In the age of intelligence, humans pursue a safe and convenient mobility experience. Intelligent vehicles have integrated various machine intelligence to support humans in making decisions on mobility. However, the support fails to meet human expectations because machine intelligence lacks a method to communicate with humans—verify the understanding of human needs, and explain how machine intelligence works. In this study, we address this issue through vehicular visualizations. Specifically, we summarize the decision-making requirements of humans, introduce how can techniques of vehicular visualizations satisfy these needs, describe prospective application scenes, and discuss future directions of vehicular visualizations to inspire related scholars or developers.