The evolution of Advanced Driver Assistance Systems (ADAS) has reached a stage of maturity where the social acceptance of these systems is becoming as important as coping with technical challenges. Most users complain that ADAS functions behave unnaturally, and they feel anxious because the driving system behaves in a way that is far from what the user prefers. One of the most controversial ADAS functions is Lane Keeping Assistance (LKA). This system detects the edge of the lane and drives the car actively by intervening on the steering wheel. Most available lane keeping follows the center of the lane, while the vehicle controller oscillates and often deviates from this line. Most people find this operation disturbing and unreliable. In our work, we propose a comprehensive Model Predictive Planner and Control that preserves the basic concept of how human drivers drive: they instinctively create the combined representation of the environment and the vehicle, and predictevely plan their action trajectory (i.e., steering angle). This method does not divide the driving task into planning and control, thereby improving tunability. On the other hand, the method provides direct relation to vehicle dynamics and high-level policies, and therefore it can be validated easier than end-to-end trained functions, such as neural networks. The initial algorithm is implemented in MATLAB and validated via simulation. The test script and the data can be accessed upon request¹¹https://github.com/jkk-research/jkk_controllers/tree/prototype/mppc