Learn-to-Race Autonomous Racing Virtual Challenge hosted on www<dot>aicrowd<dot>com platform consisted of two tracks: Single and Multi Camera. Our UniTeam team was among the final winners in the Single Camera track. The agent is required to pass the previously unknown F1-style track in the minimum time with the least amount of off-road driving violations. In our approach, we used the U-Net architecture for road segmentation, variational autocoder for encoding a road binary mask, and a nearest-neighbor search strategy that selects the best action for a given state. Our agent achieved an average speed of 105 km/h on stage 1 (known track) and 73 km/h on stage 2 (unknown track) without any off-road driving violations. Here we present our solution and results.

翻译：在www <dot>aicrowd <dot>com平台上主持学习到Race 自动竞赛虚拟挑战平台由两条轨道组成: 单一和多相机。 我们的UniTeam团队是单一相机赛程的最后赢家之一。 代理必须在最短的时间内通过先前未知的F1型赛道, 且越野驾驶违规次数最少。 在我们的方法中, 我们使用 U-Net 结构进行路段分割, 使用变式自动编码来编码道路二进制面具, 以及一个为特定州选择最佳行动的近邻搜索战略。 我们的代理在第一( 已知赛道) 阶段实现了105公里/小时的平均速度, 在第二( 未知赛道) 阶段实现了73公里/小时的平均速度, 没有任何越野驾驶违规事件。 我们在这里展示了我们的解决方案和结果 。