In the rapidly evolving field of autonomous systems, the safety and reliability of the system components are fundamental requirements. These components are often vulnerable to complex and unforeseen environments, making natural edge-case generation essential for enhancing system resilience. This paper presents GENESIS-RL, a novel framework that leverages system-level safety considerations and reinforcement learning techniques to systematically generate naturalistic edge cases. By simulating challenging conditions that mimic the real-world situations, our framework aims to rigorously test entire system's safety and reliability. Although demonstrated within the autonomous driving application, our methodology is adaptable across diverse autonomous systems. Our experimental validation, conducted on high-fidelity simulator underscores the overall effectiveness of this framework.

翻译：在自主系统这一快速发展的领域中，系统组件的安全性和可靠性是基本要求。这些组件常常容易受到复杂且不可预见环境的影响，因此自然边缘场景的生成对于增强系统韧性至关重要。本文提出了GENESIS-RL，一种利用系统级安全考量与强化学习技术系统性地生成逼真边缘场景的新型框架。通过模拟模拟真实世界情境的挑战性条件，该框架旨在严格测试整个系统的安全性和可靠性。尽管演示场景设定在自动驾驶应用中，但所提出的方法可适用于多种自主系统。在高保真模拟器上开展的实验验证充分证明了该框架的整体有效性。