An interpretable decision-making model for autonomous driving

Abstract

Modeling the interactive behavior of human drivers is essential for achieving safe and fully autonomous vehicles. Unfortunately, most decision-making systems employed in current autonomous vehicles rely on complex deep neural network models that function as black boxes with opaque reasoning that hampers human interpretation. Drawing upon the needs theories endorsed by psychologists and driving-related psychological research, we summarize five fundamental driving needs underlying the driver’s behavior: safety, dominance, achievement, order, and relatedness. Leveraging the behavior selection module from general cognitive architectures, we propose a decision-making model explicitly tailored for autonomous vehicles, comprising three distinct modules: needs assessment, motivation generation, and behavior selection. We conducted experiments to evaluate the proposed model using a self-developed 2D simulator based on Unity. The results intuitively visualized the motivation and behavior of self-driving vehicles. This model demonstrates remarkable proficiency in handling routine tasks, such as independent and complete driving tasks, intersection navigation, and maneuvering among multiple vehicles.