AgentAlign: Misalignment-Adapted Multi-Agent Perception for Resilient Inter-Agent Sensor Correlations

• URL: http://arxiv.org/abs/2412.06142v1
• Date: Mon, 09 Dec 2024 01:51:18 GMT
• Title: AgentAlign: Misalignment-Adapted Multi-Agent Perception for Resilient Inter-Agent Sensor Correlations
• Authors: Zonglin Meng, Yun Zhang, Zhaoliang Zheng, Zhihao Zhao, Jiaqi Ma,
• Abstract summary: Existing research overlooks the fragile multi-sensor correlations in multi-agent settings. AgentAlign is a real-world heterogeneous agent cross-modality feature alignment framework. We present a novel V2XSet-noise dataset that simulates realistic sensor imperfections under diverse environmental conditions.
• Score: 8.916036880001734
• License:
• Abstract: Cooperative perception has attracted wide attention given its capability to leverage shared information across connected automated vehicles (CAVs) and smart infrastructures to address sensing occlusion and range limitation issues. However, existing research overlooks the fragile multi-sensor correlations in multi-agent settings, as the heterogeneous agent sensor measurements are highly susceptible to environmental factors, leading to weakened inter-agent sensor interactions. The varying operational conditions and other real-world factors inevitably introduce multifactorial noise and consequentially lead to multi-sensor misalignment, making the deployment of multi-agent multi-modality perception particularly challenging in the real world. In this paper, we propose AgentAlign, a real-world heterogeneous agent cross-modality feature alignment framework, to effectively address these multi-modality misalignment issues. Our method introduces a cross-modality feature alignment space (CFAS) and heterogeneous agent feature alignment (HAFA) mechanism to harmonize multi-modality features across various agents dynamically. Additionally, we present a novel V2XSet-noise dataset that simulates realistic sensor imperfections under diverse environmental conditions, facilitating a systematic evaluation of our approach's robustness. Extensive experiments on the V2X-Real and V2XSet-Noise benchmarks demonstrate that our framework achieves state-of-the-art performance, underscoring its potential for real-world applications in cooperative autonomous driving. The controllable V2XSet-Noise dataset and generation pipeline will be released in the future.

Related papers

• Causal Mean Field Multi-Agent Reinforcement Learning [10.767740092703777]
  A framework named mean-field reinforcement learning (MFRL) could alleviate the scalability problem by employing the Mean Field Theory. This framework lacks the ability to identify essential interactions under nonstationary environments. We propose an algorithm called causal mean-field Q-learning (CMFQ) to address the scalability problem.
  arXiv  Detail & Related papers  (2025-02-20T02:15:58Z)
• Caution for the Environment: Multimodal Agents are Susceptible to Environmental Distractions [68.92637077909693]
  This paper investigates the faithfulness of multimodal large language model (MLLM) agents in the graphical user interface (GUI) environment. A general setting is proposed where both the user and the agent are benign, and the environment, while not malicious, contains unrelated content. Experimental results reveal that even the most powerful models, whether generalist agents or specialist GUI agents, are susceptible to distractions.
  arXiv  Detail & Related papers  (2024-08-05T15:16:22Z)
• Very Large-Scale Multi-Agent Simulation in AgentScope [112.98986800070581]
  We develop new features and components for AgentScope, a user-friendly multi-agent platform. We propose an actor-based distributed mechanism towards great scalability and high efficiency. We also provide a web-based interface for conveniently monitoring and managing a large number of agents.
  arXiv  Detail & Related papers  (2024-07-25T05:50:46Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• INTAGS: Interactive Agent-Guided Simulation [4.04638613278729]
  In many applications involving multi-agent system (MAS), it is imperative to test an experimental (Exp) autonomous agent in a high-fidelity simulator prior to its deployment to production. We propose a metric to distinguish between real and synthetic multi-agent systems, which is evaluated through the live interaction between the Exp and BG agents. We show that using INTAGS to calibrate the simulator can generate more realistic market data compared to the state-of-the-art conditional Wasserstein Generative Adversarial Network approach.
  arXiv  Detail & Related papers  (2023-09-04T19:56:18Z)
• S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality [41.25312194294171]
  We propose the first-to-Reality transfer learning framework for multi-agent cooperative perception using a novel Vision Transformer, named as S2R-ViT. Our experiments on the public multi-agent cooperative perception datasets OPV2V and V2V4Real demonstrate that the proposed S2R-ViT can effectively bridge the gap from simulation to reality.
  arXiv  Detail & Related papers  (2023-07-16T03:54:10Z)
• On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring [105.13668993076801]
  A central problem in the theory of multi-agent reinforcement learning (MARL) is to understand what structural conditions and algorithmic principles lead to sample-efficient learning guarantees. We study this question in a general framework for interactive decision making with multiple agents. We show that characterizing the statistical complexity for multi-agent decision making is equivalent to characterizing the statistical complexity of single-agent decision making.
  arXiv  Detail & Related papers  (2023-05-01T06:46:22Z)
• Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences [118.91584633024907]
  A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
  arXiv  Detail & Related papers  (2023-04-29T00:39:50Z)
• Bridging the Domain Gap for Multi-Agent Perception [19.724227909352976]
  We propose the first lightweight framework to bridge domain gaps for multi-agent perception. Our framework consists of a learnable feature resizer to align features in multiple dimensions and a sparse cross-domain transformer for domain adaption. Our method can effectively bridge the gap for features from different domains and outperform other baseline methods significantly by at least 8% for point-cloud-based 3D object detection.
  arXiv  Detail & Related papers  (2022-10-16T05:29:21Z)
• Enhancing Multi-Robot Perception via Learned Data Association [37.866254392010454]
  We address the multi-robot collaborative perception problem, specifically in the context of multi-view infilling for distributed semantic segmentation. We propose the Multi-Agent Infilling Network: an neural architecture that can be deployed to each agent in a robotic swarm. Specifically, each robot is in charge of locally encoding and decoding visual information, and an neural mechanism allows for an uncertainty-aware and context-based exchange of intermediate features.
  arXiv  Detail & Related papers  (2021-07-01T22:45:26Z)

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.