针对疲劳驾驶难以准确检测和检测模型可解释性低的问题，提出了一种可解释的麻雀优化随机森林模型（SSA-RFC-SHAP）用于驾驶疲劳检测。以驾驶员脉搏波信号为数据源，进行心率变异性分析并提取特征指标；通过皮尔逊相关性检验和卡方独立性检验筛选出用于驾驶疲劳程度判别的特征指标集；通过麻雀算法对随机森林分类器进行优化并建立驾驶疲劳三分类检测模型；最后利用夏普利加性解释算法对模型检测结果进行可解释性分析。结果表明：提出的SSA-RFC-SHAP模型在驾驶疲劳三分类检测任务中，准确率、精确率、召回率和F₁分别达到90.52%、90.34%、90.16%、90.24%，高于RFC、BiLSTM、CNN-LSTM和Gradient Boosting模型；在模型的可解释性方面，得到了各特征对模型预测的影响以及模型的具体决策过程，其中MeanHR与疲劳状态存在负相关关系，MedianNN和LF与疲劳状态存在正相关关系。可见提出的SSA-RFC-SHAP驾驶疲劳检测模型可为驾驶疲劳预警提供科学指导。

Aiming at the problem that fatigue driving is difficult to be detected accurately and the interpretability of the detection model is low, an interpretable sparrow search algorithm optimized random forest model (SSA-RFC-SHAP) was proposed for driving fatigue detection. The driver's pulse wave signal was used as the data source, the heart rate variability analysis was performed and the feature indicators were extracted. The Pearson's correlation test and chi-square independence test were used to screen out the feature indicators for the determination of driving fatigue. The Random Forest classifier was optimized by the sparrow algorithm, and the driving fatigue three-classification detection model was established. And finally, Shapley's additive explanation algorithm was used for the interpretation of the model detection results. Finally, Shapley's additive explanation algorithm was used to analyze the interpretability of the model detection results. The results show that: the proposed SSA-RFC-SHAP model has high accuracy, and in the driving fatigue three-classification detection task, the accuracy, precision, recall, and F₁ reach 90.52%, 90.34%, 90.16%, and 90.24%, respectively, which is higher than that of RFC, BiLSTM, CNN-LSTM, and Gradient Boosting model. Interpretable analysis from the perspective of the model, the influence of each feature on the model prediction and the specific decision-making process of the model are obtained, which significantly improves the interpretability of the model, in which MeanHR has a negative correlation with the fatigue state, and MedianNN and LF have a positive correlation with the fatigue state. It can be seen that the proposed SSA-RFC-SHAP driving fatigue detection model can provide scientific guidance for driving fatigue warning.