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<p>Considering the significant influence of weather conditions on heavy-duty vehicle (HDV) rollovers, this study explores a rollover warning method for HDVs subjected to crosswinds. Initially, a model to calculate the relative wind speed and direction in a crosswind environment is refined, and the formula for the lateral load-transfer ratio (LTR) is derived from vehicle rollover dynamics. Subsequently, rollover threshold boundaries for LTR are determined for 8<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11012_2025_2043_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation>4 axle HDVs, employing histograms and quartiles to refine the sensitivity of rollover indicator alarms. The risk of vehicle rollover is then quantified by integrating the weighted Mahalanobis distance(MD) with a first-order reliability method. Lastly, a probability model is utilized to analyze the impact of various random variables on the rollover outcomes. The findings affirm that the influence of side wind conditions on vehicle stability is considerable. Enhancements in vehicle rollover warning capabilities can be achieved through the application of the weighted, and the rollover risk can be effectively assessed using the probability model.</p>

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Probabilistic risk assessment for heavy duty vehicles rollover under crosswind environment

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Rui Guan,
  • Li Chang

摘要

Considering the significant influence of weather conditions on heavy-duty vehicle (HDV) rollovers, this study explores a rollover warning method for HDVs subjected to crosswinds. Initially, a model to calculate the relative wind speed and direction in a crosswind environment is refined, and the formula for the lateral load-transfer ratio (LTR) is derived from vehicle rollover dynamics. Subsequently, rollover threshold boundaries for LTR are determined for 8 \(\times\) × 4 axle HDVs, employing histograms and quartiles to refine the sensitivity of rollover indicator alarms. The risk of vehicle rollover is then quantified by integrating the weighted Mahalanobis distance(MD) with a first-order reliability method. Lastly, a probability model is utilized to analyze the impact of various random variables on the rollover outcomes. The findings affirm that the influence of side wind conditions on vehicle stability is considerable. Enhancements in vehicle rollover warning capabilities can be achieved through the application of the weighted, and the rollover risk can be effectively assessed using the probability model.