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。

AI 驾驶奥林匹克赛事中以模仿学习方法训练真实世界和仿真数据相结合

Imitation Learning Approach for AI Driving Olympics Trained on  Real-world and Simulation Data Simultaneously

We demonstrate the first application of deep reinforcement learning to
autonomous driving. From randomly initialised parameters, our model is able to
learn a policy for lane following in a handful of training episodes using a
single monocular image as input. We provide a general and easy to obtain
reward: the distance travelled by the vehicle without the safety driver taking
control. We use a continuous, model-free deep reinforcement learning algorithm,
with all exploration and optimisation performed on-vehicle. This demonstrates a
new framework for autonomous driving which moves away from reliance on defined
logical rules, mapping, and direct supervision. We discuss the challenges and
opportunities to scale this approach to a broader range of autonomous driving
tasks.

本研究首次将深度强化学习应用于自动驾驶，通过模型自主探索学习一个仅需单目图像为输入的车道跟踪策略，并采用单一、易于获取的奖励措施：在无安全司机控制下行驶的距离。我们使用连续的、无模型的深度强化学习算法，并在车上执行所有探索和优化过程，为自动驾驶开发了一个新的框架，摆脱了对定义逻辑规则、制图和直接监督的依赖，同时也探讨该方法面对更广泛自动驾驶任务时的挑战和机会。