Refuse Container Identification Using Parcel-Correlated Lift-Arm Location
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Solution Overview
Problem
Determining which refuse container is associated with a particular customer is challenging, especially in dense urban areas where container placements are inconsistent, making it difficult for refuse collection companies to accurately track service events and generate invoices.
Innovation Solution
A method and system using sensor data from refuse collection vehicles (RCVs) to detect operational states of body components, correlate location data with parcel data, and apply algorithms to identify the associated entity with the container, incorporating GPS coordinates and vehicle configuration to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify refuse containers, then the system is simple to operate, but the ability to accurately determine container-customer association deteriorates in dense urban areas
Solution Approach 1:
The patent combines multiple data sources (sensor data from RCVs, location data from GPS, operational data from body sensors, and parcel data) into a unified identification system. This merging of data streams enables accurate container-customer association by correlating container location and RCV operational state with parcel information, resolving the contradiction between identification accuracy and system simplicity.
Solution Approach 2:
The system introduces parcel data as an intermediary layer between the RCV operational data and container identification. By using parcel boundaries and location information as a mediator, the system can accurately associate containers with customers even in dense urban areas where traditional direct observation methods fail, without requiring complex manual intervention.
2Measurement precision
If manual tracking methods are used, then the system is easy to operate, but service event tracking accuracy and billing precision deteriorate
Solution Approach 1:
The system implements self-service by enabling automatic container identification and service event tracking through the integration of RCV sensor data, location data, and parcel data. The automated correlation process eliminates the need for manual tracking interventions, achieving high service event tracking accuracy while maintaining ease of operation through system automation.
Solution Approach 2:
The system establishes a feedback loop where sensor data from RCVs continuously monitors container servicing events, correlates this data with location and parcel information, and automatically updates service records. This real-time feedback mechanism ensures accurate service event tracking and billing precision without requiring complex manual operational procedures.
3Adaptability or versatility
If container placement locations are flexible to accommodate customer needs, then customer service adaptability improves, but the difficulty of detecting and measuring container locations increases
Solution Approach 1:
The patent transitions from two-dimensional street-level container location tracking to three-dimensional spatial correlation by integrating parcel boundary data and vertical layering of data sources (RCV location, container position, parcel boundaries). This dimensional expansion enables the system to detect and measure container locations accurately regardless of flexible placement positions within parcel boundaries, resolving the contradiction between placement flexibility and detection difficulty.
Data Source
AI summary
Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.