Summary of A Survey on Adversarial Robustness Of Lidar-based Machine Learning Perception in Autonomous Vehicles, by Junae Kim et al.
A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles
by Junae Kim, Amardeep Kaur
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this survey, researchers combine the fields of Adversarial Machine Learning (AML) and autonomous vehicles to investigate vulnerabilities in LiDAR-based systems. The study explores the threat landscape, including cyber-attacks on sensors and adversarial perturbations, as well as defensive strategies employed to counter these threats. By emphasizing the need for robust defenses, this paper presents a concise overview of challenges and advances in securing autonomous driving systems against adversarial attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how AI and car technology can work together, but also at the dangers that come with it. The study finds that there are many ways that bad guys could hack into self-driving cars and make them do things they don’t want to do. To fix this problem, the researchers look at different ways to defend against these attacks. |
Keywords
* Artificial intelligence * Machine learning
