Intelligent safety-critical systems such as autonomous cars operate in the complex open world; as such, they must not only deliver excellent performance in their operational design domain, but also be robust to all unexpected inputs caused by extreme weather and lighting conditions, changes of operation domains, and rare but potentially catastrophic situations. In this talk, I will present novel approaches covering the full spectrum of settings for this challenge, ranging from domain synthesis to unsupervised domain adaptation, to test-time domain adaptation, and domain generalization. Our approaches have achieved state-of-the-art performance for a wide variety of domain changes, e.g. cross-datasets, normal-to-adverse, and synthetic-to-real. At the end of the talk, I will briefly present two benchmarks that we have developed for robust visual perception.