Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
arXiv:2510.09041v3 Announce Type: replace Abstract: Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies. However, its vulnerability to adversarial attacks remains a critical barrier to real-world deployment. Although existing robust methods have achieved success, they still suffer from three key issues: (i) these methods are trained against myopic adversarial attacks, limiting their abilities to respond to more strategic threats, (ii) they have trouble causing truly safety-critical events (e.g., collisions), but instead often result i
The continuous evolution of AI robustness research, especially for safety-critical applications like autonomous driving, necessitates advanced methods to address strategic adversarial attacks.
This research addresses a critical barrier to the real-world deployment of autonomous vehicles, enhancing trust and accelerating their adoption into transportation infrastructure.
Autonomous driving systems will become significantly more resilient to sophisticated adversarial attacks, moving beyond current myopic defenses.
- · Autonomous vehicle developers
- · Ride-sharing companies
- · Smart city infrastructure
- · AI safety researchers
- · Adversarial attackers
- · Insurance companies (potentially lower accident rates)
- · Traditional automotive manufacturing
Enhanced safety and reliability of autonomous driving systems.
Accelerated regulatory approval and public acceptance of L4/L5 autonomous vehicles due to demonstrated robustness.
Increased investment in intelligent adversarial reinforcement learning for other safety-critical AI applications beyond driving, like robotics and industrial control.
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Read at arXiv cs.LG