Road Surface Evaluation Apparatus Dynamic Data Collection
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Solution Overview
Problem
Conventional road surface evaluation apparatuses face increased data load and communication infrastructure pressure as the number of vehicles increases, necessitating a more efficient method to evaluate road surface roughness without compromising accuracy.
Innovation Solution
A road surface evaluation apparatus that includes a microprocessor to acquire and process driving information from multiple vehicles, calculating roughness values and adjusting the data collection based on change rate thresholds, thereby optimizing data usage and reducing load on the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If driving information from a larger number of vehicles is collected to improve evaluation accuracy, then measurement precision improves, but the quantity of data and device complexity increase
Solution Approach 1:
The system applies different data collection strategies to different road sections based on their specific characteristics. For sections with large roughness changes, more driving information is collected from multiple vehicles. For sections with stable roughness, less data is collected. This localized approach maintains evaluation accuracy where needed while reducing overall data volume.
Solution Approach 2:
Instead of uniformly collecting data from all vehicles across all road sections, the system collects excessive data (from multiple vehicles) only for specific sections where roughness changes are detected, and uses partial data (from fewer vehicles) for stable sections. This selective data collection optimizes the balance between accuracy and data volume.
2Measurement precision
If driving information from more vehicles is collected to improve evaluation accuracy, then measurement precision improves, but the load on communication infrastructure worsens
Solution Approach 1:
The system implements variable data collection intensity across different road sections. High-resolution data collection (multiple vehicles) is applied only to sections with significant roughness changes, while low-resolution collection (fewer vehicles) is used for stable sections. This reduces the overall communication load while maintaining accuracy where critical.
Solution Approach 2:
The system uses partial action by collecting data from a subset of vehicles for most road sections, and excessive action by collecting data from many vehicles only for critical sections with large roughness changes. This dynamic approach optimizes communication infrastructure utilization.
3Measurement precision
If more driving information is acquired to improve evaluation accuracy for sections with large roughness changes, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system applies intensive data collection and processing only to specific road sections where roughness changes are detected, rather than uniformly processing all sections. This localized intensive processing improves accuracy for critical sections while minimizing time loss for stable sections.
Solution Approach 2:
The system uses partial action for time-efficient processing on stable road sections and excessive action (collecting more driving information) only for sections with large roughness changes. This selective approach balances accuracy requirements with processing time constraints.
Data Source
AI summary
A road surface evaluation apparatus includes a microprocessor is configured to perform: calculating a roughness value of road surface corresponding to a predetermined period based on driving information of the plurality of vehicles during the predetermined period; and outputting roughness information including the roughness value. The microprocessor is configured to further perform, when calculating the roughness value corresponding to a second predetermined period, estimating whether a magnitude of a change rate of the roughness value corresponding to the second predetermined period with respect to the roughness value corresponding to a first predetermined period exceeds a predetermined threshold to acquire, when the magnitude is estimated to exceed the predetermined threshold, more pieces of the driving information to be used for calculating the roughness value corresponding to the second predetermined period than when the magnitude is estimated to be equal to or less than the predetermined threshold.


