用最小二乘图融合多车检测，提升对抗攻击下的3D目标跟踪鲁棒性。
Robustifying 3D Perception via Least-Squares Graphs for Multi-Agent Object Tracking
- 基于最小二乘图建模多车检测框的微小差异，优化位置精度。
- 在真实数据集上相比最优方法提升23.3%的跟踪准确率。
- 无需额外防御机制，适合自动驾驶等高安全场景。
边缘智能系统（如自动驾驶）的关键感知能力需抵御对抗攻击，以实现对场景中多个目标的长期精准识别与定位。单机追踪虽具抗攻击能力但缺乏情境感知，因此需多机协作提升上下文理解与鲁棒性。本文提出一种基于最小二乘图的3D LiDAR场景对抗噪声缓解框架，通过多智能体对抗包围框构建全连接图，利用差分坐标与锚点优化各检测中心的位置误差，实现多车检测融合与精炼，再分两阶段完成关联与追踪，进一步抑制对抗威胁。在真实世界V2V4Real数据集上的大量实验表明，该方法在挑战性对抗条件下显著优于现有单机与多机追踪框架，性能最高提升23.3%，且无需依赖额外防御机制，展现出强韧性。
原文摘要 · Abstract (English)
The critical perception capabilities of EdgeAI systems, such as autonomous vehicles, are required to be resilient against adversarial threats, by enabling accurate identification and localization of multiple objects in the scene over time, mitigating their impact. Single-agent tracking offers resilience to adversarial attacks but lacks situational awareness, underscoring the need for multi-agent cooperation to enhance context understanding and robustness. This paper proposes a novel mitigation framework on 3D LiDAR scene against adversarial noise by tracking objects based on least-squares graph on multi-agent adversarial bounding boxes. Specifically, we employ the least-squares graph tool to reduce the induced positional error of each detection's centroid utilizing overlapped bounding boxes on a fully connected graph via differential coordinates and anchor points. Hence, the multi-vehicle detections are fused and refined mitigating the adversarial impact, and associated with existing tracks in two stages performing tracking to further suppress the adversarial threat. An extensive evaluation study on the real-world V2V4Real dataset demonstrates that the proposed method significantly outperforms both state-of-the-art single and multi-agent tracking frameworks by up to 23.3% under challenging adversarial conditions, operating as a resilient approach without relying on additional defense mechanisms.
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