|Personalization is the task that aims at improving quality of products and services by adapting itself to the current user. In the context of automotive applications, personalization is not only about how drivers sets up the position of their seat or their favorite radio channels. Going beyond that, personalization is also about the preference of driving styles and the individual behaviors in every maneuver executions. One key challenge in personalization is to be able to capture and understand the users from the historical data produced by the users. The data are usually presented in form of time series and in some cases, those time series can be remarkably long. Capturing and learning from such data poses a challenge for machine learning models. To deal with this problem, this thesis presents an approach that makes uses of recurrent neural networks to capture the time series of behavioral data of drivers and predict theirs lane change intentions. In comparison to previous works, our approach is capable of predicting not only driver's intention as predefined discrete classes (i. e. left, right and lane keeping) but also as continuous values of the time left until the drivers cross the lane markings. This provides additional information for advanced driver-assistance systems to decide when to warn drivers and when to intervene. There are two further aspects that need to be considered when developing a personalized assistance system: inter- and intra-personalization. The former refers to the differences between different users whereas the later indicates the changes in preferences in one user over time (i. e. the differences in driving styles when driving to work versus when being on a city sightseeing tour). In the scope of this thesis, both problems of inter- and intra-personalization are addressed and tackled. Our approach exploits the correlation in driving style between consecutively executed maneuvers to quickly derive the driver's current preferences. The introduced networks architecture outperforms non-personalized approaches in predicting the preference of driver when turning left. To tackle inter-personalization problems, this thesis makes use of the Siamese architecture with long short-term memory networks for identifying drivers based on vehicle dynamic information. The evaluation, which is carried out on real-world data set collected from 32 test drivers, shows that the network is able to identify unseen drivers. Further analysis on the trained network indicates that it identifies drivers by comparing their behaviors, especially the approaching and turning behaviors.