As autonomous vehicles slowly deploy into urban roads for limited use cases with significant edge case issues, closed facilities like marshaling yards provide a ripe case for combining lower-level vehicle autonomy with fixed infrastructure to create full autonomy without similar edge case concerns. Within a delivery marshaling yard, electric fleet vehicles complete a set of sequential tasks (charging, inspection, cleaning, and loading) before exiting the yard with their new load of deliveries. Hybrid automation of the vehicles and infrastructure can allow these vehicles to reach full autonomy and navigate the facility without the need of a driver, allowing for quicker movement between tasks increasing vehicle throughput. However, isolated autonomous operations based on static rules are prone to gridlock causing facility failures that temporarily shut down operations. Our orchestrated autonomy solution uses decentralized, dynamic priority scoring of vehicles based on the current status of the marshaling yard to optimally assign vehicles to tasks to increase vehicle throughput. Using a simulated facility with three marshaling yard sizes (small, medium, and large) and three demand levels (low, medium, high), we demonstrated that our orchestration solution increases vehicle throughput above static, isolated autonomy for all combinations of yard size and demand, while reducing facility failures at high demand levels.

翻译：随着自动驾驶车辆在有限应用场景下逐步部署于城市道路，并面临显著边缘案例问题，诸如编组场等封闭设施为将较低级别的车辆自主性与固定基础设施相结合以实现完全自主性（而不涉及类似边缘案例问题）提供了成熟案例。在配送编组场内，电动车队车辆需完成一系列顺序任务（充电、检查、清洁和装载），随后携带新装载的货物驶离编组场。车辆与基础设施的混合自动化可使这些车辆达到完全自主状态，无需驾驶员即可在设施内导航，从而加快任务间移动速度，提升车辆吞吐量。然而，基于静态规则的孤立自主运行易导致死锁，引发设施故障并暂时中断运行。我们的编排自主解决方案采用基于编组场当前状态的去中心化动态优先级评分机制，为车辆分配最优任务，从而提高车辆吞吐量。通过使用包含三种编组场规模（小型、中型、大型）和三种需求水平（低、中、高）的模拟设施，我们证明：在所有编组场规模与需求组合下，我们的编排方案均能实现高于静态孤立自主的车辆吞吐量，同时在高需求水平下减少设施故障。