Cargo Load Center-of-Gravity Estimation for Vehicle Stability
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Solution Overview
Problem
Existing vehicles lack effective systems to accurately estimate the load distribution and center of gravity of cargo, which is crucial for managing cargo loads and ensuring vehicle stability.
Innovation Solution
A vehicle equipped with a load sensing system and object recognition system, including ride height load sensors and imaging devices, processes images to determine the dimensions and center of gravity of cargo loads using a parallel-axis theorem, enabling precise estimation of the vehicle's new center of gravity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a load sensing system is used to detect vehicle load, then load detection capability is improved, but the system cannot accurately determine cargo load distribution and center of gravity
Solution Approach 1:
The patent combines the load sensing system with an object recognition system that uses imaging devices. By merging these two systems, the vehicle can both detect overall load weight and visually identify cargo objects, their positions, and dimensions, thereby recovering the lost distribution information that the load sensing system alone cannot provide.
Solution Approach 2:
The object recognition system acts as an intermediary between the load sensing system and the controller. It processes images to identify cargo objects and their spatial characteristics, then provides this information to the controller which combines it with load sensing data to calculate accurate center of gravity and load distribution.
2Measurement precision
If imaging devices are added to detect cargo objects, then cargo load distribution estimation is improved, but device complexity increases
Solution Approach 1:
The imaging devices serve multiple functions: they detect cargo objects, determine their dimensions, identify their positions in the cargo area, and provide visual data for center of gravity calculation. This multi-functionality reduces the need for separate specialized sensors for each measurement task, thereby limiting the increase in device complexity.
Solution Approach 2:
The object recognition system uses the imaging devices to automatically identify and characterize cargo objects without requiring manual input or additional specialized sensors. The system processes the visual information itself to extract all necessary geometric and positional data, making the added imaging capability self-sufficient rather than requiring further complex supporting systems.
3Stability of the object's composition
If the vehicle lacks load distribution detection, then vehicle stability cannot be managed, but adding detection systems increases device complexity
Solution Approach 1:
The detection system is segmented into two functional parts: a load sensing system for overall weight detection and an object recognition system for spatial distribution analysis. This segmentation allows each component to be relatively simple while their combined output provides comprehensive load distribution information needed for vehicle stability management.
Data Source
AI summary
A vehicle is provided including a plurality of wheel assemblies, a body supported on the plurality of wheel assemblies and having a cargo area for receiving a cargo load, a load sensing system configured to sense a vehicle load and generate vehicle load signals indicative of the sensed vehicle load, and an object recognition system configured to detect one or more objects in the cargo area and recognize the one or more objects. The vehicle also includes a controller processing the recognized one or more objects and determining dimensions of the cargo load and an estimated center of gravity of cargo load based on the dimensions of the one or more objects in the cargo area, and generates an output indicative of the estimated center of gravity of the cargo load.