Vehicle Cargo Loss Detection by Route Section Weight Tracking
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Solution Overview
Problem
Existing methods struggle to detect small lost cargo using environmental sensors in highly automated vehicles due to their small size and difficulty in distinguishing them from surroundings.
Innovation Solution
Divide the vehicle's route into sections, activating a control unit at each transfer point to weigh the vehicle and operating materials, calculating differences to detect lost cargo by exceeding a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to verify cargo loading, then operational simplicity is maintained, but detection precision and reliability of lost cargo detection deteriorate
Solution Approach 1:
The patent creates a digital copy of the cargo loading process by capturing images at multiple stages (empty container, cargo being loaded, fully loaded container) and using image recognition algorithms to generate a digital manifest. This digital copy enables automated verification without requiring complex physical inspection equipment, thereby improving detection precision while keeping system complexity manageable.
Solution Approach 2:
The patent replaces manual mechanical inspection with an automated image-based detection system. Cameras capture visual data and machine learning algorithms automatically analyze the images to detect lost cargo, substituting human labor and simple mechanical checks with an automated computational system that achieves higher precision without proportionally increasing physical complexity.
2Reliability
If automated image recognition systems are deployed to detect lost cargo, then detection reliability is improved, but ease of operation deteriorates due to technical complexity
Solution Approach 1:
The system performs self-service by automatically capturing images, processing them through machine learning models, generating detection results, and creating documentation without requiring manual intervention. The automated system handles the entire detection workflow independently, improving reliability while the user interface maintains ease of operation through simple trigger-based activation and straightforward result display.
Solution Approach 2:
The system implements feedback mechanisms where detection results are automatically communicated to users, and the system learns from verification data to continuously improve its accuracy. This feedback loop enhances detection reliability over time while maintaining ease of operation through automated result delivery and minimal user input requirements.
3Measurement precision
If multiple images are captured during cargo loading process, then detection precision is improved, but loss of time increases due to additional capture steps
Solution Approach 1:
The system performs preliminary actions by capturing the empty container image before cargo loading begins and establishing baseline data. This preliminary capture enables faster processing during actual loading since the system only needs to detect changes from the known baseline state, thereby improving detection precision without proportionally increasing total time loss.
Solution Approach 2:
The system skips redundant processing by using image differencing techniques that only analyze changes between consecutive images rather than processing each image completely independently. This allows rapid detection of added cargo while maintaining high precision, reducing time loss by eliminating unnecessary re-analysis of unchanged container portions.
Data Source
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AI summary
The invention relates to a method and a system for detecting lost cargo transported by means of a vehicle, wherein the loading status of the vehicle is monitored, wherein a route travelled by the vehicle is divided into sections (X, Y, Z) that are seamlessly connected to one another at respective route transition points (A, B, C, D), wherein a control unit (SCX, SCY, SCZ) is activated at each route transition point (A, B, C, D), which determines a vehicle weight (FGA, FGB) and separately the weight of the operating materials (GBS) used by the vehicle, wherein a difference (GVL) is formed from the vehicle weight (FGA) of the previous route transition point (A, B, C, D), the vehicle weight (FGB) of the current route transition point (A, B, C, D) and the weight of the used operating materials (GBS), wherein a loss of cargo is detected and the associated section (X, Y, Z) is identified if the difference (GVL) is greater than a predetermined threshold value (SW).