AutoCoach is an intelligent agent intended for improving automobile drivers' performance by applying persuasive technology. System models like Advanced driver-assistance (ADAS) and some Usage-based-Insurance (UBI) share an aim to increase car and road safety. However, most prior models do not consider the differences between driving habits. The AutoCoach design includes two unique components to build an effective persuasive system. The first component is the personality classification, which recognizes drivers' personalities by analyzing driving behavior patterns. The second component is the rewarding system, which determines the current driving behavior's risk score based on some immediate past behavior. We propose the idea of memory factor, which decides when to provide feedback to drivers based on their personality. This memory factor identifies the most critical behaviors within a flexible time-period. AutoCoach then decides on feedback to maintain safe driving or improve the level of awareness for risky driving habits.