TowardsOptimalStrategiesforTrainingSelf-Driving PerceptionModelsinSimulation

Autonomous driving relies on a huge volume of real-world data to be labeled to high precision. Alternative solutions seek to exploit driving simulators that can generate large amounts of labeled data with aplethora of content variations. However, the domain gap between the synthetic and real data remains, raising the following important question:What arethe best way toutilize aself-driving simulatorforperceptiontasks?