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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zhang, Miao Abdulatif, Sherif Loesch, Benedikt Altmann, Marco Schwarz, Marius Yang, Bin Robert Bosch GmbH Corporate Research 71272 Germany University of Stuttgart Institute of Signal Processing and System Theory 70569 Germany

ISBN: (纸本)9798331505929

The rapid evolution of deep learning and its integration with autonomous driving systems have led to substantial advancements in 3D perception using multimodal sensors. Notably, radar sensors show greater robustness compared to cameras and lidar under adverse weather and varying illumination conditions. This study delves into the often-overlooked yet crucial issue of domain shift in 4D radar-based object detection, examining how varying environmental conditions, such as different weather patterns and road types, impact 3D object detection performance. Our findings highlight distinct domain shifts across various weather scenarios, revealing unique dataset sensitivities that underscore the critical role of radar point cloud generation. Additionally, we demonstrate that transitioning between different road types, especially from highways to urban settings, introduces notable domain shifts, emphasizing the necessity for diverse data collection across varied road environments. To the best of our knowledge, this is the first comprehensive analysis of domain shift effects on 4D radar-based object detection. We believe this empirical study contributes to understanding the complex nature of domain shifts in radar data and suggests paths forward for data collection strategy in the face of environmental variability. © 2024 IEEE.

关键词： Network security

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Indoor Positioning Based on Active Radar Sensing and Passive Reflectors: Reflector Placement Optimization 13

Indoor Positioning Based on Active Radar Sensing and Passive...

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13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Hinderer, Sven Schlachter, Pascal Yuy, Zhibin Wuy, Xiaofeng Yang, Bin Institute of Signal Processing and System Theory University of Stuttgart Stuttgart Germany Huawei Technologies Duesseldorf GmbH Munich Research Center Munich Germany

ISBN: (纸本)9798350320114

We extend our work on a novel indoor positioning system (IPS) for autonomous mobile robots (AMRs) based on radar sensing of local, passive radar reflectors. Through the combination of simple reflectors and a single-channel frequency modulated continuous wave (FMCW) radar, high positioning accuracy at low system cost can be achieved. Further, a multi-objective (MO) particle swarm optimization (PSO) algorithm is presented that optimizes the 2D placement of radar reflectors in complex room settings. © 2023 IEEE.

关键词： autonomous mobile robot indoor positioning system particle swarm optimization passive reflector radar

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Introducing Intermediate Domains for Effective Self-Training during Test-Time

Introducing Intermediate Domains for Effective Self-Training...

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International Joint Conference on Neural Networks (IJCNN)

作者： Robert A. Marsden Mario Döbler Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Experiencing domain shifts during test-time is nearly inevitable in practice and likely results in a severe performance degradation. To overcome this issue, test-time adaptation continues to update the initial source model after deployment. A promising direction are methods based on self-training which have been shown to be well suited for gradual domain adaptation, since reliable pseudo-labels can be provided. In this work, we address two problems that exist when applying self-training in the setting of test-time adaptation. First, adapting a model to long test sequences that contain multiple domains can lead to error accumulation. Second, naturally, not all shifts are gradual in practice. To tackle these challenges, we introduce GTTA. By creating artificial intermediate domains that divide the current domain shift into a more gradual one, effective self-training through high quality pseudo-labels can be performed. To create the intermediate domains, we propose two independent variations: mixup and light-weight style transfer. We demonstrate the effectiveness of our approach on the continual and gradual corruption benchmarks, as well as ImageNet-R. To further investigate gradual shifts in the context of urban scene segmentation, we publish a benchmark: CarlaTTA. It enables the exploration of several non-stationary domain shifts. 1

关键词： Degradation Adaptation models Image segmentation Neural networks Benchmark testing Reliability

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COMET: Contrastive Mean Teacher for Online Source-Free Universal Domain Adaptation

COMET: Contrastive Mean Teacher for Online Source-Free Unive...

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International Joint Conference on Neural Networks (IJCNN)

作者： Pascal Schlachter Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In real-world applications, there is often a domain shift (distribution change) from training to test data. This observation recently resulted in the development of test-time adaptation (TTA). It aims to adapt a pre-trained source model to the test data without requiring access to the source data. Thereby, most existing works are so far limited to the closed-set assumption, i.e. there is no category shift (class change) between source and target domain. We argue that in a realistic open-world setting a category shift can appear in addition to a domain shift. This means, individual source classes may not appear in the target domain anymore, samples of new unknown classes may be part of the target domain or even both at the same time. Moreover, in many real-world scenarios the test data is not accessible all at once in form of a dataset but arrives sequentially as a stream of batches which require an immediate prediction. Hence, TTA must be applied in an online manner. To the best of our knowledge, the combination of these aspects, i.e. online source-free universal domain adaptation (online SF-UniDA), has not been studied yet despite its practical relevance. In this paper, we are the first ones to tackle this challenging task. We introduce a Contrastive Mean Teacher (COMET) tailored to this novel scenario. It applies a contrastive loss to rebuild a feature space where the samples of known classes build distinct clusters and the samples of new classes separate well from them. It is complemented by an entropy loss which ensures that the classifier output has a small entropy for samples of known classes and a large entropy for samples of new classes to be easily detected and rejected as unknown. To provide the losses with reliable pseudo labels, they are embedded into a mean teacher (MT) framework. We evaluate our method across two datasets and all category shifts to set an initial benchmark for online SF-UniDA. Thereby, COMET yields state-of-the-art performance and prov

关键词： Comets Training Adaptation models Codes Neural networks Benchmark testing Entropy

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COMET: Contrastive Mean Teacher for Online Source-Free Universal Domain Adaptation

arXiv

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arXiv 2024年

作者： Schlachter, Pascal Yang, Bin Institute of Signal Processing and System Theory University of Stuttgart Germany

关键词： Statistical tests

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Data-Driven Control of Distributed Event-Triggered Network systems

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 351-364页

作者： Xin Wang Jian Sun Gang Wang Frank Allgower Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the Institute for Systems Theory and Automatic Control University of StuttgartStuttgart 70550Germany the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(*** systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional *** the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is ***,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input ***,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

关键词： Data-driven control distributed event-triggered network system(ETS) linear matrix inequalitie(LMI) looped-functional stability

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Path-Complete Barrier Functions for Safety of Switched Linear systems 63

Path-Complete Barrier Functions for Safety of Switched Linea...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Anand, Mahathi Jungers, Raphael Zamani, Majid Allgower, Frank Institute for Systems Theory and Automatic Control University of Stuttgart Germany Université Catholique de Louvain Department of Mathematical Engineering Belgium University of Colorado Boulder United States

ISBN: (纸本)9798350316339

In this paper, we address the safety verification problem of switched linear dynamical systems under arbitrary switching via barrier functions. Our approach is based on a notion of path-complete barrier functions, which utilizes a collection of barrier functions associated with a directed labeled graph that can encode all the possible switching sequences. We show that path-complete barrier functions effectively generalize notions of common and multiple barrier functions studied in existing literature, and can potentially provide less conservative conditions for safety verification. We demonstrate that, for switched linear systems, the inequalities imposed via path-complete barrier functions can be easily encoded into simple linear matrix inequalities under some assumptions on the regions of interest and appropriately chosen templates for the barrier functions. We also study the relationship between path-complete barrier functions and common barrier functions, and show that for any path-complete graph with an admissible path-complete barrier function, one can derive a suitable (possibly non-smooth) common barrier function by utilizing the path-complete barrier function. Finally, we utilize several examples to illustrate the effectiveness of our approach, and briefly discuss the challenges that lay foundations for future research. © 2024 IEEE.

关键词： Directed graphs

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A SEMI-PAIRED APPROACH FOR LABEL-TO-IMAGE TRANSLATION

arXiv

引用

arXiv 2023年

作者： Eskandar, George Zhang, Shuai Abdelsamad, Mohamed Youssef, Mark Guo, Diandian Yang, Bin University of Stuttgart Institute of Signal Processing and System Theory Stuttgart Germany

Data efficiency, or the ability to generalize from a few labeled data, remains a major challenge in deep learning. Semi-supervised learning has thrived in traditional recognition tasks alleviating the need for large amounts of labeled data, yet it remains understudied in image-to-image translation (I2I) tasks. In this work, we introduce the first semi-supervised (semi-paired) framework for label-to-image translation, a challenging subtask of I2I which generates photorealistic images from semantic label maps. In the semi-paired setting, the model has access to a small set of paired data and a larger set of unpaired images and labels. Instead of using geometrical transformations as a pretext task like previous works, we leverage an input reconstruction task by exploiting the conditional discriminator on the paired data as a reverse generator. We propose a training algorithm for this shared network, and we present a rare classes sampling algorithm to focus on under-represented classes. Experiments on 3 standard benchmarks show that the proposed model outperforms state-of-the-art unsupervised and semi-supervised approaches, as well as some fully supervised approaches while using a much smaller number of paired samples. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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WAVELET-BASED UNSUPERVISED LABEL-TO-IMAGE TRANSLATION

arXiv

引用

arXiv 2023年

作者： Eskandar, George Abdelsamad, Mohamed Armanious, Karim Zhang, Shuai Yang, Bin University of Stuttgart Institute of Signal Processing and System Theory Stuttgart Germany

Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a semantic layout is used to generate a photorealistic image. State-of-the-art conditional Generative Adversarial Networks (GANs) need a huge amount of paired data to accomplish this task while generic unpaired image-to-image translation frameworks underperform in comparison, because they color-code semantic layouts and learn correspondences in appearance instead of semantic content. Starting from the assumption that a high quality generated image should be segmented back to its semantic layout, we propose a new Unsupervised paradigm for SIS (USIS) that makes use of a self-supervised segmentation loss and whole image wavelet based discrimination. Furthermore, in order to match the high-frequency distribution of real images, a novel generator architecture in the wavelet domain is proposed. We test our methodology on 3 challenging datasets and demonstrate its ability to bridge the performance gap between paired and unpaired models. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Indoor Positioning based on Active Radar Sensing and Passive Reflectors: Concepts & Initial Results 13

Indoor Positioning based on Active Radar Sensing and Passive...

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13th International Conference on Indoor Positioning and Indoor Navigation - Work-in-Progress Papers, IPIN-WiP 2023

作者： Schlachter, Pascal Yu, Zhibin Iqbal, Naveed Wu, Xiaofeng Hinderer, Sven Yang, Bin Institute of Signal Processing and System Theory University of Stuttgart Pfaffenwaldring 47 Stuttgart70569 Germany Munich Research Center Huawei Technologies Duesseldorf GmbH Riesstraße 25 Munich80992 Germany

To navigate reliably in indoor environments, an industrial autonomous vehicle must know its position. However, current indoor vehicle positioning technologies either lack accuracy, usability or are too expensive. Thus, we propose a novel concept called local reference point assisted active radar positioning, which is able to overcome these drawbacks. It is based on distributing passive retroreflectors in the indoor environment such that each position of the vehicle can be identified by a unique reflection characteristic regarding the reflectors. To observe these characteristics, the autonomous vehicle is equipped with an active radar system. On one hand, this paper presents the basic idea and concept of our new approach towards indoor vehicle positioning and especially focuses on the crucial placement of the reflectors. On the other hand, it also provides a proof of concept by conducting a full system simulation including the placement of the local reference points, the radar-based distance estimation and the comparison of two different positioning methods. It successfully demonstrates the feasibility of our proposed approach. © 2023 Copyright for this paper by its authors.

关键词： Autonomous vehicles

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