Motion Planning for Dynamic Scenario Vehicles in Autonomous-Driving Simulations

Abstract

Scenario vehicles are an important part of the dynamic environment utilized in autonomous-driving 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.