Summary of Cp-guard: Malicious Agent Detection and Defense in Collaborative Bird’s Eye View Perception, by Senkang Hu et al.
CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird’s Eye View Perception
by Senkang Hu, Yihang Tao, Guowen Xu, Yiqin Deng, Xianhao Chen, Yuguang Fang, Sam Kwong
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper proposes a novel method called CP-Guard, a defense mechanism for Collaborative Perception (CP) to detect and eliminate malicious agents in autonomous driving. CP involves multiple connected and autonomous vehicles sharing their perception information, but this makes it vulnerable to attacks. The authors develop a probability-agnostic sample consensus (PASAC) method to verify the consistency of ego CAV’s perception results with its collaborators. They also define a collaborative consistency loss (CCLoss) as a verification criterion for consensus. Experiments in collaborative bird’s eye view (BEV) tasks demonstrate the effectiveness of CP-Guard. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make self-driving cars safer by stopping bad guys from hacking into them. It’s like when you’re playing a game with friends and someone tries to cheat, but instead of just getting upset, this system finds out what’s going on and stops it. The scientists came up with a new way to do this called CP-Guard, which makes sure that the cars are working together correctly. They tested it and it worked really well! |
Keywords
» Artificial intelligence » Probability
