Vehicle-Road Interaction Modeling for Real-Time Road Condition Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods fail to predict vehicle-road interaction effects intelligently prior to their occurrence, leading to issues like occupant discomfort, vehicle performance deterioration, and safety concerns due to inadequate real-time road condition assessment.
Innovation Solution
A method involving sensor-equipped vehicles to collect data, preprocess it, and develop a model to detect road abnormalities, creating a database for predictive road condition analysis using neural networks and absolute running means.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data collection and model development are implemented, then road condition assessment capability is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the road condition monitoring system into multiple independent components: sensor data collection module, data preprocessing module, model training module, and road condition assessment module. Each component handles a specific aspect of the overall process, reducing the complexity of any single component while maintaining comprehensive functionality.
Solution Approach 2:
The patent implements preliminary action by collecting and preprocessing sensor data during controlled vehicle operations before actual road condition assessment is needed. A reference data set is created in advance through controlled operations, and a neural network model is trained beforehand to enable rapid real-time assessment without requiring complex processing during critical evaluation moments.
2Reliability
If real-time data collection and analysis are performed, then road condition prediction capability is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data preprocessing and model training during controlled operations before real-time assessment is required. The neural network model is trained in advance using preprocessed reference data, enabling rapid inference during actual road condition monitoring without requiring complex real-time processing.
Solution Approach 2:
The system uses self-service by automatically preprocessing sensor data and training the neural network model without requiring manual intervention. The model learns from the reference data set independently, and the system automatically performs data cleaning, normalization, and feature extraction, reducing the need for external computational resources during critical assessment periods.
3Measurement precision
If comprehensive sensor data is collected under controlled conditions, then model accuracy is improved, but data collection complexity and operational requirements increase
Solution Approach 1:
The patent applies local quality by implementing controlled conditions specifically during the data collection phase to ensure high-quality reference data, while allowing normal operating conditions during actual road condition assessment. The controlled conditions are applied locally to the training data collection process rather than throughout the entire system operation, maintaining ease of operation for the majority of system usage.
Data Source
AI summary
A method of developing a road conditioning monitoring model is disclosed. The method comprises utilizing vehicle sensors to collect data under controlled conditions over a road surface to develop a training data set. The training data set undergoes a data analysis to determine an absolute running mean associated with the first road surface under controlled conditions. Abnormalities in the road surface are detected by comparing the absolute running mean with the training data set. The detected abnormalities are classified to create road surface classification rules. A method of developing a road condition database is also disclosed, that comprises utilizing vehicle sensors during operation of a vehicle to collect data during the operation and feeding the data into the road conditioning monitoring model to classify the road conditions on which the vehicle is driven. A system for determining road condition impact on a vehicle is also disclosed.