Decision-Making Model for Dynamic Scenario Vehicles in Autonomous Driving Simulations

Abstract

A scenario vehicle in autonomous driving simulations is a dynamic entity that is expected to perform trustworthy bidirectional interaction tasks with the autonomous vehicle under test. Modeling interactive behavior can not only facilitate better prediction of human drivers’ intentions and motions but also be valuable in generating more human-like decisions and trajectories for autonomous vehicle testing. However, simulations of most of the available scenario vehicles on existing platforms behave conservatively. This study summarizes five driving motivations based on human-need theories of multiple psychologists, namely safety, dominance, achievement, order, and relatedness, and organizes the framework using a behavior tree. The proposed model generates different driving behaviors by simulating the changing psychological needs of human drivers during vehicle operation. Using a self-developed two-dimensional simulator, experiments were conducted by considering multiple scenarios in urban, rural, and highway road sections. The obtained results indicate that the scenario vehicles controlled by the proposed model exhibit a significant interactive nature, facilitating proactive communication rather than providing simple responses.