Scenario vehicles are an important part of the dynamic environment utilized in autonomousdriving simulations. They are required to meet the demands of traffic-scenario diversity and form a larger coverage scale in the road network. However, the current motion planning of scenario vehicles either adheres to the classical microscopic traffic-flow model or follows a predefined path; thus, interacting with the vehicle under test in a dynamic bidirectional fashion is difficult. This study researches a motion-planning method for a broader category of unmanned vehicles and proposes a motion-planning method for scenario vehicles based on Pontryagin's minimal principle, used in optimal control theory and the closed-form solution of the minimum snap method. The study reclassifies actions received from the behavior layer according to the boundary conditions and final times and derives an analytical solution for each of them. The analytical solution is then experimentally verified. The proposed method not only accomplishes efficient motion planning but also exhibits variant driving styles, which provides a practical solution for the motion planning of scenario vehicles in simulations.

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.