal Gautam Bruno Ferreira Gustavo Assunção Antonio Marin-Hernandez Paulo Menezes Electrical and Computer Engineering Department University of Coimbra Coimbra Portugal Institute of Systems and Robotics University of Coimbra Coimbra Portugal Universidad Veracruzana Xalapa Mexico

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

Human-robot interaction is becoming a necessary part of the industry to achieve higher efficiency, productivity, and comfort. However, to ensure human safety, the awareness of human workers has to be under supervision. In this research, we defined a new function to estimate awareness through eye contact between the worker and the robot. This mechanism exploits a proposed robust and real-time eye contact classifier based on SeNet-50, that outperforms previous proposals based on ResNet-18.

关键词： Productivity Industries Service robots Human-robot interaction Collaboration Real-time systems Safety

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CUPREDS: Multi-sensor Point Cloud Mapping for Local Navigation

CUPREDS: Multi-sensor Point Cloud Mapping for Local Navigati...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Carlos Tojal Luis Conde Bento Paulo Peixoto Institute of Systems and Robotics University of Coimbra Coimbra Portugal ESTG Polytechnic Institute of Leiria Leiria Portugal Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Robot mapping usually benefits from the combination of several sensors. However, most algorithms for Simultaneous Localization And Mapping using point clouds only consider a single point cloud as input. There are also scenarios where SLAM is not needed and a simple local map representation without probabilities is preferred, for example due to throughput requirements. For these scenarios, software is needed that combines multiple point clouds into a unified, short-lived, robot-centric representation. Mapping software is only one tool in the software stack and should not compromise other components, which is why its performance-oriented design is of utmost importance. CUPREDS presents a ROS package for local mapping that prioritizes robustness and performance, with minimal overhead for its internal mechanisms.

关键词： Point cloud compression Simultaneous localization and mapping Navigation Software architecture Software algorithms Throughput Software

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Interaction-aware Maneuver Prediction for Autonomous Vehicles using Interaction Graphs

Interaction-aware Maneuver Prediction for Autonomous Vehicle...

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IEEE Symposium on Intelligent Vehicle

作者： Iago Pachêco Gomes Cristiano Premebida Denis Fernando Wolf Institute of Mathematics and Computer Science University of São Paulo Brazil Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal

Intention prediction (IP) is a challenging task for intelligent vehicle’s perception systems. IP provides the likelihood, or probability, of a target vehicle to perform a maneuver subjected to a finite set of possibilities. There are many factors that influence the decision-making process of a driver, which should be considered in a prediction framework. In addition, the lack of labeled large-scale dataset with maneuver annotation imposes another challenge to the task. This paper proposes an Interaction-aware Maneuver Prediction framework, called IAMP, using interaction graphs to extract complex interaction features from traffic scenes. In addition, we explored a semi-supervised approach called Noisy Student to take advantage of unlabeled data in the training step. Experimental results show relevant improvement when using unlabeled data, increasing the average performance of a classifier by 7.17% of accuracy. Moreover, this approach also made it possible to obtain an intention predictor with similar results to a classifier., even when using a shorter observation horizon.

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Late-Fusion Multimodal Human Detection Based on RGB and Thermal Images for Robotic Perception

Late-Fusion Multimodal Human Detection Based on RGB and Ther...

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European Conference on Mobile Robots (ECMR)

作者： Elísio Sousa Kennedy O. S. Mota Iago P. Gomes Luís Garrote Denis F. Wolf Cristiano Premebida Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Institute of Mathematics and Computer Science University of São Paulo Brazil

This paper addresses the problem of detecting humans in RGB and Thermal (long-wave IR) images taken by cameras mounted onboard a mobile robot. Human/Pedestrian detection is currently one of the most pertinent object detection problems, mainly due to safety concerns in autonomous vehicles. The majority of approaches apply deep-learning techniques based solely on RGB images. However, they have a few shortcomings, namely that during foggy weather, nighttime, and low-light scenarios, these images may not contain sufficient information. To address these issues, this work studies the use of thermal cameras as a complementary source of information for human detection in indoor and outdoor environments. The proposed approach uses YOLOv5 to detect pedestrians in both thermal and RGB images. Moreover, the different modalities are combined using early and late fusion techniques. Evaluation of the proposed approach is carried out in the FLIR Aligned dataset and in a new in-house dataset. Results indicate that the use of fusion techniques highlights a promising way to improve the overall performance in this application domain.

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Interaction-Aware Maneuver Intention Prediction Using Gated Mixture-of-Experts Attention Mechanism

Interaction-Aware Maneuver Intention Prediction Using Gated ...

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International Conference on Intelligent Transportation

This paper addresses the task of predicting the behavior of traffic participants, which involves complexities such as road geometry and agent interactions. To overcome these challenges, this paper presents a novel framework called AIMP (Attention-based Interaction-aware Maneuver Prediction). AIMP utilizes interaction graphs to extract intricate interaction features from traffic scenes. The framework incorporates a Gated Mixture-of-Experts Attention Mechanism, which combines information from road geometry, interaction patterns, and motion dynamics. This fusion process also considers prior maneuver intention estimations, enhancing both explainability and informativeness. Experimental results highlight a performance enhancement (approximately 2% ∼ 9% of accuracy) of the proposed AIMP framework compared to alternative fusion methods.

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A data acquisition system to capture extreme human driving behaviour

A data acquisition system to capture extreme human driving b...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Ines Rito Lima José Santos-Victor Department of Electrical and Computer Engineering Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Institute for Systems and Robotics (ISR)) Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

Assessing and encoding human driving expertise while handling challenging events, primarily during drifts, can unveil new insights regarding the professional response to extreme driving which can be integrated into Autonomous Vehicles (AVs) safety procedures. Considering the complex and dynamic data related to both human behaviour and car devices, this research area urges the inclusion of Artificial Intelligence (AI) strategies for the extraction of interdependencies and patterns that encode human driving expertise. To do so, acquiring data that comprises the pilot and vehicle performances is of utmost importance. The present work proposes a specially designed data acquisition system to collect data regarding the vehicle dynamic movement and the associated human behaviour while driving in challenging environments. Distinct sensing devices are placed across the vehicle to retrieve the vehicle position, acceleration, angular rotation, steering orientation, among others. Data was collected from professional drivers during official racing events, and initial processing results show spatial and temporal coherence with the racing performances. The preliminary analysis of the data also reveals potentially extreme events during the race.

关键词： Performance evaluation Data acquisition Robot sensing systems Encoding Sensors Safety Data mining

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Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks

arXiv

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arXiv 2024年

作者： Doostmohammadian, Mohammadreza Pequito, Sérgio Mechatronics Department Faculty of Mechanical Engineering Semnan University Semnan Iran Department of Electrical and Computer Engineering Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Portugal

Distributed optimization finds many applications in machine learning, signal processing, and control systems. In these real-world applications, the constraints of communication networks, particularly limited bandwidth, necessitate implementing quantization techniques. In this paper, we propose distributed optimization dynamics over multi-agent networks subject to logarithmically quantized data transmission. Under this condition, data exchange benefits from representing smaller values with more bits and larger values with fewer bits. As compared to uniform quantization, this allows for higher precision in representing near-optimal values and more accuracy of the distributed optimization algorithm. The proposed optimization dynamics comprise a primary state variable converging to the optimizer and an auxiliary variable tracking the objective function's gradient. Our setting accommodates dynamic network topologies, resulting in a hybrid system requiring convergence analysis using matrix perturbation theory and eigenspectrumanalysis. © 2024, CC BY.

关键词： Support vector machines

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A Scalable Real-time Semantic Segmentation Network for Autonomous Driving 1

A Scalable Real-time Semantic Segmentation Network for Auton...

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1st Workshop on Advanced Multimedia Computing for Smart Manufacturing and engineering, AMC-SME 2023

作者： Liu, Jun Wu, Chao Yuan, Geng Niu, Wei Zhang, Wenbin Song, Houbing Herbert Robotics Institute of School of Computer Science Carnegie Mellon University Pittsburgh United States Department of Electrical & Computer Engineering Northeastern University Boston United States School of Computing University of Georgia Athens United States Department of Computer Science Michigan Technological University Houghton United States Department of Information Systems University of Maryland Baltimore County Baltimore United States

ISBN: (纸本)9798400702730

Computational resources are limited on real-time embedded devices, so the available computing cost of deployment on the target platform must be considered. We develop a feature extraction module based on the MobileNet backbone that can be adjusted in terms of computational complexity and capacity using the depth multiplier parameter, classifier depth, and kernel depth. These three parameters allow us to control the count of channels within the network, effectively managing the model's capacity and computational requirements. To achieve semantic segmentation, we incorporate additional components, such as an extension module. This extension module typically includes 1x1 pointwise convolutional layers for pixel-level classification and a transposed convolutional layer for upsampling the output to the original input image size. By combining the feature extraction module with this extension module, we can create a complete architecture capable of performing semantic segmentation tasks. The feature extraction module provides the initial feature extraction and the extension module adds the necessary components for accurate pixel-wise classification and upsampling. Compared to Hardware-aware Neural Architecture Search (NAS), pruning, runtime pruning, and knowledge distillation methods, our model has several advantages in terms of modular design, structural controllability, ease of implementation, and cost-effectiveness. Our computational efficiency, as measured by FLOPS, is highly competitive. Our method is distinguished by solving the problem of MobileNet's inability to adjust the size and number of convolution kernels. It achieves this through adaptable parameter tuning, including MobileNet's depth multiplier, the kernel size in the FCN head's Separable Convolution layer, and the depth of the first Point-wise Convolution layer. These adjustments are customized to match hardware's max multiply-accumulates (MACs), optimizing network capacity and maximizing resource utilizat

关键词： Feature extraction

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Hyperbolic Delaunay Geometric Alignment

arXiv

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arXiv 2024年

作者： Medbouhi, Aniss Aiman Marchetti, Giovanni Luca Polianskii, Vladislav Kravberg, Alexander Poklukar, Petra Varava, Anastasia Kragic, Danica Division of Robotics Perception and Learning Department of Intelligent Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resulting hyperbolic data representations. To this end, we propose Hyperbolic Delaunay Geometric Alignment (HyperDGA) – a similarity score for comparing datasets in a hyperbolic space. The core idea is counting the edges of the hyperbolic Delaunay graph connecting datapoints across the given sets. We provide an empirical investigation on synthetic and real-life biological data and demonstrate that HyperDGA outperforms the hyperbolic version of classical distances between sets. Furthermore, we showcase the potential of HyperDGA for evaluating latent representations inferred by a Hyperbolic Variational Auto-Encoder. © 2024, CC BY.

关键词： Geometry

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Bilateral-Head Region-Based Convolutional Neural Networks: A Unified Approach for Incremental Few-Shot Object Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4376-4390页

作者： Li, Yiting Zhu, Haiyue Tian, Sichao Ma, Jun Xiang, Cheng Vadakkepat, Prahlad National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research 138634 Singapore Peking Union Medical College Institute of Medicinal Plant Development Chinese Academy of Medical Sciences Beijing100193 China Robotics and Autonomous Systems Thrust Guangzhou511453 China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong SAR Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen Futian518045 China

Practical object detection systems are highly desired to be open-ended for learning on frequently evolved datasets. Moreover, learning with little supervision further adds flexibility for real-world applications such as autonomous driving and robotics, where large-scale datasets could be prohibitive or expensive to obtain. However, continual adaption with small training examples often results in catastrophic forgetting and dramatic overfitting. To address such issues, a compositional learning system is proposed to enable effective incremental object detection from nonstationary and few-shot data streams. First of all, a novel bilateral-head framework is proposed to decouple the representation learning of base (pretrained) and novel (few-shot) classes into separate embedding spaces, which takes care of novel concept integration and base knowledge retention simultaneously. Moreover, to enhance learning stability, a robust parameter updating rule, i.e., recall and progress mechanism, is carried out to constrain the optimization trajectory of sequential model adaption. Beyond that, to enforce intertask class discrimination with little memory burden, we present a between-class regularization method that expands the decision space of few-shot classes for constructing unbiased feature representation. Final, we deeply investigate the incomplete annotation issue considering the realistic scenario of incremental few-shot object detection (iFSOD) and propose a semisupervised object labeling mechanism to accurately recover the missing annotations for previously encountered classes, which further enhances the robustness of the target detector to counteract catastrophic forgetting. Extensive experiments conducted on both Pascal visual object classes dataset (VOC) and microsoft common objects in context dataset (MS-COCO) datasets demonstrate the effectiveness of our method. © 2020 IEEE.

关键词： Feature extraction

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