  Fully autonomous driving has been widely studied and is becoming increasingly
feasible. However, such autonomous driving has yet to be achieved on public
roads, because of various uncertainties due to surrounding human drivers and
pedestrians. In this paper, we present an end-to-end learningbased autonomous
driving system named SuperDriver AI, where Deep Neural Networks (DNNs) learn
the driving actions and policies from the experienced human drivers and
determine the driving maneuvers to take while guaranteeing road safety. In
addition, to improve robustness and interpretability, we present a slit model
and a visual attention module. We build a datacollection system and emulator
with real-world hardware, and we also test the SuperDriver AI system with
real-world driving scenarios. Finally, we have collected 150 runs for one
driving scenario in Tokyo, Japan, and have shown the demonstration of
SuperDriver AI with the real-world vehicle.
