Safety and cost are two important concerns for the development of autonomous driving technologies. From the academic research to commercial applications of autonomous driving vehicles, sufficient simulation and real world testing are required. In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue. However, the virtual simulation world differs from the real world in many aspects such as lighting, textures, vehicle dynamics, and agents' behaviors, etc., which makes it difficult to bridge the gap between the virtual and real worlds. This gap is commonly referred to as the reality gap (RG). In recent years, researchers have explored various approaches to address the reality gap issue, which can be broadly classified into two categories: transferring knowledge from simulation to reality (sim2real) and learning in digital twins (DTs). In this paper, we consider the solutions through the sim2real and DTs technologies, and review important applications and innovations in the field of autonomous driving. Meanwhile, we show the state-of-the-arts from the views of algorithms, models, and simulators, and elaborate the development process from sim2real to DTs. The presentation also illustrates the far-reaching effects of the development of sim2real and DTs in autonomous driving.

翻译：安全性和成本是自动驾驶技术发展的两个核心关注点。从自动驾驶汽车的学术研究到商业应用，都需要进行充分的仿真与真实世界测试。通常，研究先在仿真环境中进行大规模测试，再将学习到的驾驶知识迁移到现实世界。因此，如何将在仿真中习得的驾驶知识适配到现实环境成为关键问题。然而，虚拟仿真世界与现实世界在光照、纹理、车辆动力学及智能体行为等诸多方面存在差异，这使得跨越虚拟与现实世界的鸿沟变得困难。这一鸿沟通常被称为现实差距（RG）。近年来，研究人员探索了多种应对现实差距问题的方法，这些方法可大致分为两类：从仿真到现实的迁移学习（sim2real）以及在数字孪生（DTs）中的学习。本文通过sim2real和DTs技术审视相关解决方案，综述了自动驾驶领域的重要应用与创新。同时，我们从算法、模型及仿真器视角展示了当前前沿进展，并阐述了从sim2real到DTs的发展历程。本文还揭示了sim2real与DTs发展对自动驾驶领域的深远影响。