论文标题
SIM2REAL图像翻译对CARLA模拟器中车道保持辅助系统的影响
Effects of Sim2Real Image Translation on Lane Keeping Assist System in CARLA Simulator
论文作者
论文摘要
自动驾驶汽车模拟的优点是在各种环境变量和场景中测试算法而不会浪费时间和资源，但是，现实世界存在视觉差距。在本文中，我们训练了DCLGAN，实际上转换了Carla模拟器的图像，并评估了SIM2Real转换的效果，该转换重点是LKAS（Lane Keep Assiss Systean）算法。为了避免DCLGAN扭曲的车道翻译的情况，我们发现了使用FSIM（特征相似性）的最佳训练超参数。训练后，我们建立了一个系统，该系统将DCLGAN模型与Carla和AV实时连接。然后，我们收集了数据（例如图像，GPS），并使用以下四种方法对其进行了分析。首先，用FID测量了图像现实，我们定量反映了车道特性。通过DCLGAN的CARLA图像的FID值比原始图像小。其次，通过DCLGAN提高了通过ENET-SAD的车道分割精度。第三，在弯曲的路线中，使用DCLGAN的情况更靠近车道的中心，并且成功率很高。最后，在直线路线中，DCLGAN在从车道中心偏离车道之后，提高了车道恢复能力。
Autonomous vehicle simulation has the advantage of testing algorithms in various environment variables and scenarios without wasting time and resources, however, there is a visual gap with the real-world. In this paper, we trained DCLGAN to realistically convert the image of the CARLA simulator and evaluated the effect of the Sim2Real conversion focusing on the LKAS (Lane Keeping Assist System) algorithm. In order to avoid the case where the lane is translated distortedly by DCLGAN, we found the optimal training hyperparameter using FSIM (feature-similarity). After training, we built a system that connected the DCLGAN 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, image reality was measured with FID, which we verified quantitatively reflects the lane characteristics. CARLA images that passed through DCLGAN had smaller FID values than the original images. Second, lane segmentation accuracy through ENet-SAD was improved by DCLGAN. Third, in the curved route, the case of using DCLGAN drove closer to the center of the lane and had a high success rate. Lastly, in the straight route, DCLGAN improved lane restoring ability after deviating from the center of the lane as much as in reality.
