用于联网自动驾驶中弹性协作决策的即插即用重加权方法
Plug-and-Play Reweighting for Resilient Collaborative Decision-Making in Connected Autonomous Driving
浏览论文内容
中文总结 AI 辅助
针对联网自动驾驶中协作决策受感知噪声和对抗攻击影响的问题，提出弹性协作决策（RCDM）框架，含基于注意力的编解码器，设计即插即用重加权模块，经高保真模拟评估，性能优于现有方法，实现最优弹性能。
中文摘要 AI 辅助
协作决策是多机器人系统（如联网自动驾驶车辆）的一项基本能力。然而，协作方中的感知噪声和对抗攻击会严重影响决策可靠性。现有方法通常依赖针对特定攻击的防御进行再训练或基于限制性扰动假设来提高弹性，这限制了其实用性。本文提出了一种新颖的弹性协作决策（RCDM）框架，它由一个基于注意力的编码器和一个基于注意力的解码器组成。为提高对受损观测的弹性，设计了一种即插即用重加权模块，通过分析邻域点相对于局部结构的一致性，对偏离局部中位数较大的点赋予较小权重，从而降低受损输入的影响。该模块可无缝集成到基于注意力的协作决策中，无需额外训练。在高保真模拟中评估了该方法，考虑了感知噪声和五种攻击类型。实验结果表明，该方法始终比现有方法高出26%，并实现了当前最优的弹性能。
英文摘要
Collaborative decision-making is a fundamental capability in multi-robot systems, such as connected autonomous vehicles. However, perceptual noise and adversarial attacks in collaborators can severely affect decision reliability. Overall, existing methods typically rely on retraining with attack-specific defenses or on restrictive perturbation assumptions to improve resilience, which limits their practicality. In this paper, we propose a novel Resilient Collaborative Decision-Making (RCDM) framework that consists of an attention-based encoder for extracting individual robot perceptual embeddings and an attention-based decoder for fusing collaborator perceptions and making decisions. To improve resilience to corrupted observations, we design a novel plug-and-play reweighting module that down-weights the influence of corrupted inputs by analyzing the consistency of neighborhood points relative to the local structure and assigning smaller weights to points that deviate strongly from the local median. This module can be seamlessly integrated into attention-based collaborative decision-making without requiring additional training. We evaluate our method in high-fidelity simulations, considering perceptual noise and five types of attacks across diverse accident-prone scenarios. Experimental results demonstrate that our approach consistently outperforms existing methods by up to 26% and achieves state-of-the-art resilient performance.
发表机构
- North Carolina State University（北卡罗来纳州立大学）
- University of Maryland, College Park（马里兰大学帕克分校）
- University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
机构由 AI 辅助整理，请以论文原文为准。
