SuperDriverAI：面向端到端基于学习的自动驾驶的设计与实现
SuperDriverAI: Towards Design and Implementation for End-to-End Learning-based Autonomous Driving
AI总结：
本文提出端到端自动驾驶系统SuperDriver AI，利用DNN学习人类驾驶策略并保证安全，引入slit模型和视觉注意力模块提升鲁棒性与可解释性，并在东京真实场景中验证了其有效性。
摘要：
完全自动驾驶已被广泛研究，且正变得日益可行。然而，由于周围人类驾驶员和行人带来的各种不确定性，这种自动驾驶尚未在公共道路上实现。本文提出了一种名为SuperDriver AI的端到端基于学习的自动驾驶系统，其中深度神经网络（DNN）从经验丰富的人类驾驶员处学习驾驶动作与策略，并在保证道路安全的前提下决定要采取的驾驶操作。此外，为提升鲁棒性和可解释性，我们提出了slit模型和视觉注意力模块。我们构建了带有真实世界硬件的数据采集系统和模拟器，并在真实驾驶场景下测试了SuperDriver AI系统。最后，我们在日本东京针对某一驾驶场景收集了150次运行数据，并展示了SuperDriver AI在真实车辆上的演示。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
