This study introduces a Preference-Based Reinforcement Learning (PbRL) approach tailored for autonomous vehicle (AV) applications within a simulated environment. Traditional RL methods often struggle with the complexities of reward function engineering, failing to perform behaviors of human desire. The proposed framework integrates human preferences directly into the training loop, our framework offers a novel methodology for enhancing the decision-making processes of autonomous system. Our results demonstrate that PbRL can refine the strategies of AVs to align more closely with human-like decision-making, highlighting the potential for increased adaptability and safety in autonomous technologies.