用波形数据还原被干扰的激光点云，提升自动驾驶安全性。
Neural Reconstruction of LiDAR Point Clouds under Jamming Attacks via Full-Waveform Representation and Simultaneous Laser Sensing
- 利用波形信号和同步激光感知，识别干扰与真实信号差异。
- 真实场景中车辆点云重建率达92%（静态）和73%（动态）。
- 专为抗干扰设计，适合自动驾驶感知系统优化者。
激光雷达在自动驾驶感知中至关重要，但易受欺骗攻击。干扰攻击通过注入高频激光脉冲，使激光雷达完全失效。我们发现，尽管点云数据变得随机，但原始波形数据仍保留攻击与真实信号的可区分特征。本文提出PULSAR-Net，利用现代激光雷达中未被充分利用的中间波形表示和同步激光感知，重建受干扰下的真实点云。该模型采用新型带轴向空间注意力的U-Net架构，专门识别波形中的攻击信号。为解决现有数据集缺乏干扰下波形数据的问题，我们构建了物理驱动的数据生成流程，合成逼真的干扰波形。尽管仅在合成数据上训练，PULSAR-Net在真实世界静态和驾驶场景中对被干扰车辆的点云重建率分别达92%和73%。
原文摘要 · Abstract (English)
LiDAR sensors are critical for autonomous driving perception, yet remain vulnerable to spoofing attacks. Jamming attacks inject high-frequency laser pulses that completely blind LiDAR sensors by overwhelming authentic returns with malicious signals. We discover that while point clouds become randomized, the underlying full-waveform data retains distinguishable signatures between attack and legitimate signals. In this work, we propose PULSAR-Net, capable of reconstructing authentic point clouds under jamming attacks by leveraging previously underutilized intermediate full-waveform representations and simultaneous laser sensing in modern LiDAR systems. PULSAR-Net adopts a novel U-Net architecture with axial spatial attention mechanisms specifically designed to identify attack-induced signals from authentic object returns in the full-waveform representation. To address the lack of full-waveform representations in existing LiDAR datasets under jamming attacks, we introduce a physics-aware dataset generation pipeline that synthesizes realistic full-waveform representations under jamming attacks. Despite being trained exclusively on synthetic data, PULSAR-Net achieves reconstruction rates of 92% and 73% for vehicles obscured by jamming attacks in real-world static and driving scenarios, respectively.
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