论文标题
同时在现实世界和模拟数据上培训的AI驾驶奥运会的模仿学习方法
Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously
论文作者
论文摘要
在本文中，我们描述了在AI驾驶奥运会竞争挑战之后解决车道的获胜方法，这是通过模仿一组模拟和现实世界数据的模仿学习。 AI驾驶奥运会是一场两阶段的比赛：在第一阶段，算法在模拟环境中竞争，最好的环境前进到了真实世界的决赛。参与者在比赛中遇到的主要问题之一是，在模拟环境中为最佳性能而训练的算法在现实世界环境中不存在，反之亦然。经典控制算法在任务之间也不能很好地转化，因为它们必须调整为特定的驾驶条件，例如照明，道路类型，相机位置等。为了克服这个问题，我们采用了模仿学习算法，并在从模拟和现实世界中从仿真和实际环境中表现出色的数据集中培训了该问题，并在所有环境中都表现出色。
In this paper, we describe our winning approach to solving the Lane Following Challenge at the AI Driving Olympics Competition through imitation learning on a mixed set of simulation and real-world data. AI Driving Olympics is a two-stage competition: at stage one, algorithms compete in a simulated environment with the best ones advancing to a real-world final. One of the main problems that participants encounter during the competition is that algorithms trained for the best performance in simulated environments do not hold up in a real-world environment and vice versa. Classic control algorithms also do not translate well between tasks since most of them have to be tuned to specific driving conditions such as lighting, road type, camera position, etc. To overcome this problem, we employed the imitation learning algorithm and trained it on a dataset collected from sources both from simulation and real-world, forcing our model to perform equally well in all environments.
