CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust Modulus

• URL: http://arxiv.org/abs/2502.08169v1
• Date: Wed, 12 Feb 2025 07:23:26 GMT
• Title: CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust Modulus
• Authors: Yunjiang Xu, Lingzhi Li, Jin Wang, Benyuan Yang, Zhiwen Wu, Xinhong Chen, Jianping Wang,
• Abstract summary: Collaborative perception, fusing information from multiple agents, can extend perception range so as to improve performance. temporal asynchrony in real-world environments, caused by communication delays, clock misalignment, or sampling configuration differences, can lead to information mismatches. We propose CoDynTrust, an uncertainty-encoded asynchronous fusion perception framework that is robust to the information mismatches caused by temporal asynchrony.
• Score: 9.552300496606644
• License:
• Abstract: Collaborative perception, fusing information from multiple agents, can extend perception range so as to improve perception performance. However, temporal asynchrony in real-world environments, caused by communication delays, clock misalignment, or sampling configuration differences, can lead to information mismatches. If this is not well handled, then the collaborative performance is patchy, and what's worse safety accidents may occur. To tackle this challenge, we propose CoDynTrust, an uncertainty-encoded asynchronous fusion perception framework that is robust to the information mismatches caused by temporal asynchrony. CoDynTrust generates dynamic feature trust modulus (DFTM) for each region of interest by modeling aleatoric and epistemic uncertainty as well as selectively suppressing or retaining single-vehicle features, thereby mitigating information mismatches. We then design a multi-scale fusion module to handle multi-scale feature maps processed by DFTM. Compared to existing works that also consider asynchronous collaborative perception, CoDynTrust combats various low-quality information in temporally asynchronous scenarios and allows uncertainty to be propagated to downstream tasks such as planning and control. Experimental results demonstrate that CoDynTrust significantly reduces performance degradation caused by temporal asynchrony across multiple datasets, achieving state-of-the-art detection performance even with temporal asynchrony. The code is available at https://github.com/CrazyShout/CoDynTrust.

Related papers

• MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Impairment Detection via Synchronous and Attentive Fusion [4.526574526136158]
  A Multi-scale Hypergraph Network for MCI Detection via Synchronous and Attentive Fusion is presented. Our approach employs the Phase-Locking Value (PLV) to calculate the phase synchronization relationship in the spectrum domain of regions of interest. We structure the PLV coefficients dynamically adjust strategy, and the dynamic hypergraph is modelled based on a comprehensive temporal-spectrum fusion matrix.
  arXiv  Detail & Related papers  (2024-12-11T02:59:57Z)
• StreamLTS: Query-based Temporal-Spatial LiDAR Fusion for Cooperative Object Detection [0.552480439325792]
  We propose Time-Aligned COoperative Object Detection (TA-COOD), for which we adapt widely used dataset OPV2V and DairV2X. Experiment results confirm the superior efficiency of our fully sparse framework compared to the state-of-the-art dense models.
  arXiv  Detail & Related papers  (2024-07-04T10:56:10Z)
• Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation [96.78845113346809]
  Retrieval-augmented language models (RALMs) have shown strong performance and wide applicability in knowledge-intensive tasks. This paper proposes SynCheck, a lightweight monitor that leverages fine-grained decoding dynamics to detect unfaithful sentences. We also introduce FOD, a faithfulness-oriented decoding algorithm guided by beam search for long-form retrieval-augmented generation.
  arXiv  Detail & Related papers  (2024-06-19T16:42:57Z)
• Uncertainty-Aware Deep Attention Recurrent Neural Network for Heterogeneous Time Series Imputation [0.25112747242081457]
  Missingness is ubiquitous in multivariate time series and poses an obstacle to reliable downstream analysis. We propose DEep Attention Recurrent Imputation (Imputation), which jointly estimates missing values and their associated uncertainty. Experiments show that I surpasses the SOTA in diverse imputation tasks using real-world datasets.
  arXiv  Detail & Related papers  (2024-01-04T13:21:11Z)
• TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support [59.41529066449414]
  We propose TrustGuard, a GNN-based accurate trust evaluation model that supports trust dynamicity. TrustGuard is designed with a layered architecture that contains a snapshot input layer, a spatial aggregation layer, a temporal aggregation layer, and a prediction layer. Experiments show that TrustGuard outperforms state-of-the-art GNN-based trust evaluation models with respect to trust prediction across single-timeslot and multi-timeslot.
  arXiv  Detail & Related papers  (2023-06-23T07:39:12Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)
• Edge Continual Learning for Dynamic Digital Twins over Wireless Networks [68.65520952712914]
  Digital twins (DTs) constitute a critical link between the real-world and the metaverse. In this paper, a novel edge continual learning framework is proposed to accurately model the evolving affinity between a physical twin and its corresponding cyber twin. The proposed framework achieves a simultaneously accurate and synchronous CT model that is robust to catastrophic forgetting.
  arXiv  Detail & Related papers  (2022-04-10T23:25:37Z)
• Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting [48.8617204809538]
  We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To learn complex correlations across heterogeneous sequences, a tailored encoder is devised to combine the advances in deep point processes models and variational recurrent neural networks. Our model can be trained effectively using variational inference and generates predictions with Monte-Carlo simulation.
  arXiv  Detail & Related papers  (2021-01-31T11:00:55Z)
• Slow and Stale Gradients Can Win the Race [39.750046808758526]
  Distributed Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in runtime as it waits for the slowest workers (stragglers) Asynchronous methods can alleviate stragglers, but cause gradient staleness that can adversely affect the convergence error. We present a novel theoretical characterization of the speedup offered by asynchronous methods by analyzing the trade-off between the error in the trained model and the actual training runtime.
  arXiv  Detail & Related papers  (2020-03-23T23:27:50Z)
• Event-based Asynchronous Sparse Convolutional Networks [54.094244806123235]
  Event cameras are bio-inspired sensors that respond to per-pixel brightness changes in the form of asynchronous and sparse "events" We present a general framework for converting models trained on synchronous image-like event representations into asynchronous models with identical output. We show both theoretically and experimentally that this drastically reduces the computational complexity and latency of high-capacity, synchronous neural networks.
  arXiv  Detail & Related papers  (2020-03-20T08:39:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.