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Safety and cost are two important concerns for the development of autonomousdriving technologies. From the academic research to commercial applications ofautonomous driving vehicles, sufficient simulation and real world testing arerequired. In general, a large scale of testing in simulation environment isconducted and then the learned driving knowledge is transferred to the realworld, so how to adapt driving knowledge learned in simulation to realitybecomes a critical issue. However, the virtual simulation world differs fromthe real world in many aspects such as lighting, textures, vehicle dynamics,and agents' behaviors, etc., which makes it difficult to bridge the gap betweenthe virtual and real worlds. This gap is commonly referred to as the realitygap (RG). In recent years, researchers have explored various approaches toaddress the reality gap issue, which can be broadly classified into threecategories: 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 inthe field of autonomous driving. Meanwhile, we show the state-of-the-arts fromthe views of algorithms, models, and simulators, and elaborate the developmentprocess from sim2real to DTs and PI. The presentation also illustrates thefar-reaching effects and challenges in the development of sim2real, DTs, and PIin autonomous driving.

How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence