As autonomous driving advances towards real-world implementation, accurately predicting the behavior of traffic participants remains a critical challenge for ensuring both safety and efficiency in driving. This study emphasizes the use of convolutional neural networks (CNN), employing long short-term memory (LSTM) and multilayer perceptron (MLP) as comparative models to investigate deep learning applications in behavior prediction. Utilizing Kaggle’s “predictionforᵥalidationdata. csv” dataset, the research develops a predictive system following data cleaning, feature extraction, and oversampling procedures. Experimental results indicate that CNN effectively extracts visual features through its convolution-pooling architecture, achieving an accuracy of 94. 12% along with a perfect ROC-AUC score. In low-dimensional scenarios, MLP matches CNN’s accuracy by leveraging multilayer nonlinear transformations; however, it lacks depth in feature representation. Conversely, LSTM demonstrates limited performance due to its reliance on minimal temporal features, attaining only 52. 94% accuracy—underscoring its dependence on rich temporal data. This study underscores the suitability of CNN for spatial contexts, highlights MLP’s efficiency in low-dimensional settings, and points out LSTM’s requirement for datasets abundant in temporal information. Future research may focus on optimizing CNN architectures, exploring cross-model fusion techniques, and developing multisource datasets to enhance predictive capabilities.
Zhao et al. (Thu,) studied this question.