mlsanityData to 14 Oct 2026

A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles

arXiv 2411.13778 ↗First posted 21 Nov 2024cs.LG cs.AI cs.CRcited by 3

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Abstract (arXiv)

In autonomous driving, the combination of AI and vehicular technology offers great potential. However, this amalgamation comes with vulnerabilities to adversarial attacks. This survey focuses on the intersection of Adversarial Machine Learning (AML) and autonomous systems, with a specific focus on LiDAR-based systems. We comprehensively explore the threat landscape, encompassing cyber-attacks on sensors and adversarial perturbations. Additionally, we investigate defensive strategies employed in countering these threats. This paper endeavors to present a concise overview of the challenges and advances in securing autonomous driving systems against adversarial threats, emphasizing the need for robust defenses to ensure safety and security.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 179 references that are in the map. First posted on arXiv 21 Nov 2024.

Builds on

19 of 179 references.

References · 19

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2013Intriguing properties of neural networkscited by 5,155
  3. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,106
  4. 2017Adversarial Patchcited by 517
  5. 2020Weight Poisoning Attacks on Pre-trained Modelscited by 341
  6. 2023Poisoning Web-Scale Training Datasets is Practicalcited by 290
  7. 2020A2D2: Audi Autonomous Driving Datasetcited by 190
  8. 2018PointCloud Saliency Mapscited by 112
  9. 2022Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptionscited by 80
  10. 2019Adversarial Attack and Defense on Point Setscited by 63
  11. 2019Adversarial Objects Against LiDAR-Based Autonomous Driving Systemscited by 63
  12. 2021KITTI-CARLA: a KITTI-like dataset generated by CARLA Simulatorcited by 34
7 more
  1. 2020Adversarial Attacks on Monocular Depth Estimationcited by 20
  2. 2022A Survey of Robust 3D Object Detection Methods in Point Cloudscited by 10
  3. 2023LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions, and New Attack Strategiescited by 9
  4. 2020Monocular Depth Estimators: Vulnerabilities and Attackscited by 7
  5. 2023Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systemscited by 6
  6. 2014Exploiting Social Navigationcited by 3
  7. 2023What Would Trojans Do? Exploiting Partial-Information Vulnerabilities in Autonomous Vehicle Sensingcited by 2

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Cited by year (2026 to 14 Oct)

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

3 papers.

  1. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 126
  2. 2026BadDreamer: Transferable Backdoor Attacks against Video World Models for Autonomous Drivingcited by 1
  3. 2026AD2: Analysis and Detection of Adversarial Threats in Visual Perception for End-to-End Autonomous Driving Systemscited by 0