Abstract: To allocate driving privilege in a reasonable way for shared control of intelligent vehicle, an evaluation method was proposed for driving capability in the longitudinal and lateral scenarios, to improve the safety and comfort of intelligent vehicles. Firstly, the driving capability was defined and analyzed and car-following stimulate for longitudinal scenario and moving double lane change stimulate for lateral scenario were designed. Data collection was conducted based on a driver-in-the-loop intelligent simulation platform (DILISP). And then, a driving capability identification model was established based on Hammerstein identification process and a principal component analysis (PCA) was used to decouple and reduce the dimension of the key parameters in Hammerstein model. Finally, a driving capability classification method was carried out based on a combination method of ant clustering algorithm (ACA) and subjective questionnaire. The evaluation equation for driving capability was developed by multiple linear regression(MLR). Results show that the proposed evaluation method for driving capability in the longitudinal and lateral scenarios can achieve accurate and reliable evaluation results. The mean identification accuracy of longitudinal and lateral driving capability identification model and fitting accuracy of evaluation equations can reach 90%.