In the recent years, the interaction between human driver and Advanced Driver Assistance System (ADAS) has gradually aroused people’s concern. As a result, the concept of personalized ADAS is being put forward. As an important system of ADAS, Lane Keeping Assistance System (LKAS) also attracts great attention. To achieve personalized LKAS, driver lane keeping characteristic (DLKC) indices which could distinguish different driver lane keeping behavior should be researched. However, there are few researches on DLKC indices for personalized LKAS. Although there are many researches on modeling driver steering behavior, these researches are not sufficient to obtain DLKC indices. One reason is that most of researches are for double lane change behavior which is different from driver lane keeping behavior. The other reason is that the researches on driver lane keeping behavior only provide model structure and rarely discuss identification procedure such as how to select suitable data. Besides, these researches ignore the relationship between driver behavior and the design of personalized LKAS. In this paper, DLKC indices for personalized LKAS are comprehensively researched. Firstly, DLKC indices are analyzed and determined based on driver lane keeping process and LKAS working process. DLKC indices consist of the following three parts: steering return timing, steering return process, and steering return ending. Secondly, lane keeping experiments are conducted to acquire driver lane keeping data based on virtual Electric Power Steering (EPS) platform. Thirdly, DLKC indices are identified based on statistical method and driver steering model. With statistical method, steering return timing and steering return ending indices are obtained. With driver steering model, steering return process indices are obtained. In the end, the values of DLKC indices are verified and the results show that they are in accordance with driver lane keeping behavior.