通过动态优先级调度提升配送场站无人车协同效率
Framework for Collaborative Operation of Autonomous Delivery Vehicles Within a Marshaling Yard
- 基于场站实时状态动态分配车辆任务优先级
- 在大小场站与高低需求下均提升车辆吞吐量
- 有效避免拥堵，适合物流自动化场景
随着自动驾驶车辆在城市道路中逐步部署，封闭设施如配送场站成为实现完全自主的理想场景。场站内电动车队需完成充电、检查、清洁和装载等一系列顺序任务后才能出站配送。将车辆与固定基础设施结合的混合自动化，可使车辆无需驾驶员即可自主运行，从而加快任务间流转，提升车辆吞吐量。然而，仅依赖静态规则的孤立自治易导致拥堵，引发设施瘫痪。本文提出一种协同自治方案，通过去中心化方式根据场站实时状态动态评分并分配任务优先级，优化车辆调度。在模拟的三种规模（小、中、大）场站与三种需求水平（低、中、高）下测试表明，该方案在所有组合下均优于静态孤立自治，且在高需求时显著降低设施故障率。
原文摘要 · Abstract (English)
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.
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