Racing autonomous cars faster than the best human drivers has been a
longstanding grand challenge for the fields of Artificial Intelligence and
robotics. Recently, an end-to-end deep reinforcement learning agent met this
challenge in a high-fidelity racing simulator, Gran Turismo. However, this
agent relied on global features that require instrumentation external to the
car. This paper introduces, to the best of our knowledge, the first super-human
car racing agent whose sensor input is purely local to the car, namely pixels
from an ego-centric camera view and quantities that can be sensed from on-board
the car, such as the car's velocity. By leveraging global features only at
training time, the learned agent is able to outperform the best human drivers
in time trial (one car on the track at a time) races using only local input
features. The resulting agent is evaluated in Gran Turismo 7 on multiple tracks
and cars. Detailed ablation experiments demonstrate the agent's strong reliance
on visual inputs, making it the first vision-based super-human car racing
agent.

使用仅局部输入的姿态视角摄像头图像和车辆信息，通过训练期间仅借助全局特征，该研究介绍了第一个视觉型超人级赛车智能体，其在时间赛中击败了最优秀的人类驾驶员。