In the realm of transportation science, the advent of deep learning has propelled
advancements in predicting longitudinal driving behavior. This study explores
the application of deep neural network architectures, specifically
long–short-term memory (LSTM) and convolutional neural networks (CNNs),
recognized for their effectiveness in handling sequential data. Using a 3-s
temporal window that includes past vehicle progress, speed, and acceleration,
the proposed model, a hybrid LSTM–CNN architecture, predicts the vehicle’s speed
and progress for the next 6 s. The approach achieves state-of-the-art
performance, particularly within a 4 s horizon, but remains competitive even for
longer-term predictions. This is achieved despite the simplicity of its input
space, which does not include information about vehicles other than the target
vehicle. As a result, while its performance may decrease slightly for
longer-term predictions due to the lack of environmental information, it still
offers reliable predictions and can be applied effectively in scenarios with
partial observability. The comparative analysis of multilayer perceptron (MLP),
LSTM, and one-dimensional CNN architectures highlights the challenges faced by
MLP in capturing the complex nonlinearity of driving behavior. LSTM and CNN
demonstrate superior performance, with model complexity influencing outcomes. No
statistically significant difference is observed in the performance between LSTM
and CNN models.
