Driverless vehicles promise a host of societal benefits including dramatically improved safety, increased accessibility, greater productivity, and higher quality of life. As this new technology approaches widespread deployment, both industry and government are making provisions for teleoperations systems, in which remote human agents provide assistance to driverless vehicles. This assistance can involve real-time remote operation and even ahead-of-time input via human-in-the-loop artificial intelligence systems. In this paper, we address the problem of staffing such a remote support center. Our analysis focuses on the tradeoffs between the total number of remote agents, the reliability of the remote support system, and the resulting safety of the driverless vehicles. By establishing a novel connection between queues with large batch arrivals and storage processes, we determine the probability of the system exceeding its service capacity. This connection drives our staffing methodology. We also develop a numerical method to compute the exact staffing level needed to achieve various performance measures. This moment generating function based technique may be of independent interest, and our overall staffing analysis may be of use in other applications that combine human expertise and automated systems.

中文翻译：

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超越安全驱动因素：为自动驾驶汽车配备远程操作系统

无人驾驶汽车有望带来一系列社会效益，包括显着提高安全性、增加可达性、提高生产力和提高生活质量。随着这项新技术接近广泛部署，行业和政府都在为远程操作系统做出规定，其中远程人工代理为无人驾驶车辆提供帮助。这种帮助可能涉及实时远程操作，甚至可以通过人在环人工智能系统进行提前输入。在本文中，我们解决了为此类远程支持中心配备人员的问题。我们的分析侧重于远程代理总数、远程支持系统的可靠性以及由此产生的无人驾驶车辆安全性之间的权衡。通过在具有大批量到达的队列和存储过程之间建立新的连接，我们确定系统超出其服务容量的概率。这种联系推动了我们的人员配备方法。我们还开发了一种数值方法来计算实现各种绩效指标所需的确切人员配备水平。这种基于矩生成函数的技术可能具有独立的兴趣，我们的整体人员配置分析可能在其他结合了人类专业知识和自动化系统的应用程序中有用。