A method for predicting the
rollover risk of a
truck on a sand-accumulated
road surface based on
machine learning comprises the steps that a human-vehicle-road closed-loop
simulation model is constructed, vehicle operation parameters serve as input of the closed-loop
simulation model, lateral acceleration and a transverse load transfer rate are output, the vehicle operation parameters serve as a first
original data set, and the lateral acceleration and the transverse load transfer rate serve as a second
original data set; taking the vehicle operation parameters, the lateral acceleration and the transverse load transfer rate as a second
original data set; normalizing, coding and dimensionally expanding the first and second original data sets respectively; constructing a Transform-LSTM (
Long Short Term Memory) regression model, and training the Transform-LSTM regression model to obtain a risk regression model; inputting the first original
data set and the second original
data set into a clustering analysis model, performing clustering after determining an optimal clustering number, and outputting a
risk classification result; and constructing a Transform-LSTM (
Long Short Term Memory) classification model, and training the Transform-LSTM classification model to obtain a risk prediction model. According to the method, the factor of road sand accumulation in the existing risk prediction field is supplemented, the heavy
truck rollover prediction method in the severe environment is enriched, and the accident rate can be effectively reduced.