An important aspect of automated driving is to handle situations where it fails or is not allowed in specific traffic situations. This case study explores means, by which control transitions in a mixed autonomy system can be organized in order to minimize their adverse impact on traffic flow. We assess a number of different approaches for a coordinated management of transitions, covering classic traffic management paradigms and AI-driven controls. We demonstrate that they yield excellent results when compared to a do-nothing scenario. This text further details a model for control transitions that is the basis for the simulation study presented. The results encourage the deployment of reinforcement learning on the control problem for a scenario with mandatory take-over requests.

中文翻译：

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基于强化学习的交通控制：减轻控制转换的不利影响


自动驾驶的一个重要方面是处理在特定交通情况下失败或不允许的情况。本案例研究探讨了在混合自治系统中组织控制转换的方法，以尽量减少对交通流的不利影响。我们评估了多种不同的协调过渡管理方法，涵盖经典的交通管理范例和人工智能驱动的控制。我们证明，与什么都不做的情况相比，它们可以产生出色的结果。本文进一步详细介绍了控制转换模型，该模型是所提出的仿真研究的基础。结果鼓励在具有强制接管请求的场景的控制问题上部署强化学习。