Vehicle speed prediction method and system considering dynamic traffic environment in car-following scenarios

A system for predicting vehicle speed considering a dynamic traffic environment in a car-following scenario, comprising:

• a car-following case data extraction module;
• a feature fusion and normalization processing module;
• a redundant variable removal module;
• and
• a vehicle speed prediction model training and prediction output module;
• wherein the car-following case data extraction module is configured for extracting car-following case data by setting a standard and a threshold for a car-following process in a naturalistic driving dataset;
• the feature fusion and normalization processing module is configured for obtaining a traffic environment feature related to the car-following case data by using a target detection algorithm, and extracting a vehicle dynamics parameter from the car-following case data, fusing the vehicle dynamics parameter with the traffic environment feature, and performing normalization processing to obtain a fused feature;
• the redundant variable removal module is configured for removing a redundant variable from the fused feature by using a combined feature selection method;
• and
• the vehicle speed prediction model training and prediction output module is configured for dividing the fused feature after removal of the redundant variable into datasets, training a pre-established Informer-FDR vehicle speed prediction model, and obtaining a vehicle speed prediction result by using a trained Informer-FDR vehicle speed prediction model;
• and
• wherein the pre-established Informer-FDR vehicle speed prediction model is configured to replace a max pooling layer of a distillation layer in a decoder of an Informer deep learning model with an average pooling layer, and change a convolutional layer to dilated causal convolution;
• the dilated causal convolution consists of causal convolution and dilated convolution;
• the causal convolution is subject to time constraint limitations, convolving an output at time t only with elements at time t and earlier in a previous layer to ensure causality in time series prediction, so that network output information is only influenced by past input information;
• an operation of the dilated convolution is expressed as:
• F
• ⁡
• (
• s
• )
• =
• ∑
• i
• =
• 0
• K
• -
• 1
• F
• i
• *
• X
• t
• -
• d
• ·
• i
• ;
• wherein K is a size of a convolution kernel, s is an element of X, Fi is a weight of the convolution kernel, t is a time step, and t−d·i denotes a past direction;
• the pre-established Informer-FDR vehicle speed prediction model is configured to introduce PReLU as an activation function of the distillation layer, PReLU shifts an output mean toward zero, and an improved distillation operation is as follows:
• X
• j
• +
• 1
• t
• =
• AvgPool
• ⁢
• (
• P
• ⁢
• Re
• ⁢
• LU
• ⁢
• (
• DCConv
• ⁡
• (
• [
• X
• j
• t
• ]
• A
• ⁢
• B
• )
• )
• )
• ;
• wherein ProbSparse self-attention is contained in [⋅]AB;
• the dilated causal convolution is implemented through DCConv(⋅);
• PReLU(⋅) represents the activation function, and a final output is generated through AvgPool(⋅);
• and
• the pre-established Informer-FDR vehicle speed prediction model is configured to incorporate a residual connection module;
• the residual connection module consists of three ProbSparse self-attention layers and two distillation layers, and three ProbSparse self-attention layers are a first ProbSparse self-attention layer, a second ProbSparse self-attention layer, a third ProbSparse self-attention layer;
• to ensure that feature maps output by the first ProbSparse self-attention layer and the third ProbSparse self-attention layer have the same shape when added together, a 1×1 convolution is used to merge an output of the first ProbSparse self-attention layer with an output of the third ProbSparse self-attention layer in a main encoder, ensuring effective transmission of feature information.