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Autonomous vehicle simulation has the advantage of testing algorithms invarious environment variables and scenarios without wasting time and resources,however, there is a visual gap with the real-world. In this paper, we trainedDCLGAN to realistically convert the image of the CARLA simulator and evaluatedthe effect of the Sim2Real conversion focusing on the LKAS (Lane Keeping AssistSystem) algorithm. In order to avoid the case where the lane is translateddistortedly by DCLGAN, we found the optimal training hyperparameter using FSIM(feature-similarity). After training, we built a system that connected theDCLGAN model with CARLA and AV in real-time. Then, we collected data (e.g.images, GPS) and analyzed them using the following four methods. First, imagereality was measured with FID, which we verified quantitatively reflects thelane characteristics. CARLA images that passed through DCLGAN had smaller FIDvalues than the original images. Second, lane segmentation accuracy throughENet-SAD was improved by DCLGAN. Third, in the curved route, the case of usingDCLGAN drove closer to the center of the lane and had a high success rate.Lastly, in the straight route, DCLGAN improved lane restoring ability afterdeviating from the center of the lane as much as in reality.

Effects of Sim2Real Image Translation on Lane Keeping Assist System in CARLA Simulator