Three-dimensional simulation for training autonomous vehicles in smart city environments

This paper proposes a photorealistic 3D city simulation method for training autonomous vehicles. The proposed method incorporates human simulation, animal simulation, vehicle simulation, and traffic light simulation. To generate natural actions for humans and animals, a motivation-based approach is first applied; then the Q-Network is used to select optimal goals depending on the motivations, and action plans are made based on a hierarchical task network. For vehicles, affinity propagation, data augmentation, and convolutional neural network are employed to generate driver driving data for realistic vehicle movement simulation. A traffic light system is also implemented based on rules derived from real-life observation. The results of experiments in which a virtual city was created demonstrate that the proposed method can simulate city environments naturally. The proposed method can be applied to various smart city applications, such as autonomous vehicle training systems.