Reconstructs authentic LiDAR point clouds under jamming attacks with a 92% success rate by exploiting raw full-waveform representations.
April 2, 2026
Original Paper
Neural Reconstruction of LiDAR Point Clouds under Jamming Attacks via Full-Waveform Representation and Simultaneous Laser Sensing
arXiv · 2604.00371
The Takeaway
Discovers that while spoofing pulses blind standard point cloud processing, legitimate signals remain distinguishable in the underlying full-waveform data. This creates a physics-aware defense for autonomous vehicle perception systems.
From the abstract
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 u