Passenger Kinematics Variance in Different Vehicle Manoeuvres - Biomechanical Response Corridors Based on Principal Component Analysis

• This study explores the influence of occupant characteristics and belt type on occupant kinematics in evasive manoeuvres and provides models for construction of response corridors. Data originated from evasive manoeuvres with male and female volunteers. Principal component analysis and linear mixed models were used on selected data to create predictive models for kinematics and belt time histories, using belt configuration, sex, age, stature, and BMI as co-variates. Monte Carlo simulations of resulting models were used to generate upper and lower response corridor limits around the predicted responses. For translational and rotational displacements of the head and the torso, the first three principal components together captured 91%-99% of the variance in the responses. Belt configuration, sex, age, stature, BMI, and their interaction effects were found statistically significant (p < 0.05) in the linear mixed model analysis in lane changes, braking and U-turns at 40 km/h but not in U-turns at 30 km/h or when aware of turn. Response corridors for average sex, stature and BMI, were provided. In conclusion, the models and data provided can be used for validation of human body models with a range of anthropometries and in different manoeuvres and belt configurations potentially occurring in pre-crash manoeuvres.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Teknisk mekanik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Applied Mechanics (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Farkostteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Vehicle Engineering (hsv//eng)

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

occupant kinematics

volunteer response corridors

Evasive manoeuvres

linear mixed model

principal component analysis

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