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TechRxiv 2023年

作者： Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University Krakow Poland

In this paper we have addressed the implementation of the accumulation and projection of high-resolution event data stream (HD – 1280 × 720 pixels) onto the image plane in FPGA devices. The results confirm the feasibility of this approach, but there are a number of challenges, limitations and trade-offs to be considered. The required hardware resources of selected data representations, such as binary frame, event frame, exponentially decaying time surface and event frequency, were compared with those available on several popular platforms from AMD Xilinx. The resulting event frames can be used for typical vision algorithms, such as object classification and detection, using both classical and deep neural network methods. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

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Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithms

arXiv

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arXiv 2023年

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Poland

Object detection in 3D is a crucial aspect in the context of autonomous vehicles and drones. However, prototyping detection algorithms is time-consuming and costly in terms of energy and environmental impact. To address these challenges, one can check the effectiveness of different models by training on a subset of the original training set. In this paper, we present a comparison of three algorithms for selecting such a subset – random sampling, random per class sampling, and our proposed MONSPeC (Maximum Object Number Sampling per Class). We provide empirical evidence for the superior effectiveness of random per class sampling and MONSPeC over basic random sampling. By replacing random sampling with one of the more efficient algorithms, the results obtained on the subset are more likely to transfer to the results on the entire dataset. The code is available at: https://***/vision-agh/monspec. © 2023, CC BY.

关键词： Object detection

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High-definition event frame generation using SoC FPGA devices

High-definition event frame generation using SoC FPGA device...

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Krzysztof Blachut Tomasz Kryjak Department of Automatic Control and Robotics Embedded Vision Systems Group AGH University of Krakow Poland

In this paper we have addressed the implementation of the accumulation and projection of high-resolution event data stream (HD – 1280×720 pixels) onto the image plane in FPGA devices. The results confirm the feasibility of this approach, but there are a number of challenges, limitations and trade-offs to be considered. The required hardware resources of selected data representations, such as binary frame, event frame, exponentially decaying time surface and event frequency, were compared with those available on several popular platforms from AMD Xilinx. The resulting event frames can be used for typical vision algorithms, such as object classification and detection, using both classical and deep neural network methods.

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Detection-segmentation convolutional neural network for autonomous vehicle perception

Detection-segmentation convolutional neural network for auto...

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International Conference on Methods and Models in Automation and robotics (MMAR)

作者： Maciej Baczmanski Robert Synoczek Mateusz Wasala Tomasz Kryjak Department of Automatic Control and Robotics Embedded Vision Systems Group AGH University of Krakow Poland

Object detection and segmentation are two core modules of an autonomous vehicle perception system. They should have high efficiency and low latency while reducing computational complexity. Currently, the most commonly used algorithms are based on deep neural networks, which guarantee high efficiency but require high-performance computing platforms. In the case of autonomous vehicles, i.e. cars, but also drones, it is necessary to use embedded platforms with limited computing power, which makes it difficult to meet the requirements described above. A reduction in the complexity of the network can be achieved by using an appropriate: architecture, representation (reduced numerical precision, quantisation, pruning), and computing platform. In this paper, we focus on the first factor – the use of so-called detection-segmentation networks as a component of a perception system. We considered the task of segmenting the drivable area and road markings in combination with the detection of selected objects (pedestrians, traffic lights, and obstacles). We compared the performance of three different architectures described in the literature: MultiTask V3, HybridNets, and YOLOP. We conducted the experiments on a custom dataset consisting of approximately 500 images of the drivable area and lane markings, and 250 images of detected objects. Of the three methods analysed, MultiTask V3 proved to be the best, achieving 99% mAP 50 for detection, 97% MIoU for drivable area segmentation, and 91% MIoU for lane segmentation, as well as 124 fps on the RTX 3060 graphics card. This architecture is a good solution for embedded perception systems for autonomous vehicles. The code is available at: https://***/vision-agh/MMAR_2023.

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Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithms

TechRxiv

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TechRxiv 2023年

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Poland

关键词： Object detection

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Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithms

Comparative study of subset selection methods for rapid prot...

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International Conference on Methods and Models in Automation and robotics (MMAR)

作者： Konrad Lis Tomasz Kryjak Department of Automatic Control and Robotics Embedded Vision Systems Group AGH University of Krakow Poland

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Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency

arXiv

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arXiv 2024年

作者： Sánchez, Manuel Muñoz van der Ploeg, Chris Smit, Robin Elfring, Jos Silvas, Emilia van de Molengraft, René Department of Mechanical Engineering Robotics Group Japan Control Systems Technology Group Eindhoven University of Technology Eindhoven Netherlands Department of Integrated Vehicle Safety Helmond Netherlands VDL CropTeq Robotics Eindhoven Netherlands

Predicting the movement of other road users is beneficial for improving automated vehicle (AV) performance. However, the relationship between the time horizon associated with these predictions and AV performance remains unclear. Despite the existence of numerous trajectory prediction algorithms, no studies have been conducted on how varying prediction lengths affect AV safety and other vehicle performance metrics, resulting in undefined horizon requirements for prediction methods. Our study addresses this gap by examining the effects of different prediction horizons on AV performance, focusing on safety, comfort, and efficiency. Through multiple experiments using a state-of-the-art, risk-based predictive trajectory planner, we simulated predictions with horizons up to 20 seconds. Based on our simulations, we propose a framework for specifying the minimum required and optimal prediction horizons based on specific AV performance criteria and application needs. Our results indicate that a horizon of 1.6 seconds is required to prevent collisions with crossing pedestrians, horizons of 7-8 seconds yield the best efficiency, and horizons up to 15 seconds improve passenger comfort. We conclude that prediction horizon requirements are application-dependent, and recommend aiming for a prediction horizon of 11.8 seconds as a general guideline for applications involving crossing pedestrians. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Tightly-Coupled Factor Graph Formulation For Radar-Inertial Odometry

Tightly-Coupled Factor Graph Formulation For Radar-Inertial ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Jan Michalczyk Julius Quell Florian Steidle Marcus G. Müller Stephan Weiss Control of Networked Systems Group University of Klagenfurt Austria Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we present a Radar-Inertial Odometry (RIO) method based on the nonlinear optimization of factor graphs in a sliding window fashion. Our method makes use of a light-weight, low-power, inexpensive and commonly available hardware enabling easy deployment on small Unmanned Aerial Vehicles (UAV)s. We keep the state estimation problem bounded by employing partial marginalization of the oldest states, rendering the method real-time capable. We compare the implemented approach to the state-of-the-art multi-state Extended Kalman Filter (EKF)-based method in a one-to-one fashion. That is, we implemented in a single custom C++ RIO framework both estimation back-ends with all other parts shared and thus identical for a fair direct comparison. In the real-world flight experiments, we compare the two methods and show that both perform similarly in terms of accuracy when the linearization point is not far from the true state. Upon wrong initialization, the factor graph approach heavily outperforms the EKF approach. We also acknowledge that the influence of undetected outliers can overwhelm the inherent benefits of the nonlinear optimization approach leading to the insight that the estimator front-end has an important (and often underestimated) role in the overall performance. The open source code and datasets can be found here: https://***/aau-cns/aaucns_rio.

关键词： Accuracy Uncertainty Autonomous aerial vehicles Software Real-time systems Vectors Trajectory Odometry Optimization Convergence

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Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency

Prediction Horizon Requirements for Automated Driving: Optim...

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IEEE Symposium on Intelligent Vehicle

作者： Manuel Muñoz Sánchez Chris van der Ploeg Robin Smit Jos Elfring Emilia Silvas Rene van de Molengraft Mechanical Engineering Department Robotics group Mechanical Engineering Department Dynamics and Control Group Integrated Vehicle Safety Department TNO Helmond The Netherlands VDL CropTeq Robotics Eindhoven The Netherlands Mechanical Engineering Department Control Systems Technology group Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Predicting the movement of other road users is beneficial for improving automated vehicle (AV) performance. However, the relationship between the horizon of these predictions and AV performance remains unclear. Despite the existence of numerous trajectory prediction algorithms, no studies have been conducted on how varying prediction horizons affect AV safety and other vehicle performance metrics, resulting in undefined horizon requirements for prediction methods. Our study addresses this gap by examining the effects of different prediction horizons on AV performance, focusing on safety, comfort, and efficiency. Through multiple experiments using a state-of-the-art, risk-based predictive trajectory planner, we simulated predictions with horizons up to 20 seconds. Based on our simulations, we propose a framework for specifying the minimum required and optimal prediction horizons depending on specific AV performance criteria and application needs. Our results indicate that a horizon of 1.6 seconds is required to prevent collisions with crossing pedestrians, horizons of 7-8 seconds yield the best efficiency, and horizons up to 15 seconds improve passenger comfort. We conclude that prediction horizon requirements are application-dependent, and recommend aiming for a prediction horizon of 11.8 seconds as a general guideline for applications involving crossing pedestrians.

关键词： Measurement Pedestrians Intelligent vehicles Roads Pipelines Predictive models Prediction algorithms

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EXPLOITING EQUIVARIANCE IN THE DESIGN OF TRACKING controlLERS FOR EULER-POINCARE systems ON MATRIX LIE groupS

arXiv

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arXiv 2024年

作者： Hampsey, Matthew Banavar, Ravi van Goor, Pieter Mahony, Robert Systems Theory and Robotics Group Australian National University ACT2601 Australia Interdisciplinary Programme in Systems and Control Engineering Indian Institute of Technology Bombay India

The trajectory tracking problem is a fundamental control task in the study of mechanical systems. A key construction in tracking control is the error or difference between an actual and desired trajectory. This construction also lies at the heart of observer design and recent advances in the study of equivariant systems have provided a template for global error construction that exploits the symmetry structure of a group action if such a structure exists. Hamiltonian systems are posed on the cotangent bundle of configuration space of a mechanical system and symmetries for the full cotangent bundle are not commonly used in geometric control theory. In this paper, we propose a group structure on the cotangent bundle of a Lie-group and leverage this to define momentum and configuration errors for trajectory tracking drawing on recent work on equivariant observer design. We show that this error definition leads to error dynamics that are themselves "Euler-Poincare like" and use these to derive simple, almost global trajectory tracking control for fully-actuated Euler-Poincare systems on a Lie-group state space. Copyright © 2024, The Authors. All rights reserved.

关键词： Lie groups

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