UECP: Uncertainty-Enhanced Collaborative Perception

• URL: http://arxiv.org/abs/2606.23046v1
• Date: Mon, 22 Jun 2026 08:55:10 GMT
• Title: UECP: Uncertainty-Enhanced Collaborative Perception
• Abstract summary: Collaborative perception serves as a pivotal solution to enhance the perception capability of individual agents in autonomous driving.<n>Existing methods typically rely on a confidence map, which is correlated with the detection results and thus fails to provide unbiased physical evidence.<n>This paper first proposes an uncertainty map, a physically grounded and unambiguous metric for evaluating perception quality.<n>We develop the Uncertainty-Enhanced Collaborative Perception framework, centered on the Uncertainty-Aware Pyramid Fusion module.
• Score: 15.846318270344641
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaborative perception serves as a pivotal solution to enhance the perception capability of individual agents in autonomous driving, where a core challenge lies in seeking reliable evidence to quantify and weight the contribution of each participating agent. Existing methods typically rely on a confidence map, which is co-trained with the detection head, but it is inherently correlated with the detection results and thus fails to provide unbiased physical evidence. Furthermore, how to deeply integrate evidence into the cooperative fusion process remains an open question. To address these issues, this paper first proposes an uncertainty map, a physically grounded and unambiguous metric for evaluating perception quality. This map is directly supervised by real-time sensor signals, i.e., LiDAR point density, ensuring decoupling from detection noise and thereby providing physical scenario-aware evidence for weighting agent contribution. Based on this map, we develop the Uncertainty-Enhanced Collaborative Perception (UECP) framework, centered on the Uncertainty-Aware Pyramid Fusion (UAPF) module. UAPF uses a coarse-to-fine strategy, with two key components: Uncertainty-Weighted Downsampling (UWD) for high-fidelity feature preservation, and Uncertainty-Guided Residual Fusion (UGRF) to reinforce ego features, suppressing noise and ensuring robust fusion. Extensive experiments on real-world datasets show UECP outperforms state-of-the-art methods in effectiveness and robustness by embedding the uncertainty map into fusion. Code will be publicly available.

Related papers

• Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations [55.76952683833966]
  This paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC) to reduce confidence set size when data may be corrupted.<n>Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size.
  arXiv  Detail & Related papers  (2026-07-29T05:16:59Z)
• Towards Trustworthy Depression Estimation via Disentangled Evidential Learning [50.22167852149165]
  We propose EviDep, an evidential learning framework that jointly quantifies depression severity.<n>EviDep enforces strict information integrity to guarantee robust evidence synthesis.<n>It achieves state-of-the-art predictive accuracy and superior uncertainty calibration, delivering a robust fail-safe mechanism for trustworthy clinical screening.
  arXiv  Detail & Related papers  (2026-04-17T13:27:11Z)
• From Decoupled to Coupled: Robustness Verification for Learning-based Keypoint Detection with Joint Specifications [11.371752627865929]
  Keypoint detection underpins many vision tasks, including pose estimation, viewpoint recovery, and 3D reconstruction.<n>Despite its importance, formal robustness verification for keypoint detectors is largely unexplored due to high-dimensional inputs and continuous coordinate outputs.<n>We propose the first coupled robustness verification framework for heatmap-based keypoint detectors.
  arXiv  Detail & Related papers  (2026-03-05T19:01:53Z)
• Agentic Uncertainty Quantification [76.94013626702183]
  We propose a unified Dual-Process Agentic UQ (AUQ) framework that transforms verbalized uncertainty into active, bi-directional control signals.<n>Our architecture comprises two complementary mechanisms: System 1 (Uncertainty-Aware Memory, UAM), which implicitly propagates verbalized confidence and semantic explanations to prevent blind decision-making; and System 2 (Uncertainty-Aware Reflection, UAR), which utilizes these explanations as rational cues to trigger targeted inference-time resolution only when necessary.
  arXiv  Detail & Related papers  (2026-01-22T07:16:26Z)
• Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency [78.91846841708586]
  We show that even facts answered with perfect self-consistency can rapidly collapse under mild contextual interference.<n>We propose Neighbor-Consistency Belief (NCB), a structural measure of belief that evaluates response coherence across a conceptual neighborhood.<n>We also present Structure-Aware Training (SAT), which optimize context-invariant belief structure and reduces long-tail knowledge brittleness by approximately 30%.
  arXiv  Detail & Related papers  (2026-01-09T16:23:21Z)
• ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation [91.20492150248106]
  We investigate the internal mechanisms behind unfaithful generation and identify a subset of mid-to-deep feed-forward networks (FFNs) that are disproportionately activated in such cases.<n>We propose Parametric Knowledge Muting through FFN Suppression (ParamMute), a framework that improves contextual faithfulness by suppressing the activation of unfaithfulness-associated FFNs.<n> Experimental results show that ParamMute significantly enhances faithfulness across both CoFaithfulQA and the established ConFiQA benchmark, achieving substantial reductions in reliance on parametric memory.
  arXiv  Detail & Related papers  (2025-02-21T15:50:41Z)
• A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making [16.67762257337951]
  A privacy-preserving distributed credible evidence fusion method with three-level consensus (PCEF) is proposed in this paper.<n>The simulations show that PCEF is close to CCEF both in credibility and fusion results and obtains higher decision accuracy with less time-comsuming than existing methods.
  arXiv  Detail & Related papers  (2024-12-03T03:36:42Z)
• Credible fusion of evidence in distributed system subject to cyberattacks [2.5539863252714636]
  This paper proposes an algorithm for credible evidence fusion against cyberattacks.<n>We focus on three requirements for evidence fusion, i.e., preserving evidence's privacy, identifying attackers and excluding their evidence.<n>The states of normal nodes are shown to converge to their WAVCCME, while the attacker's evidence is excluded from the fusion.
  arXiv  Detail & Related papers  (2024-11-29T13:46:04Z)
• What If the Input is Expanded in OOD Detection? [77.37433624869857]
  Out-of-distribution (OOD) detection aims to identify OOD inputs from unknown classes. Various scoring functions are proposed to distinguish it from in-distribution (ID) data. We introduce a novel perspective, i.e., employing different common corruptions on the input space.
  arXiv  Detail & Related papers  (2024-10-24T06:47:28Z)
• Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach [0.0]
  False alerts due to/ compromised IDS in ICS networks can lead to severe economic and operational damage. This work presents an approach for reducing false alerts in CPS power systems by dealing with uncertainty without prior distribution of alerts.
  arXiv  Detail & Related papers  (2021-11-20T00:05:39Z)

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.