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

Table 4

Model evaluation result at 0.5 s before crossing the zebra crossing.

Algorithm	Accuracy (%)	WSI			WWI			SWI
		Pr (%)	Re (%)	F1 (%)	Pr (%)	Re (%)	F1 (%)	Pr (%)	Re (%)	F1 (%)
SVM	85.26	84.76	84.24	84.50	88.75	87.12	87.93	82.35	84.34	83.33
RF	87.07	86.59	86.59	86.59	90.63	88.41	89.51	84.12	86.14	85.12
LSTM	89.30	90.24	88.62	89.43	91.88	90.74	91.30	87.06	88.62	87.83
AT-Bi-LSTM	92.12	92.07	91.52	91.79	93.75	93.17	93.46	90.59	91.67	91.12
Stacking	95.36	95.12	94.55	94.83	96.88	96.27	96.57	94.12	95.24	94.67

Note. Pr represents precision, Re represents recall, and F1 represents F1 scores.