mlsanityData to 7 Oct 2026

Navigating Threats: A Survey of Physical Adversarial Attacks on LiDAR Perception Systems in Autonomous Vehicles

arXiv 2409.20426 ↗First posted 30 Sep 2024cs.CVcited by 3

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Abstract (arXiv)

Autonomous vehicles (AVs) rely heavily on LiDAR (Light Detection and Ranging) systems for accurate perception and navigation, providing high-resolution 3D environmental data that is crucial for object detection and classification. However, LiDAR systems are vulnerable to adversarial attacks, which pose significant challenges to the safety and robustness of AVs. This survey presents a thorough review of the current research landscape on physical adversarial attacks targeting LiDAR-based perception systems, covering both single-modality and multi-modality contexts. We categorize and analyze various attack types, including spoofing and physical adversarial object attacks, detailing their methodologies, impacts, and potential real-world implications. Through detailed case studies and analyses, we identify critical challenges and highlight gaps in existing attacks for LiDAR-based systems. Additionally, we propose future research directions to enhance the security and resilience of these systems, ultimately contributing to the safer deployment of autonomous vehicles.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 69 of its 118 references that are in the map. First posted on arXiv 30 Sep 2024.

Builds on

69 of 118 references.

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38 more
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50 most cited of 69 All 69 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025A workflow for generating synthetic LiDAR datasets in simulation environmentscited by 0
  2. 20253D Human Pose and Shape Estimation from LiDAR Point Clouds: A Reviewcited by 0
  3. 2026AD2: Analysis and Detection of Adversarial Threats in Visual Perception for End-to-End Autonomous Driving Systemscited by 0