Autonomous driving technologies have been making giant strides to improve the safety of roads and their efficiency. There is a more humane factor that rides on the vehicle besides the reduction of accidents with time and aptitude for the reduction of accidents: adaptability to personalized driving experience, which depends on user acceptance. This paper presents a comprehensive approach to trajectory planning and tracking of autonomous vehicles with human-like driving behaviors. This is achieved based on an integrated framework of the Artificial Potential Field model and Model Predictive Control; consequently, several environmental variables and the manners of various drivers, either conservative or aggressive, are considered. APF only ensures safe and dynamic obstacle avoidance whereas MPC adapts the vehicle control according to the taste and preference of the user in real time. Simulations on car-following and lane-changing scenarios validate the proposed method, which can generate adaptive trajectories close to those produced by humans. The results are that the algorithm will really personalize driving patterns according to drivers and occupants' preferences with respect to increasing user comfort and acceptance without compromising the high standards of safety.

Autonomous Vehicles Trajectory Planning, Tracking , Driving Styles, Model Predictive Control (MPC), Vehicle Control Algorithms, Adaptive Control