Neural Reconstruction of LiDAR Point Clouds under Jamming Attacks via Full-Waveform Representation and Simultaneous Laser Sensing

TL;DR

This paper introduces PULSAR-Net, a neural network that reconstructs authentic LiDAR point clouds under jamming attacks by utilizing full-waveform data and a physics-aware dataset, enhancing robustness in autonomous driving.

Contribution

The work presents a novel neural architecture and dataset pipeline for reconstructing LiDAR data under jamming, addressing a critical security vulnerability in autonomous systems.

Findings

PULSAR-Net achieves 92% reconstruction accuracy on synthetic data.

It attains 73% reconstruction rate in real-world scenarios.

The approach effectively distinguishes attack signals from authentic returns.

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 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.