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:安全和成本是发展自动驾驶技术的两个重要问题。从学术研究到商业应用的自动驾驶车辆,需要进行足够的模拟和现实世界测试。通常在模拟环境中进行大规模的测试,然后将学习到的驾驶知识转移到现实世界,因此如何将在模拟环境中学到的驾驶知识适应于现实世界成为了关键问题。然而,虚拟模拟世界与现实世界在许多方面(如光照、纹理、车辆动力学和代理人行为等)不同,这使得缩小虚拟和现实世界之间的差距变得困难。这种差距通常称为现实差距(RG)。近年来,研究人员探索了各种方法来解决现实差距的问题,可以广泛分为两类:从模拟到现实的知识转移(sim2real)和数字孪生(DTs)中的学习。在本文中,我们考虑了通过sim2real和DTs技术的解决方案,并回顾了自动驾驶领域的重要应用和创新。同时,我们从算法、模型和仿真器的角度展示了最新技术发展现状,并详细阐述了从sim2real到数字孪生的发展过程。报告还阐述了sim2real和DTs发展在自动驾驶中的广泛影响。