Driving like a human: 3D traffic situation evolution for efficient and comfortable AV decisions

Abstract

This paper proposes a novel decision-making framework for automated vehicles (AVs). The framework integrates traffic situation assessment into the decision process to emulate experienced human drivers. It enables the AV to make forward-looking decisions based on macroscopic traffic flow states, guiding the AV toward regions that are safer, more efficient, and more comfortable. To this end, a three-dimensional (3D) traffic-situation quantification model is developed. The model considers static and dynamic flow complexity, vehicle interaction intensity, and traffic stream motion. A machine-learning-based predictor is then introduced to capture the spatiotemporal evolution of traffic situations. A Level- $K$ game framework is further used for situation-aware interaction modeling and behavior selection. The selected decisions are refined by an optimal-control-based trajectory optimizer with kinematic and collision-avoidance constraints. Simulation results in smooth and congested traffic scenarios show that the proposed method improves efficiency and comfort over human driving while maintaining safety.