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Accid Anal Prev ; 200: 107540, 2024 Jun.
Article in English | MEDLINE | ID: mdl-38479204

ABSTRACT

As the detrimental impact of the commonly recommended centered driving mode for autonomous trucks on road longevity is gaining attention, more lateral control modes are being proposed to enhance road sustainability. However, there is currently a lack of research on the lateral safety analysis of autonomous trucks with different lateral control modes, especially in complex driving scenarios (such as overtaking) and adverse weather conditions. Therefore, this study developed a safety assessment framework to comparatively analyze the risk probability differences in lateral accidents during overtaking maneuvers by autonomous trucks with different lateral control modes under adverse weather conditions. Based on aerodynamics and vehicle dynamics simulations to capture the multifactorial influences on truck lateral deviation, the results are used for model validation and training. In the reliability approach, Support Vector Machine Regression (SVR) is introduced to establish the SVR response surface model with optimal predictive performance, and combined with Monte Carlo simulations for safety assessment, quantifying safety indices. The results indicate that trucks being overtaken during overtaking maneuvers are more prone to lateral accidents under crosswind influences. The overall impact of lateral control modes on the lateral safety trends is minor. Compared to other lateral control modes, following the centered zero-drift mode is generally safer. However, in conditions of low wind speeds (below 20 km/h) or on highly slippery road surfaces (road friction coefficient below 0.1), autonomous trucks following a uniform distribution mode can better maintain a low-risk level. This study provides crucial insights for future considerations integrating road longevity and truck safety in a collaborative manner, and the proposed methodology has broad applications.


Subject(s)
Accidents, Traffic , Automobile Driving , Humans , Accidents, Traffic/prevention & control , Reproducibility of Results , Motor Vehicles , Weather
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