Yard Check-In Automation Using IoT and ML Verification
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Solution Overview
Problem
Conventional check-in and check-out processes at storage facilities, yards, and logistics centers are time-consuming and prone to delays due to manual documentation review, leading to logistical inefficiencies, driver communication issues, and compliance challenges, with potential penalties and fines.
Innovation Solution
A system utilizing IoT devices and machine learning algorithms to automate the capture and verification of vehicle and driver credentials, perform inspections, and manage asset transfers, reducing manual intervention and enhancing real-time data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual check-in and check-out processes are used with trained facility operators reviewing documentation, then verification accuracy and compliance are maintained, but processing time increases and logistical delays occur
Solution Approach 1:
The patent replaces manual mechanical review processes with automated image capture devices, sensors, and machine learning algorithms. The system automatically captures images of vehicles, drivers, and documentation, then uses computer vision and ML models to verify credentials and compliance requirements, eliminating the need for manual operator review while maintaining verification accuracy.
Solution Approach 2:
The system enables self-service check-in and check-out by allowing vehicles to present their own credentials through automated image capture and verification. The machine learning system independently validates documentation and vehicle conditions without requiring human intervention, making the process autonomous and significantly faster.
2Reliability
If manual documentation review is performed by facility operators, then complex documentation can be verified, but the process becomes time-consuming and requires specially trained personnel
Solution Approach 1:
The patent replaces manual documentation review with automated image capture devices and machine learning algorithms. The system captures images of documentation, uses optical character recognition and ML models to extract and verify information, thereby eliminating the need for specially trained operators while maintaining verification capability for complex documentation.
Solution Approach 2:
The patent introduces an intermediary machine learning system that acts as a bridge between raw documentation images and verification decisions. The ML models process and interpret complex documentation, extracting relevant information and making verification determinations, thereby simplifying the overall system architecture while maintaining high verification accuracy.
3Reliability
If single vehicle or document issues are manually processed, then individual problems can be identified, but logistical delays occur at entry and exit locations
Solution Approach 1:
The patent implements continuous automated verification processing that operates without interruption. Multiple image capture devices and sensors continuously monitor vehicles, drivers, and documentation as they pass through entry and exit points. The machine learning system processes this continuous data stream in real-time, ensuring that issue detection does not interrupt the flow of vehicles and maintaining high throughput.
Solution Approach 2:
The system performs preliminary verification of vehicle credentials, driver authorization, and documentation completeness before vehicles fully enter or exit the facility. By pre-validating all requirements upfront, the system identifies potential issues before they cause delays, allowing for proactive resolution and maintaining continuous operational flow.
Data Source
AI summary
Techniques are described for automating the check in and check out and inspection process at a logistics facility. For example, a system may be configured to capture sensor data associated with an approaching vehicle. The sensor system may utilize the sensor data to extract information usable to complete forms, assess damage, and authenticate the shipment.