  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.
