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.

本文描述了赢得 AI 驾驶奥林匹克比赛车道追踪挑战的方法，该方法通过混合模拟和真实数据上的模仿学习实现。