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42nd IEEE/ACM International Conference on Computer-Aided design, ICCAD 2023

作者： Kumar, Shubham Chauhan, Yogesh Singh Amrouch, Hussam Stuttgart Germany Indian Institute of Technology Kanpur Department of Electrical Engineering India Ai Processor Design Germany Munich Institute of Robotics and Machine Intelligence Tum Germany

ISBN: (纸本)9798350315592

Monolithic three-dimensional (M3D) integration signifies a notable technological leap by providing solutions of high density and energy efficiency, particularly for ultra-efficient edge AI solutions. Among emerging technologies, ferroelectric thin-film transistors (FeTFTs) have attracted substantial interest due to their potential in neuromorphic computing and their compatibility with back-end-of-the-line (BEOL) fabrication processes. Nevertheless, the challenge of M3D integrated circuits lies in elevated temperatures resulting from limited heat dissipation across various stacked tiers, subsequently exerting adverse effects on system reliability. In this work, we explore how compute-in-memory (CIM) architectures can be realized using BEOL FeTFT to accelerate deep learning applications. We demonstrate how generated temperature impacts the reliability and ultimately degrade the inference accuracy. To achieve that, we have modified the open-source '3D+NeuroSim' framework to introduce FeTFT and then employed it to estimate the key figures of merit, such as throughput, power density, area, etc. Then, we perform a comprehensive thermal analysis for the simulated M3D architecture to explore how the DNN accuracy will be impacted due to the generated heat. We also demonstrate how FeTFT devices can be accurately modeled using TCAD simulations and how run-time variability (due to temperature effects) and design-time variability (due to process variation) impact the reliability of FeTFT transistors. © 2023 IEEE.

关键词： Thermoanalysis

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Snapshot: Towards Application-Centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments

Snapshot: Towards Application-Centered Models for Pedestrian...

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IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Nico Uhlemann Yipeng Zhou Tobias Simeon Mohr Markus Lienkamp Technical University of Munich Germany School of Engineering & Design Institute of Automotive Technology and Munich Institute of Robotics and Machine Intelligence

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

This paper explores pedestrian trajectory prediction in urban traffic while focusing on both model accuracy and real-world applicability. While promising approaches exist, they often revolve around pedestrian datasets excluding traffic-related information, or resemble architectures that are either not real-time capable or robust. To address these limitations, we first introduce a dedicated benchmark based on Argoverse 2, specifically targeting pedestrians in traffic environments. Following this, we present Snapshot, a modular, feed-forward neural network that outperforms the current state of the art, reducing the Average Displacement Error (ADE) by 8.8 % while utilizing significantly less information. Despite its agent-centric encoding scheme, Snapshot demonstrates scalability, real-time performance, and robustness to varying motion histories. Moreover, by integrating Snapshot into a modular autonomous driving software stack, we showcase its real-world applicability. 1 1 https://***/TUMFTM/Snapshot

关键词： Pedestrians Scalability Full stack Focusing Predictive models Real-time systems Robustness Trajectory History Autonomous vehicles

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Scenario Understanding of Traffic Scenes Through Large Visual Language Models

Scenario Understanding of Traffic Scenes Through Large Visua...

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IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Esteban Rivera Jannik Lübberstedt Nico Uhlemann Markus Lienkamp Technical University of Munich Germany School of Engineering & Design Institute of Automotive Technology and Munich Institute of Robotics and Machine Intelligence

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Deep learning models for autonomous driving, encompassing perception, planning, and control, depend on vast datasets to achieve their high performance. However, their generalization often suffers due to domain-specific data distributions, making an effective scene-based categorization of samples necessary to improve their reliability across diverse domains. Manual captioning, though valuable, is both laborintensive and time-consuming, creating a bottle-neck in the data annotation process. Large Visual Language Models (LVLMs) present a compelling solution byautomating image analysis and categorization through contextual queries, often without requiring retraining for new categories. In this study, we evaluate the capabilities of LVLMs, including GPT-4 and LLaVA, to understand and classify urban traffic scenes on both an inhouse dataset and the BDD100K. We propose a scalable captioning pipeline that integrates state-of-the-art models, enabling a flexible deployment on new datasets. Our analysis, combining quantitative metrics with qualitative insights, demonstrates the effectiveness of LVLMs to understand urban traffic scenarios and highlights their potential as an efficient tool for data-driven advancements in autonomous driving.

关键词： Point cloud compression Visualization Solid modeling Three-dimensional displays Predictive models Solids Cognition Planning Reliability Autonomous vehicles

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Motion planning for robotics:A review for sampling-based planners

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Biomimetic intelligence & robotics 2025年第1期5卷 15-34页

作者： Liding Zhang Kuanqi Cai Zewei Sun Zhenshan Bing Chaoqun Wang Luis Figueredo Sami Haddadin Alois Knoll School of Computation Information and Technology(CIT)Technical University of MunichMunich 85748Germany Munich Institute of Robotics and Machine Intelligence(MIRMI) Technical University of MunichMunich 80992Germany School of Control Science and Engineering Shandong UniversityJinan 250061China School of Computer Science University of NottinghamNottingham 315199United Kingdom

Recent advancements in robotics have transformed industries such as manufacturing,logistics,surgery,and planetary exploration.A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length,sweep area,execution time,and energy *** the available algorithms,sampling-based methods have gained the most traction in both research and industry due to their ability to handle complex environments,explore free space,and offer probabilistic completeness along with other formal *** their widespread application,significant challenges still *** advance future planning algorithms,it is essential to review the current state-of-the-art solutions and their *** this context,this work aims to shed light on these challenges and assess the development and applicability of sampling-based ***,we aim to provide an in-depth analysis of the design and evaluation of ten of the most popular planners across various *** findings highlight the strides made in sampling-based methods while underscoring persistent *** work offers an overview of the important ongoing research in robotic motion planning.

关键词： robotics Motion planning Sampling-based algorithms

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Snapshot: Towards Application-Centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments

Snapshot: Towards Application-Centered Models for Pedestrian...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2025

作者： Uhlemann, Nico Zhou, Yipeng Mohr, Tobias Simeon Lienkamp, Markus Technical University of Munich Germany School of Engineering & Design Institute of Automotive Technology Munich Institute of Robotics and Machine Intelligence Germany

ISBN: (纸本)9798331536626

关键词： Feedforward neural networks

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Scenario Understanding of Traffic Scenes Through Large Visual Language Models

arXiv

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arXiv 2025年

作者： Rivera, Esteban Lübberstedt, Jannik Uhlemann, Nico Lienkamp, Markus Technical University of Munich Germany School of Engineering & Design Institute of Automotive Technology Munich Institute of Robotics and Machine Intelligence Germany

Deep learning models for autonomous driving, encompassing perception, planning, and control, depend on vast datasets to achieve their high performance. However, their generalization often suffers due to domain-specific data distributions, making an effective scene-based categorization of samples necessary to improve their reliability across diverse domains. Manual captioning, though valuable, is both labor-intensive and time-consuming, creating a bottleneck in the data annotation process. Large Visual Language Models (LVLMs) present a compelling solution by automating image analysis and categorization through contextual queries, often without requiring retraining for new categories. In this study, we evaluate the capabilities of LVLMs, including GPT-4 and LLaVA, to understand and classify urban traffic scenes on both an in-house dataset and the BDD100K. We propose a scalable captioning pipeline that integrates state-of-the-art models, enabling a flexible deployment on new datasets. Our analysis, combining quantitative metrics with qualitative insights, demonstrates the effectiveness of LVLMs to understand urban traffic scenarios and highlights their potential as an efficient tool for data-driven advancements in autonomous driving. © 2025, CC BY.

关键词： Visual languages

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Scenario Understanding of Traffic Scenes Through Large Visual Language Models

Scenario Understanding of Traffic Scenes Through Large Visua...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2025

作者： Rivera, Esteban Lubberstedt, Jannik Uhlemann, Nico Lienkamp, Markus Technical University of Munich Germany School of Engineering & Design Institute of Automotive Technology Munich Institute of Robotics and Machine Intelligence Germany

ISBN: (纸本)9798331536626

关键词： Deep learning

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InstKD: Towards Lightweight 3D Object Detection With Instance-Aware Knowledge Distillation

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Zhang, Haonan Liu, Longjun Huang, Yuqi Lei, Xinyu Tong, Lei Wen, Bihan National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Deep neural network (DNN) is extensively explored for LiDAR-based 3D object detection, a crucial perception task in the field of autonomous driving. However, the presence of redundant parameters and complex computations pose challenges for the practical deployment of DNNs. Despite knowledge distillation (KD) is an effective approach for accelerating models, extremely small number of efforts explore its potential on LiDARbased 3D detectors. Besides, existing studies neglect to elaborately investigate 3D voxel-wise features for compression. To this end, we propose instance-aware knowledge distillation (InstKD) for 3D detector compression. The proposed method conducts KD by fully excavating two types of knowledge related to 3D voxelwise features. Firstly, the 3D voxel-wise feature of teacher is transferred to teach the student. In order to prioritize the knowledge with strong guiding capacity, we introduce expanded bounding box (E-Bbox) to distinguish and balance the foreground and background regions. Besides, we generate contribution map (CM) by calculating the gap between the classification response of teacher and student models to further dynamically balance individual instance for distillation. Secondly, we also align the relation-based knowledge of 3D voxel-wise features between the distillation pairs. To avoid incalculable relation on a massive number of 3D voxel-wise features, we distill the relation among instances selected by E-Bboxes, where the intra-relation of homogeneous instances and inter-relation of heterogeneous instances are transferred in a dual-pathway manner. In the experiments, we compress different models on benchmarks with varying scales. The results demonstrate that our method achieves the lightweight 3D detector with slight performance drop. For example, on KITTI dataset, our 2× compressed SECOND (75.5% parameters and 74.5% FLOPs reduction) achieves 66.83% mAP, surpassing its teacher model. The key code is available at https://***/zhnxj

关键词： Object detection

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Diffusion-based learning of contact plans for agile locomotion 23

Diffusion-based learning of contact plans for agile locomoti...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Dhédin, Victor Ravi, Adithya Kumar Chinnakkonda Jordana, Armand Zhu, Huaijiang Meduri, Avadesh Righetti, Ludovic Schölkopf, Bernhard Khadiv, Majid Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany New York University Tandon School of Engineering United States Max-Planck Institute for Intelligent Systems Tübingen Germany

ISBN: (纸本)9798350373578

Legged robots have become capable of performing highly dynamic maneuvers in the past few years. However, agile locomotion in highly constrained environments such as stepping stones is still a challenge. In this paper, we propose a combination of model-based control, search, and learning to design efficient control policies for agile locomotion on stepping stones. In our framework, we use nonlinear model predictive control (NMPC) to generate whole-body motions for a given contact plan. To efficiently search for an optimal contact plan, we propose to use Monte Carlo tree search (MCTS). While the combination of MCTS and NMPC can quickly find a feasible plan for a given environment (a few seconds), it is not yet suitable to be used as a reactive policy. Hence, we generate a dataset for optimal goal-conditioned policy for a given scene and learn it through supervised learning. In particular, we leverage the power of diffusion models in handling multi-modality in the dataset. We test our proposed framework on a scenario where our quadruped robot Solo12 successfully jumps to different goals in a highly constrained environment (video). © 2024 IEEE.

关键词： Multipurpose robots

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Biomechanical Test Setup for the Investigation of Forehead Suture Techniques

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Current Directions in Biomedical engineering 2023年第1期9卷 532-535页

作者： Micheler, Carina M. Lang, Jan J. Wilhelm, Nikolas J. Schaack, Victor G. Eisenhart-Rothe, Rüdiger Von Burgkart, Rainer H. H. Bäumlisberger, Anja Wachtel, Nikolaus Department of Orthopaedics and Sports Orthopaedics University Hospital Rechts der Isar School of Medicine Technical University of Munich Munich Germany Institute for Machine Tools and Industrial Management School of Engineering and Design Technical University of Munich Garching near Munich Germany Non-destructive Testing School of Engineering and Design Technical University of Munich Munich Germany Munich Institute of Robotics and Machine Intelligence Department of Electrical and Computer Engineering Technical University of Munich Munich Germany Division of Hand Plastic and Aesthetic Surgery University Hospital LMU Munich Munich Germany

Wound healing can be delayed if the biomechanical stability of the wound closure is inadequate. Therefore, it is necessary to investigate different suturing techniques for their biomechanical stability. In this study, suturing techniques suitable for the forehead area were investigated. For this application, a special test setup was developed to simulate the curvature of the forehead and the corresponding physiological configuration. The average forehead curvature is 62.24 ± 4.11 mm in radius. To simulate this curvature, the skin specimens are subjected to tensile stress over the spherical surface using a standard uniaxial testing machine. For the evaluation, an automated evaluation tool for MATLAB was also developed. Three different suturing techniques (Straight, Lazy-S, Zigzag) were investigated and tested for their biomechanical stability. Of the three suturing techniques, the Zigzag suture proved to be the most stable with the highest stiffness of 44.23 ± 8.18 % and the highest final failure of 32.60 ± 4.95 % (relative to the control sample without incision). The study has shown that the test setup can be used to investigate different forehead suture techniques. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： MATLAB

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