Summary of Vcat: Vulnerability-aware and Curiosity-driven Adversarial Training For Enhancing Autonomous Vehicle Robustness, by Xuan Cai et al.
VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustness
by Xuan Cai, Zhiyong Cui, Xuesong Bai, Ruimin Ke, Zhenshu Ma, Haiyang Yu, Yilong Ren
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed Vulnerability-aware and Curiosity-driven Adversarial Training (VCAT) framework is a pioneering approach to improve the robustness of autonomous vehicles (AVs) against malicious attacks. Existing adversarial training methods can get stuck in exploiting established vulnerabilities, leading to poor improvement for AVs. VCAT introduces a surrogate network to fit the value function of the AV victim, providing dense information about its inherent vulnerabilities. This framework also employs random network distillation to characterize the novelty of the environment and guide the attacker in exploring unexplored territories. In the victim defense training phase, the AV is trained in critical scenarios where the pretrained attacker generates attack behaviors. Experimental results show that VCAT significantly improves the robust control capabilities of learning-based AVs, outperforming conventional training modalities and alternative reinforcement learning counterparts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AVs need to be protected from malicious attacks in complex traffic environments. Researchers have developed adversarial training methods to make AVs more robust. However, these methods can get stuck and not improve the AV’s performance well. A new approach called VCAT tries to solve this problem by understanding what makes an AV vulnerable and using that information to train the AV to be more secure. The VCAT framework uses a special network to understand the AV’s vulnerabilities and another technique to make the attacker explore new areas. This helps the AV learn how to defend itself better in different scenarios. The results show that VCAT can significantly improve an AV’s ability to stay safe, even when faced with attacks. |
Keywords
» Artificial intelligence » Distillation » Reinforcement learning
