Vehicle Load Prediction via External Object Classification
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Solution Overview
Problem
Existing vehicle weight measurement systems require physical sensors inside the vehicle and can only measure loads on a local portion, failing to accurately assess the overall load capacity for transporting objects, including passengers and cargo, which can lead to overloading and inefficient operations.
Innovation Solution
A method using vehicle sensors to detect and classify objects outside the vehicle, predicting the load based on classification, user data, and sensor data such as images, LIDAR, and weight sensors, allowing for the actuation of vehicle components to manage the load and communicate with users about capacity limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical sensors are placed inside the vehicle to measure load, then local load measurement is achieved, but the system cannot accurately assess overall load capacity for transporting objects
Solution Approach 1:
The system divides the load assessment task into multiple components: external object detection (identifying what is being transported), object classification (categorizing objects by type and estimated weight), and integration with vehicle sensor data. This segmentation allows the system to infer overall load capacity without requiring comprehensive physical sensors throughout the vehicle interior.
Solution Approach 2:
The system introduces an intermediary classification process that bridges the gap between external object detection and load measurement. By classifying objects based on visual characteristics and using this classification to estimate weight, the system mediates between what can be externally observed and what is needed for accurate load assessment.
2Ease of manufacture
If sensors measure load on a local portion of the vehicle, then sensor placement is simple, but the system fails to measure total vehicle load including passengers and cargo
Solution Approach 1:
The system makes the vehicle's sensor system multi-functional by using existing sensors (weight sensors, suspension sensors) for both their original purposes and for contributing to overall load assessment. Additionally, the external object detection system serves multiple functions: identifying objects, classifying them, and estimating their weights, all without requiring separate dedicated sensors for each function.
Solution Approach 2:
The system performs preliminary object detection and classification before the objects are loaded into the vehicle. This allows the system to know in advance what objects will be transported, their estimated weights, and categorize them accordingly, enabling proactive load management rather than reactive measurement after loading.
3Measurement precision
If multiple sensors are used to detect objects outside the vehicle, then object detection accuracy improves, but system complexity increases
Solution Approach 1:
The system merges multiple data sources into a unified object detection and classification framework. By combining data from weight sensors, suspension sensors, and external detection systems, the system achieves comprehensive load assessment through integration rather than through multiple independent complex subsystems. The classification system serves as a unifying layer that processes information from various sources.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate prediction of vehicle load capacity, preventing overloading and optimizing vehicle operations by integrating external object detection and classification with user data for informed decision-making.
Implementation Method 1
vehicle sensors to detect and classify objects outside the vehicle, predicting the load based on classification, user data, and sensor data such as images, LIDAR, and weight sensors
Data Source
AI summary
An object outside of a vehicle can be detected with vehicle sensor data. A load for the vehicle can be predicted at least in part based on classifying the object. A vehicle component can be actuated based on the predicted load.