Intuitive decision-making modeling for self-driving vehicles

This paper tries to make self-driving vehicles have human drivers´ common sense and intuitive decision-making ability. Human drivers often make decisions according to not only what they see, but also their predictions based on experiences and reasoning results. We propose a systematical intuitive decision-making for self-driving vehicles. The method combines similarity matching, online learning mechanism and prediction together. Similarity matching can make a decision based on previous learned knowledge, while online learning can enrich the knowledge database, and prediction can make the system have reasoning common sense to produce decisions in unfamiliar and incomplete traffic scenarios. Basically, intuitive decision-making can produce a decision quickly without long-time reasoning computation. A simple test example tested the proposed method.