Stacking-Based Ensemble Learning Method for the Recognition of the Pedestrian Crossing Intention

Abstract

Accurate recognition of pedestrian crossing intentions is essential for the safe operation of autonomous vehicles on urban roads. However, the current pedestrian crossing intention recognition model has the problems of relatively low recognition accuracy and short recognition advance time. Based on the above problems, this paper carried out a study on the recognition model of pedestrian crossing intention. Firstly, the pedestrian and vehicle crossing data were collected through laser radar and a high-definition monitor, and 1980 groups of valid samples were selected. Secondly, the pedestrian crossing intention characterization parameter set was determined through statistical analysis. Finally, this paper proposes a pedestrian crossing intention recognition model based on stacking ensemble learning. The ensemble learning framework integrates random forest (RF), support vector machine (SVM), long short-term memory network (LSTM), an attention mechanism, and bidirectional LSTM (AT-Bi-LSTM). Compared with traditional machine learning methods, the proposed method shows greater advantages in recognition accuracy. The model recognition accuracy reaches 95.36% when the model is recognized at 0.5 s before crossing the zebra crossing, and the model recognition accuracy is 89.27% when the model is recognized at 1s before crossing the zebra crossing. The research in this paper is of great significance for building a more intelligent pedestrian-vehicle collaboration and promoting the industrial application of the autonomous vehicle.