Vehicle Driving Support Control Based on Driver Acceptability
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Solution Overview
Problem
Existing driving support systems fail to effectively address potential risks and may be perceived as unnecessary by drivers, leading to reduced effectiveness in preventing accidents.
Innovation Solution
A method and device that learn a driver's behavior tendency and estimate their acceptability for driving support control, executing the control only when conditions are met to avoid perception of unnecessary processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving support control is executed based on learning data for all obstacles, then accident prevention is improved, but driver acceptance deteriorates due to perception of unnecessary processing
Solution Approach 1:
The system changes the parameter of risk degree classification, dividing obstacles into multiple risk degrees (first risk degree for high risk, second risk degree for low risk) based on learning data. This allows the driving support control to be selectively executed only for high-risk obstacles, improving driver acceptance while maintaining accident prevention effectiveness.
2Reliability
If driving support control is executed for potential risks, then safety is improved, but driver acceptance deteriorates due to perception of unnecessary processing
Solution Approach 1:
The system changes the parameter of risk classification by distinguishing between apparent risks and potential risks, and further dividing them into different risk degrees. By executing driving support control selectively based on these classifications, the system maintains safety while reducing unnecessary interventions that lower driver acceptance.
3Reliability
If driving support control is executed frequently, then accident prevention is improved, but driver acceptance deteriorates leading to avoidance settings
Solution Approach 1:
The system applies partial action by executing driving support control only for obstacles classified as high risk (first risk degree) rather than for all obstacles. This selective execution reduces the frequency of intervention, preventing driver frustration and avoidance settings while maintaining adequate accident prevention.
Data Source
AI summary
Information about the driving environment of the vehicle and information about factors influencing the driving behavior of the vehicle by the driver of the vehicle are obtained. A tendency of the driver's driving behavior is learned based on the information about the driving environment and the information about the factors. Based on the learned tendency of driving behavior, the driver's receptivity to the execution of driving support control of the vehicle is estimated. The driving support control is executed according to the estimated acceptability when the conditions for executing the driving support control are satisfied.