Edge Computing System for Automated Vehicle and Container Verification
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Solution Overview
Problem
The logistics and shipping industries face significant delays due to the variety of forms, documents, and containers, leading to inefficiencies in check-in and check-out processes at facilities, resulting in long wait times and congestion.
Innovation Solution
An IoT-based edge computing system equipped with sensors and image capture technologies that automate the verification and documentation process for vehicles, containers, and drivers, using machine learning models to identify and extract information from various sources, including documents and sensor data, to streamline entry and exit procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual verification and documentation processes are used for vehicles, containers, and drivers, then accuracy of verification can be maintained, but check-in and check-out times increase significantly causing congestion and delays
Solution Approach 1:
The patent replaces manual mechanical verification processes with an automated sensor-based system. Sensors capture images and data from vehicles, containers, and drivers, which are then processed by machine learning models to automatically verify identities and complete documentation, eliminating the need for manual checking and significantly reducing wait times
Solution Approach 2:
The system enables self-service verification where the automated sensor system and machine learning models independently perform identification, verification, and documentation completion without human intervention. The system processes multiple vehicles simultaneously, allowing the facility to handle higher throughput while maintaining verification accuracy
2Reliability
If multiple forms and documents are manually processed for each vehicle and container, then compliance accuracy can be ensured, but the complexity of the check-in and check-out process increases
Solution Approach 1:
The patent implements a universal automated system that handles multiple verification functions simultaneously. The sensor system captures various types of data (vehicle images, container images, driver images), and the machine learning models process all this information to complete multiple forms and documents in one integrated process, reducing overall process complexity while maintaining compliance accuracy
Solution Approach 2:
The system introduces an intermediary automated processing layer between the physical verification objects and the documentation requirements. The machine learning models act as intermediaries that translate sensor data into verified information and completed forms, simplifying the complex interaction between multiple verification requirements and documentation needs
3Productivity
If automated sensor systems and machine learning models are deployed, then check-in and check-out speeds increase, but the device complexity and initial setup requirements increase
Solution Approach 1:
The system performs preliminary data capture and processing before vehicles actually need verification. Sensors continuously capture images and data, and machine learning models pre-process this information so that when verification is needed, the system can quickly retrieve and use the pre-processed data, maintaining high throughput without requiring overly complex real-time processing systems
Data Source
AI summary
Techniques are described for automating the check in and check out process at a logistics facility. For example, a sensor system may be configured to capture sensor data associated with an approaching vehicle. The sensor system may utilize the sensor data to extra information usable to complete forms, assess damage, and authenticate the shipment.