End-to-end learning has emerged as a major paradigm for developing autonomous systems. Unfortunately, with its performance and convenience comes an even greater challenge of safety assurance. A key factor of this challenge is the absence of the notion of a low-dimensional and interpretable dynamical state, around which traditional assurance methods revolve. Focusing on the online safety prediction problem, this paper proposes a configurable family of learning pipelines based on generative world models, which do not require low-dimensional states. To implement these pipelines, we overcome the challenges of learning safety-informed latent representations and missing safety labels under prediction-induced distribution shift. These pipelines come with statistical calibration guarantees on their safety chance predictions based on conformal prediction. We perform an extensive evaluation of the proposed learning pipelines on two case studies of image-controlled systems: a racing car and a cartpole.

翻译：端到端学习已成为开发自主系统的主要范式。然而，其性能与便利性带来了更大的安全保证挑战。这一挑战的关键因素在于缺乏传统保证方法所依赖的低维可解释动态状态概念。本文聚焦在线安全预测问题，提出一种基于生成式世界模型的可配置学习流水线系列，该方案无需低维状态。为实现这些流水线，我们克服了学习安全信息潜在表征以及在预测诱导分布偏移下缺失安全标签的挑战。这些流水线基于保形预测方法，对其安全概率预测提供统计校准保证。我们针对图像控制系统的两个案例研究（赛车和倒立摆）对所提出的学习流水线进行了广泛评估。