The traditional driving behavior model framework divides the driving behaviors into car-following and lane-changing, which are modeled separately. While the integrated driving behavior model framework believes that car-following and lane-changing are inseparable, so all driving behaviors are modeled as a whole. Based on these two behavioral model frameworks, this paper analyzed the performance of the data-driven human-like driving models. Firstly, it established integrated driving behavior model framework and car-following lane-changing combined model framework and then determined the input and output of the models according to the influencing factors in driving. Secondly, two combinations of car-following, lane-changing and intention recognition modules were proposed: discriminative combination and probability combination. Subsequently, the processing of the original data were carried out to build integrated driving behavior, car-following, lane-changing, and intention recognition datasets, which were used to train and calibrate the corresponding modules. Finally, the study compared the performance of the two combination models with the integrated driving behavior model in various aspects, including model accuracy, safety, robustness and migration. The results show that, when the model input and output, the parameter calibration process and the dataset are the same, the accuracy of the human-like driving model based on long short-term memory neural network (LSTM) is better than the model based on FNN. The mean square error of the model based on LSTM can reach 0.227 m², and the mean square error of the model based on FNN is 0.470 m². Within the LSTM-based model, the model using the car-following lane-changing combined model framework has better robustness and transferability than the model using the integrated driving behavior model framework. For the car-following lane-changing combined model, the mean square error of ±10% noise robustness can reach 1.383 m², and the mean square error of transferability can reach 0.462 m². For the integrated driving behavior model, the mean square error of ±10% noise robustness is 2.314 m², and the mean square error of transferability is 0.484 m².

The physical condition of drivers is closely related to traffic safety, especially the driver’s cardiovascular health condition. Real-time monitoring of drivers’ health can help drivers understand their physical condition in time and reduce traffic accidents caused by sudden illnesses. In this study, firstly, 657 PPG (Photoplethysmography Signal) pulse wave datasets from Guilin People’s Hospital, Guangxi Zhuang Autonomous Region, China, were dichotomized numerically for cerebrovascular diseases after the noise reduction by Chebyshev Ⅱ filter and the extraction of time domain features, frequency domain features and wavelet packet features by fast Fourier method. Then, the numerically labeled cerebrovascular disease types were used as output parameters to construct driver cerebrovascular disease dataset. To solve the problem of unbalanced classification of samples in actual dataset, an oversampling supplement was performed by the SMOTE algorithm and a driver cerebrovascular disease classification model, namely SSA-DELM, was constructed based on PPG feature values, followed with model training and testing on actual datasets. The results show that the proposed classification model can provide comparatively accurate early warning for drivers suffering from cerebral infarction or cerebrovascular disease, with an accuracy of 83%, an average precision of 80%, a completeness of 76.6%, an F₁ score of 0.79, and a mean average precision of 0.80. This research can provide theoretical model basis and technical support for drivers’ dynamic health monitoring system based on PPG signal. The proposed model has a large application space in the software service and intelligent medical care of the new energy automobile industry, which is in line with the sales mode of the whole industry chain of “terminal + software + service” of new energy automobile enterprises, and is also in line with modern people’s attention to environmental protection, family health and intelligent transportation.