Road Surface Estimation From Multi-Vehicle State Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in easily estimating road surface state information from vehicle state information, particularly when vehicles without smartphones travel on roads, and in accurately determining road surface abnormalities.
Innovation Solution
An information processing apparatus that acquires vehicle state information from multiple vehicles and uses a trained machine learning model to estimate road surface state information, including road surface roughness and international roughness index (IRI), by associating vehicle state information with corresponding road surface state information in training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex processing methods are used to determine road state abnormalities (comparing maximum wheel speed change rate and avoidance behavior ratio against threshold values), then the accuracy of road state detection is improved, but the complexity of the system increases and the ease of estimation deteriorates
Solution Approach 1:
The patent transforms the road state estimation problem from complex multi-parameter threshold comparison to a simplified parameter transformation process. By pre-calculating wheel speed change rates and storing them in a lookup table, the system changes the parameter representation from raw sensor data to pre-processed statistical values, enabling accurate road state detection through simple comparison operations rather than complex real-time calculations
Solution Approach 2:
The patent applies preliminary action by pre-calculating and storing wheel speed change rates in a lookup table before actual road state estimation is needed. This pre-processing step eliminates the need for complex real-time computations during operation, allowing the system to achieve high detection accuracy through simple table lookups and threshold comparisons, thereby reducing processing complexity while maintaining measurement precision
2Ease of operation
If smartphone-based measurement systems are used to collect vehicle state information, then the ease of data collection is improved, but the coverage of road surface state information deteriorates because vehicles without smartphones cannot contribute data
Solution Approach 1:
The patent applies universality by designing a road state estimation system that works with any vehicle equipped with wheel speed sensors, regardless of whether it has a smartphone. By using universal vehicle parameters (wheel speed, vehicle type, load condition) that can be obtained from standard vehicle sensors, the system enables both smartphone-equipped and non-smartphone vehicles to contribute road surface state information, thereby expanding information coverage while maintaining ease of operation through standardized data collection methods
3Measurement precision
If multiple vehicles' state information is aggregated to improve road surface state estimation accuracy, then the measurement precision is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent applies parameter changes by transforming individual vehicle state information into standardized wheel speed change rate parameters that can be directly compared and aggregated. By normalizing the data representation and using pre-calculated parameters stored in lookup tables, the system enables efficient aggregation of multiple vehicle measurements without requiring complex data processing, thereby improving measurement precision while controlling device complexity
Solution Approach 2:
The patent applies copying by using pre-calculated wheel speed change rates stored in lookup tables that represent typical vehicle responses to various road conditions. Instead of processing raw sensor data from each vehicle in real-time, the system copies and compares pre-computed parameter values, enabling efficient aggregation of multiple vehicle measurements to improve estimation accuracy while minimizing processing complexity
Data Source
AI summary
An information processing apparatus acquires vehicle state information for each of a plurality of vehicles. The information processing apparatus estimates road surface state information of a road surface on which each of the vehicles has traveled, based on the acquired vehicle state information for each of the vehicles. The information processing apparatus estimates the road surface state information by inputting the acquired vehicle state information to a trained model that outputs the road surface state information in a case where the vehicle state information is input and that has been trained in advance based on training data in which the vehicle state information and the road surface state information are associated with each other.