Vehicle Cargo Load Profiling for Balanced Weight Distribution
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Solution Overview
Problem
Existing vehicle load weight determination systems fail to effectively balance loads within vehicles, leading to instability and uneven tire wear due to inadequate weight distribution guidance, especially when loads approach or exceed maximum weight limits.
Innovation Solution
A system that virtually divides the cargo area into cells, using machine learning to create a load profile based on sensor data from weight sensors, tire pressure, inertial measurement units, and cameras, providing guidance through visual, auditory, or haptic means to balance the load and prevent overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor types are used to determine load weight, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (weight sensors, tire pressure sensors, inertial measurement units, suspension sensors) into a unified load determination system. The controller integrates data from all these sensors to create a comprehensive load profile, merging their functions to achieve high measurement precision while managing system complexity through centralized processing.
Solution Approach 2:
The controller serves multiple functions: it processes data from various sensor types, creates load profiles, determines weight distribution, and provides guidance to the driver. This multi-functionality allows the system to achieve high measurement precision without proportionally increasing device complexity, as a single component performs multiple critical tasks.
2Ease of operation
If the cargo area is divided into multiple virtual cells, then load distribution guidance is improved, but device complexity increases
Solution Approach 1:
The cargo area is virtually divided into multiple cells or zones within the software. Each cell can independently track load weight and distribution, enabling detailed guidance on where to place or move items. This segmentation approach improves ease of operation by providing specific, actionable guidance rather than general advice, while the virtual nature of the division keeps hardware complexity low.
Solution Approach 2:
The system creates a virtual model (copy) of the cargo area with its cells and load distribution. This digital representation allows the controller to analyze and provide guidance without physically dividing the cargo space. The virtual copy enables complex load management functionality while maintaining simple physical system architecture.
3Stability of the object's composition
If real-time load monitoring is implemented, then vehicle stability is improved, but use of energy increases
Solution Approach 1:
The system implements load monitoring at periodic intervals rather than continuously. The controller checks load weight and distribution at scheduled times or at key events (such as when the vehicle is stationary or at regular time intervals). This periodic approach maintains vehicle stability through timely monitoring while significantly reducing energy consumption compared to continuous real-time monitoring.
Data Source
AI summary
A method includes virtually dividing a cargo area of a vehicle into a plurality of virtual cells. The method also includes creating, by implementing machine learning, a load profile for the cargo area. The load profile indicates a weight estimate at each of the plurality of virtual cells based on a load in the cargo area. Guidance on moving the load within the cargo area is provided based on the load profile to balance the load in the cargo area.