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International Carpathian control Conference (ICCC)

作者： Marek Sibielak Jarosław Konieczny Adam Smoter Department of Process Control Faculty of Mechanical Engineering and Robotics AGH University of Krakow Krakow Poland

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

The paper presents the synthesis of a nonlinear model of a wheeled vehicle suspension strut using an asymmetric (single-rod) electrohydraulic actuator. Due to differences in the active areas of the piston and the nonlinear nature of flows through the throttling orifices of the servo valve, the nonlinear model is characterised by increased complexity relative to systems using symmetric actuators (double rod). The paper covers the results of simulation studies to linearise the model and synthesise a linear quadratic controller based on the linearised model. The derived linear-quadratic controller was utilised to control a nonlinear model of the suspension strut. The control performance of the nonlinear active suspension strut model was compared with laboratory tests of the passive suspension system.

关键词： Suspensions (mechanical systems) Roads Orifices Wheels Process control Valves Mathematical models

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Meaningful human control of partially automated driving systems: Insights from interviews with Tesla users

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Transportation Research Part F: Traffic Psychology and Behaviour 2025年 113卷 213-236页

作者： Suryana, Lucas Elbert Nordhoff, Sina Calvert, Simeon Zgonnikov, Arkady van Arem, Bart Department of Transport and Planning Faculty of Civil Engineering and Geosciences Delft University of Technology Delft Netherlands Electric Vehicle Research Center Institute of Transportation Studies University of California Davis United States Department of Cognitive Robotics Faculty of Mechanical Engineering Delft University of Technology Delft Netherlands Centre for Meaningful Human Control Delft University of Technology Delft Netherlands

Partially automated driving systems are designed to perform specific driving tasks—such as steering, accelerating, and braking—while still requiring human drivers to monitor the environment and intervene when necessary. This shift of driving responsibilities from human drivers to automated systems raises concerns about accountability, particularly in scenarios involving unexpected events. To address these concerns, the concept of meaningful human control (MHC) has been proposed. MHC emphasises the importance of humans retaining oversight and responsibility for decisions made by automated systems. Despite extensive theoretical discussion of MHC in driving automation, there is limited empirical research on how real-world partially automated systems align with MHC principles. This study offers two main contributions: (1) an empirical evaluation of MHC in partially automated driving, based on 103 semi-structured interviews with users of Tesla's Autopilot and Full Self-Driving (FSD) Beta systems;and (2) a methodological framework for assessing MHC through qualitative interview data. We operationalise the previously proposed tracking and tracing conditions of MHC using a set of evaluation criteria to determine whether these systems support meaningful human control in practice. Our findings indicate that several factors influence the degree to which MHC is achieved. Failures in tracking—where drivers' expectations regarding system safety are not adequately met—arise from technological limitations, susceptibility to environmental conditions (e.g., adverse weather or inadequate infrastructure), and discrepancies between technical performance and user satisfaction. Tracing performance—the ability to clearly assign responsibility—is affected by inconsistent adherence to safety protocols, varying levels of driver confidence, and the specific driving mode in use (e.g., Autopilot versus FSD Beta). These findings contribute to ongoing efforts to design partially automated driving

关键词： Human engineering

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Design of an Embedded control System for a Linear MR Damper Powered by Energy Harvested from Vibrations

Design of an Embedded Control System for a Linear MR Damper ...

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International Carpathian control Conference (ICCC)

作者： Łukasz Jastrzębski Arkadiusz Kozieł Department of Process Control Faculty of Mechanical Engineering and Robotics AGH University of Krakow Krakow Poland

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

The work concerns design and realization of embedded control system (ECS) dedicated to a self-powered vibration reduction system equipped with a magnetorheological damper. Unlike a typical MR damper control system whose electrical circuits are powered from an external energy source, the developed system is powered by energy extracted from a vibrating structure and converted into electrical energy using an electromagnetic harvester. The ECS contains a rectifier, a driver unit, a 32-bit microcontroller, and an internal power supply system which are integrated in an engineered PCB. The sensors (MEMS accelerometers) employed in the control system are powered by DC voltage provided by the ECS. The paper presents the structure, components, and functions of the ECS

关键词： Vibrations Micromechanical devices Power supplies Microcontrollers Rectifiers control systems Shock absorbers

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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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FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training

FlashMix: Fast Map-Free LiDAR Localization via Feature Mixin...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Raktim Gautam Goswami Naman Patel Prashanth Krishnamurthy Farshad Khorrami Department of Electrical and Computer Engineering Control/Robotics Research Laboratory (CRRL) NYU Tandon School of Engineering Brooklyn NY USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Map-free LiDAR localization systems accurately localize within known environments by predicting sensor position and orientation directly from raw point clouds, eliminating the need for large maps and descriptors. However, their long training times hinder rapid adaptation to new environments. To address this, we propose FlashMix, which uses a frozen, scene-agnostic backbone to extract local point descriptors, aggregated with an MLP mixer to predict sensor pose. A buffer of local descriptors is used to accelerate training by orders of magnitude, combined with metric learning or contrastive loss regularization of aggregated descriptors to improve performance and convergence. We evaluate FlashMix on various LiDAR localization benchmarks, examining different regularizations and aggregators, and demonstrating its effectiveness for rapid and accurate LiDAR localization in real-world scenarios. The code is available at https://***/raktimgg/FlashMix.

关键词： Location awareness Training Measurement Point cloud compression Laser radar Accuracy Pose estimation Benchmark testing Mixers Convergence

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Process Assurance for Object Detection Through Deep Neural Networks to Accomplish the Autonomous Aerial Refueling Task

Process Assurance for Object Detection Through Deep Neural N...

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2023 IEEE International Automated Vehicle Validation Conference, IAVVC 2023

作者： Clement, Danielle Mottino, Sarah Costello, Donald Aeronautics Division Lockheed Martin Corporation Fort WorthTX United States United States Naval Academy Weapons Robotics and Control Engineering Department AnnapolisMD United States

This paper presents a high level discussion on certifying of a deep neural network to build situational awareness for an autonomous vehicle to complete the aerial refueling task through object detection verification a... 详细信息

ISBN: (纸本)9798350322538

关键词： Object detection

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Analysis of YouTube Video Comments with NLP Methods 16

Analysis of YouTube Video Comments with NLP Methods

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16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

作者： Mironela, Pirnau Iustin, Priescu Mihaela, Priescu Catalina Claudiu, Pirnau Daniel, Ghiculescu Liviu 'Titu Maiorescu' University Faculty of Informatics Department of Informatics Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Industrial Engineering and Robotics Manufacturing Engineering Department Bucharest Romania

ISBN: (纸本)9798350371154

This study proposes methods that can be used to examine and interpret comments that users have made after watching videos on YouTube on a particular topic. YouTube tutorials are very popular among young people. They have become an important pillar in informal education, thus contributing to the rapid acquisition of skills and knowledge. Under these circumstances, we were interested in analysing the YouTube videos comments, knowing that the platform has a continuous increase in popularity, which is also due to the opportunities of sharing them. Since ChatGPT-themed YouTube videos have seen a significant surge in popularity since 2022, we were interested to analyse some videos that approach the topics of 'ChatGPT, AI clone, AI robot and Deep Learning.' © 2024 IEEE.

关键词： Deep learning

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Nullspace Adaptive Identification of Plant and Actuator Model Parameters for Underactuated Ground Vehicles: Theory and Experimental Evaluation ⁎

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IFAC-PapersOnLine 2024年第28期58卷 1097-1102页

作者： Allan H. Elsberry Jeremy J. Dawkins Annie M. Mao Louis L. Whitcomb Department of Weapons Robotics and Control Engineering United States Naval Academy Annapolis MD 21401 USA Department of Mechanical Engineering Johns Hopkins University Baltimore MD 21218 USA

This paper reports a novel Nullspace Adaptive Identification (NSAID) algorithm to estimate plant and actuator model parameters for an underactuated ground vehicle with dynamics represented by a 3 degree-of-freedom second-order dynamic model, and reports an evaluation of its performance in simulation and experiments. Precise identification of ground vehicle plant and actuator model parameters is critical for physically realistic model-based simulation, control, navigation, and fault detection. However, ground vehicle plant model parameters, such as vehicle mass, moments of inertia, tire cornering stiffness and actuator model parameters such as motor torque constants are generally not possible to estimate a priori from first principles, and can change with varying payloads, configurations, and driving environments, and thus must be determined experimentally. Some parameter identification methods such as least squares regression depend on acceleration measurements, and most identification methods assume a priori knowledge of actuator parameters. In contrast, NSAID estimates plant and actuator parameters simultaneously, does not require acceleration measurements, can be utilized offline or online during vehicle operation, and can be applied with open or closed-loop control.

关键词： Nonlinear Adaptive control Vehicle Dynamics Model Parameter Identification

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High-Dimensional controller Tuning through Latent Representations

High-Dimensional Controller Tuning through Latent Representa...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Alireza Sarmadi Prashanth Krishnamurthy Farshad Khorrami Department of Electrical and Computer Engineering Control/Robotics Research Laboratory (CRRL) NYU Tandon School of Engineering Brooklyn NY

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we propose a method to automatically and efficiently tune high-dimensional vectors of controller parameters. The proposed method first learns a mapping from the high-dimensional controller parameter space to a lower dimensional space using a machine learning-based algorithm. This mapping is then utilized in an actor-critic framework using Bayesian optimization (BO). The proposed approach is applicable to complex systems (such as quadruped robots). In addition, the proposed approach also enables efficient generalization to different control tasks while also reducing the number of evaluations required while tuning the controller parameters. We evaluate our method on a legged locomotion application. We show the efficacy of the algorithm in tuning the high-dimensional controller parameters and also reducing the number of evaluations required for the tuning. Moreover, it is shown that the method is successful in generalizing to new tasks and is also transferable to other robot dynamics.

关键词： Legged locomotion Machine learning algorithms Heuristic algorithms Dynamics Aerospace electronics Vectors Quadrupedal robots

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Degradable Tracking System based on Hardware Multi-Model Estimators

Degradable Tracking System based on Hardware Multi-Model Est...

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2022 Resilience Week, RWS 2022

作者： Kiriakidis, Kiriakos Croteau, Brien Severson, Tracie Rodriguez-Seda, Erick Robucci, Ryan Islam, Riadul Rahman, Saad United States Naval Academy Department of Weapons Robotics and Control Engineering Department of Cyber Science United States University of Maryland Department of Computer Science and Electrical Engineering United States

ISBN: (数字)9781665488198

ISBN: (纸本)9781665488198

Sensing systems onboard unmanned vehicles operate in an environment of constrained computational resources. A cyber-Attack may primarily aim to degrade these computing devices and, ultimately, incapacitate the sensing system itself. To prepare a prototype tracking system for degradation, this paper proposes distributed hardware implementation of a Multiple Model estimator on two FPGA units and, after an attack, adaptation of the estimator by leveraging Dynamic Partial Reconfiguration of the single surviving FPGA. The method ensures that the most likely models of the estimator are loaded on to the fabric of the surviving FPGA with minimal interruption. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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