arXiv:2510.06970v2 Announce Type: replace-cross Abstract: Compliance with maritime traffic rules is essential for the safe operation of autonomous vessels, yet training reinforcement learning (RL) agents to adhere to them is challenging. The behavior of RL agents is shaped by the training scenarios they encounter, but creating scenarios that capture the complexity of maritime navigation is non-trivial, and real-world data alone is insufficient. To address this, we propose a falsification-driven RL approach that generates adversarial training scenarios in which the vessel under test violates ma
The increasing sophistication of AI models and the imperative for real-world safety in autonomous systems are driving innovation in robust training methodologies like falsification-driven AI.
This development allows for more reliable and safer autonomous systems, particularly in high-stakes environments like maritime navigation, reducing risks and accelerating adoption.
Autonomous systems like vessels can now be trained more effectively to comply with complex rules and operate safely even in scenarios not encountered in real-world data, expanding their operational envelopes.
- · Autonomous shipping companies
- · AI safety researchers
- · Defence contractors
- · Simulation software providers
- · Developers relying solely on real-world data
- · Inflexible legacy maritime systems
Autonomous vessels achieve higher safety certifications and broader operational deployment.
The methodology extends to other complex autonomous systems like self-driving cars and aerial drones, improving their robustness and regulatory acceptance.
The reduced risk perception for autonomous systems accelerates the automation of entire industries, leading to significant economic restructuring.
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Read at arXiv cs.LG