Driving Ability Evaluation from Steering Data in High-Load Sections
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing driving ability evaluation systems require applying a load to the driver, which hinders normal driving and is inconvenient.
Innovation Solution
A system that calculates driving ability evaluation values based on travel data, specifically using α and Hp values derived from steering characteristics in different sections of vehicle travel, without requiring a load on the driver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a load is applied to the driver to evaluate safe driving ability, then the evaluation accuracy is improved, but the ease of operation deteriorates because normal driving is hindered
Solution Approach 1:
The system performs preliminary classification of travel sections into high-load and low-load sections based on road gradient, curvature, and traffic conditions. This allows the evaluation to be conducted using pre-identified sections that naturally provide the required driving load, eliminating the need to apply artificial loads during evaluation.
Solution Approach 2:
The invention extracts only the necessary travel data from specific high-load sections of the journey. By selecting and isolating relevant sections where the driver naturally experiences sufficient load, the system achieves accurate evaluation without requiring the driver to perform additional maneuvers or experience artificial load applications.
2Quantity of substance
If travel data from all sections is used for evaluation, then the quantity of data is increased, but the manufacturing precision deteriorates due to inclusion of low-quality data from low-load sections
Solution Approach 1:
The travel journey is segmented into multiple sections based on road gradient, curvature, and traffic conditions. Each section is evaluated independently, and only sections classified as high-load contribute to the final evaluation. This segmentation allows the system to process large quantities of travel data while maintaining high precision by excluding low-quality data from low-load sections.
Solution Approach 2:
Different quality requirements are applied to different sections of travel data. High-load sections are identified and given higher weight in the evaluation, while low-load sections are excluded or given minimal weight. This local quality approach ensures that the overall evaluation precision is maintained by focusing on high-quality data segments.
Data Source
AI summary
A driving ability determination system, includes: an acquisition unit configured to acquire travel data of a vehicle; a first calculation unit configured to calculate a first evaluation value indicating characteristics of steering by a driver of the vehicle based on the travel data acquired by the acquisition unit; and a second calculation unit configured to calculate a second evaluation value indicating characteristics of steering by the driver of the vehicle based on travel data in a first section among pieces of the travel data acquired by the acquisition unit. The first calculation unit calculates the first evaluation value based on the travel data in the first section and travel data in a second section different from the first section among pieces of the travel data acquired by the acquisition unit.