To evaluate the performance of existing car-following models applied to Chinese drivers, five representative car-following models were calibrated and validated with read-world driving data. From the driving data collected in the Shanghai Naturalistic Driving Study, 2,100 urban-expressway car-following periods extracted. Using a 5-fold cross validation technique, eighty percent of the car-following periods were randomly selected as calibration dataset, and the remaining 20% as validation dataset. Based on the calibration dataset, values of parameters were calibrated using genetic algorithm for Gaxis-Herman-Rothery, Gipps, intelligent driver, full velocity difference, and Wiedemann models. The performances of these models on predicting inter-vehicle spacing were then validated with validation datasets. The results showed that 1) the full velocity difference (FVD) model had the lowest error term on validation dataset (21%), and the smallest standard deviation of errors; 2) compared to the Wiedemann model used by VISSIM®, the FVD model is more easily calibrated and demonstrates a higher and more robust performance, justify its suitability to be applied for microscopic traffic simulations in China. These results would be valuable for developing intelligent vehicles and microscopic traffic simulation tools tailored to the characteristics of Chinese drivers as well as road and traffic environment in China.