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 three categories: transferring knowledge from simulation to reality (sim2real), learning in digital twins (DTs), and learning by parallel intelligence (PI) technologies. In this paper, we consider the solutions through the sim2real, DTs, and PI 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 and PI. The presentation also illustrates the far-reaching effects and challenges in the development of sim2real, DTs, and PI in autonomous driving.

翻译：安全性与成本是自动驾驶技术发展的两大核心关切。从学术研究到自动驾驶车辆的商业应用，均需进行充分的仿真与现实世界测试。通常情况下，先在仿真环境中进行大规模测试，再将习得的驾驶知识迁移至现实世界，因此如何将仿真环境中的驾驶知识适配到现实世界成为关键问题。然而，虚拟仿真世界在光照、纹理、车辆动力学及智能体行为等多个方面与现实世界存在差异，这使得弥合虚拟与现实世界之间的鸿沟颇具挑战性。这一差异通常被称为现实鸿沟。近年来，研究者探索了多种应对现实鸿沟问题的方法，可大致分为三类：从仿真到现实的知识迁移、基于数字孪生的学习以及基于平行智能技术的学习。本文通过Sim2real、数字孪生及平行智能技术视角审视相关解决方案，梳理自动驾驶领域的重要应用与创新成果。同时，从算法、模型和仿真器维度展示技术前沿，阐述从Sim2real到数字孪生与平行智能的发展脉络。文中还阐释了Sim2real、数字孪生及平行智能在自动驾驶发展中的深远影响与挑战。