Estimation of Safety Margin Based on the Personality Traits Aiming at the Personalized Driving Assistance System

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Key Points

This research aims to predict the safety margin of drivers based on their personality traits without using driving data.
Conducted experiments using a driving simulator to collect data during lane changes.
Calculated a risk level metric that reflects each driver's acceptable risk.
Developed a predictive model using partial least squares regression with personality traits as predictors.
The predictive model showed high accuracy without needing personalized driving data.
The approach suggested effectiveness in adapting driving assistance systems for individuals.
It indicated potential improvements over existing methods that require extensive data collection.

Analysis demonstrates effective safety margin prediction in personalized driving assistance systems, highlighting individual traits.

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Yuki Mekata (2026) studied this question.

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