A Strategy for Integrated Multi-Demands High-Performance Motion Planning Based on Nonlinear MPC

Abstract

Nonlinear Model Predictive Control (NMPC) is an effective approach for motion planning in autonomous vehicles that need to satisfy multiple driving demands. Within the realm of planner design, current strategies inadequately address the issues related to redundancy and conflicts among these diverse demands. This shortcoming leads to low efficiency and suboptimal performance, particularly when faced with a high volume of demands. In response to this challenge, this paper introduces the Hierarchical and Multi-Domain (HMD) strategy as a solution for designing a multi-objective NMPC planner. This strategy enables the dynamic adjustment of the integration method for demand indicators based on their priority. To evaluate the risk of breaching driving demands, several risk functions are established. The constraints and objective function of the planner are meticulously designed in accordance with the HMD strategy and evaluation functions. Simulation results attest to the advantages of the HMD-based planner, which, compared to planners based on traditional multi-objective (TMO) strategies, exhibits a 68.5% improvement in solution efficiency and the simultaneous enhancement of driving safety. Additionally, the HMD approach reduces the maximum jerk by 58.8%.

Keywords

• autonomous vehicle
• multi-objective optimization
• motion planning
• nonlinear model predictive control