Risk Information Processing System Using Correlation Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional risk information processing systems face challenges in calculating the degree of risk for a wide area where vehicles travel, as they require all risk factors and types to be sensed in all road sections and time zones, and fail to consider variations in risk based on individual drivers.
Innovation Solution
A risk information processing method that estimates the degree of risk for a combination by using three or more pieces of risk information stored in the system, including second, third, and fourth risk information, to calculate correlations between spots and situations, allowing for risk estimation without sensing data from all combinations, and stores individual and overall risk information to specify driver-specific risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If risk information is calculated for all combinations of spots and situations using conventional methods, then measurement precision of risk degree is improved, but device complexity and loss of time increase significantly
Solution Approach 1:
The patent applies partial action by calculating risk degrees only for necessary combinations of spots and situations rather than all possible combinations. The system selectively computes risk information based on actual vehicle locations and relevant situations, avoiding unnecessary calculations for combinations that will not be queried, thereby reducing computational complexity while maintaining adequate measurement precision.
Solution Approach 2:
The patent implements preliminary action by pre-calculating and storing risk degrees for all combinations of spots and situations in a database before actual risk queries are made. This pre-computation allows the system to quickly retrieve pre-stored risk information during operation without performing complex real-time calculations, thus reducing device complexity and response time.
2Reliability
If risk information is calculated for all combinations of spots and situations, then reliability of risk assessment is improved, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating risk degrees for all spot-situation combinations and storing them in advance. This allows the system to maintain high reliability by having complete risk data available while reducing calculation time during actual operations, as the system only needs to retrieve pre-computed values rather than perform complex real-time calculations.
Solution Approach 2:
The patent uses copying by creating a database that stores copies of risk degree values for all combinations of spots and situations. Instead of recalculating risk information each time it is needed, the system retrieves copied pre-computed values from storage, thereby maintaining assessment reliability while significantly reducing computation time.
3Ease of operation
If conventional risk information processing is used, then ease of operation is maintained, but adaptability to individual drivers deteriorates
Solution Approach 1:
The patent applies local quality by customizing risk information presentation for individual drivers based on their specific characteristics, driving history, and risk preferences. While the underlying risk calculation system remains standardized and easy to operate, the output and notification mechanisms are locally adapted to each driver's needs, providing personalized risk alerts without complicating the overall system operation.
Solution Approach 2:
The patent implements dynamics by making the risk information system adaptable to individual drivers through dynamic adjustment of notification thresholds, alert frequencies, and risk parameter weighting based on driver-specific data. This allows the system to maintain ease of operation while becoming versatile in accommodating different driver behaviors and risk tolerances.
Data Source
AI summary
A risk information processing method is used in a risk information processing system that manages a degree of risk at a target spot at which a moving object is located. The method includes: receiving sensor data collected at each of two spots different from the target spot; determining a degree of risk of an event at each of the two spots from the sensor data collected at each of the two spots; storing, as risk information the determined degree of risk associated with each of the two spots; estimating first risk information associated with the target spot without sensor data of first event at the target spot, by using the stored risk information, the first risk information being a degree of risk of the first event at the target spot; and outputting information for the target spot based on the estimated first risk information.