Framework for Collaborative Operation of Autonomous Delivery Vehicles Within a Marshaling Yard

TL;DR

This paper presents a decentralized, dynamic priority-based orchestration framework for autonomous delivery vehicles in marshaling yards, improving throughput and reducing failures.

Contribution

It introduces a novel orchestrated autonomy system combining vehicle and infrastructure automation with dynamic priority scoring.

Findings

Increased vehicle throughput across all yard sizes and demand levels.

Reduced facility failures at high demand levels.

Demonstrated effectiveness in simulated environments.

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.