Vehicle Weight Estimation From Operational Maneuvers Without Load Sensors
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Solution Overview
Problem
Existing methods for determining a vehicle's weight are often inaccessible, difficult, and expensive, particularly when trying to estimate weight during operation or in real-time without the use of external weighing devices or vehicle-installed load sensors.
Innovation Solution
A method using at least one processor to receive vehicle data associated with the vehicle, identify vehicle maneuvers, and employ a machine learning model trained with previous vehicle maneuvers to estimate the vehicle's weight based on the collected data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external weighing devices such as truck scales or weighbridges are used to measure vehicle weight, then measurement precision is improved, but ease of operation deteriorates because these devices are not always accessible and cannot measure weight during vehicle operation
Solution Approach 1:
The patent replaces mechanical weighing devices (truck scales, weighbridges) with a computational system that uses machine learning models to estimate vehicle weight. The system processes vehicle data including geospatial information, engine parameters, and maneuver characteristics to predict weight without physical contact with the vehicle, enabling measurements during operation and improving accessibility.
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a mediator between vehicle operations and weight determination. Instead of directly weighing the vehicle, the system uses intermediate data points (engine data, geospatial data, maneuver data) processed through machine learning models to infer weight, enabling indirect measurement during vehicle operation.
2Measurement precision
If load sensors are installed in the vehicle to measure weight, then measurement precision is improved, but device complexity and installation difficulty increase
Solution Approach 1:
The patent extracts the weight measurement function from physical sensors installed in the vehicle and relocates it to an external computational system. By removing the need for installed load sensors and using only externally collected vehicle data processed through machine learning, the system eliminates installation complexity while maintaining measurement capability.
Solution Approach 2:
The patent enables the vehicle to effectively measure its own weight using its existing operational data (engine parameters, geospatial information, maneuver characteristics) without requiring external sensing equipment. The vehicle's normal operational data serves the dual purpose of vehicle control and weight estimation, eliminating the need for additional sensors.
3Measurement precision
If load sensors are installed in the vehicle to measure weight, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
The patent replaces expensive, permanent load sensor installations with a cost-effective computational approach using existing vehicle data. The system uses readily available data from vehicle operations processed through machine learning models, eliminating the need for costly sensor hardware and installation while maintaining measurement capability.
Data Source
AI summary
Disclosed herein are methods for determining an estimated weight of a vehicle. The methods comprise operating at least one processor to: receive vehicle data associated with the vehicle, the vehicle data comprising a plurality of vehicle parameters collected during operation of the vehicle; identify one or more vehicle maneuvers based on the vehicle data, each vehicle maneuver being associated with a portion of the vehicle data; and use at least one machine learning model to determine the estimated weight of the vehicle based on the portion of the vehicle data associated with each of the one or more vehicle maneuvers, the at least one machine learning model trained using training data associated with a plurality of previous vehicle maneuvers. Also disclosed are systems for implementing methods of the present disclosure.