Yard Mission Control API Integration for Autonomous Trailer Moves
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Solution Overview
Problem
Yard management systems struggle to efficiently integrate manual and autonomous vehicle operations, leading to inefficiencies and disruptions due to incomplete status visibility and unsuitable task allocation, while autonomous vehicles face challenges in identifying suitable parking spots and trailer types, and remote assistance is hindered by bandwidth limitations.
Innovation Solution
Implementing mission control directly with the yard management system via APIs to update status and automate task selection, using deep learning for spot identification, and employing a vehicle credential management server for software updates, along with a video bakery to optimize remote assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If mission control operates independently from the yard management system, then autonomous vehicle operations can be managed separately, but the user cannot see the true status of all moves within the yard and must simultaneously monitor information from mission control
Solution Approach 1:
The patent merges mission control with the yard management system by integrating the autonomous vehicle status into the YMS interface. This allows users to view both manual and autonomous move statuses in a single unified system, eliminating the need to separately monitor mission control while maintaining complete visibility of all yard operations.
Solution Approach 2:
The yard management system is enhanced to serve multiple functions: it continues to manage manual vehicles and inventory while also displaying and coordinating autonomous vehicle operations. This multi-functionality allows a single system to handle both traditional yard management tasks and autonomous fleet monitoring without requiring separate dedicated systems.
2Extent of automation
If the YMS sends all move requests to mission control for autonomous handling, then automation level increases, but manual movements are disrupted when autonomous vehicles cannot complete moves
Solution Approach 1:
The system performs preliminary assessment of move requests before assigning them to autonomous vehicles. Mission control evaluates whether each move is suitable for autonomous handling based on predefined criteria and yard conditions, making advance decisions that prevent disruptions to manual operations while maximizing autonomous vehicle utilization.
Solution Approach 2:
The system dynamically adjusts the allocation of move requests between autonomous and manual vehicles based on real-time conditions. When autonomous vehicles are unavailable or unable to complete a move, the system flexibly transitions the task to manual handling without rigid adherence to predetermined assignments, ensuring continuous operational reliability.
3Measurement precision
If autonomous vehicles autonomously select parking spots using imagery and deep learning, then spot identification capability is enhanced, but the system complexity and processing requirements increase
Solution Approach 1:
Autonomous vehicles are equipped with self-service capabilities to identify and select their own parking spots using onboard imagery systems and deep learning algorithms. The vehicles independently process visual data to detect empty spots, assess suitability, and navigate to selected locations without requiring external guidance, thereby reducing the need for complex centralized spot management infrastructure.
Solution Approach 2:
The patent replaces traditional mechanical or manual spot identification methods with optical imagery systems and deep learning processing. Instead of using physical markers, sensors, or manual verification, the system uses computer vision technology to automatically detect and classify parking spots, substituting computational processing for physical identification mechanisms.
4Loss of information
If mission control continuously monitors and updates yard inventory status, then information currentness is improved, but the computational load and communication overhead increase
Solution Approach 1:
Mission control continuously monitors yard inventory status by maintaining persistent communication channels with autonomous vehicles and the yard management system. This continuous monitoring ensures that inventory information remains current without requiring periodic batch updates, allowing real-time visibility of trailer locations and parking spot status while optimizing communication efficiency through event-driven updates only when changes occur.
Data Source
AI summary
A mission control may receive, from a yard management system (YMS), a move request identifying a destination spot in a yard and one or more of a trailer identifier of a trailer to be moved, a pick-up spot of the trailer, and a trailer type. Mission control determines whether the move request is feasible for an autonomous vehicle (AV). When the move request is feasible, mission control generates a mission defining directives to control the AV to move the trailer from the pick-up spot to the destination spot and sets, via an application programming interface (API) of the YMS, a status of the move request to indicate autonomous move is scheduled.