Framework for Collaborative Operation of Autonomous Delivery Vehicles Within a Marshaling Yard
James O'Hara, Karl Wunderlich, Gregory Stevens
TLDR
This paper introduces an orchestrated autonomy framework for marshaling yards, boosting vehicle throughput and reducing gridlock.
Key contributions
- Addresses gridlock in autonomous marshaling yards caused by static operational rules.
- Proposes an orchestrated autonomy solution with dynamic, decentralized vehicle priority scoring.
- Optimally assigns vehicles to tasks, significantly increasing overall throughput.
- Reduces facility failures, particularly under high demand, compared to static autonomy.
Why it matters
Autonomous vehicles in closed facilities like marshaling yards offer a practical path to full autonomy. This framework prevents gridlock and optimizes operations, making such deployments more efficient and reliable. It's crucial for scaling autonomous delivery fleets.
Original Abstract
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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