NMPC trajectory planner for urban autonomous driving

This paper presents a trajectory planner for autonomous driving based on a Nonlinear Model Predictive Control (NMPC) algorithm that accounts for Pacejka's nonlinear lateral tyre dynamics as well as for zero speed conditions through a novel slip angle calculation. In the NMPC framework, road boundaries and obstacles (both static and moving) are taken into account with soft and hard constraints implementation. The numerical solution of the NMPC problem is carried out using ACADO toolkit coupled with the quadratic programming solver qpOASES. The effectiveness of the proposed NMPC trajectory planner has been tested using CarMaker multibody models. The formulation of vehicle, road and obstacles' models has been specifically tailored to obtain a continuous and differentiable optimisation problem. This allows to achieve a computationally efficient implementation by exploiting automatic differentiation. Moreover, robustness is improved by means of a parallelised implementation of multiple instances of the planning algorithm with different spatial horizon lengths. Time analysis and performance results obtained in closed-loop simulations show that the proposed algorithm can be implemented within a real-time control framework of an autonomous vehicle.

Automated summary

This paper presents a trajectory planner for autonomous driving based on a Nonlinear Model Predictive Control (NMPC) algorithm. The proposed algorithm considers Pacejka's nonlinear lateral tyre dynamics and zero speed conditions through a novel slip angle calculation. Road boundaries and obstacles are taken into account with soft and hard constraints implementation. The numerical solution of the NMPC problem is carried out using ACADO toolkit coupled with the quadratic programming solver qpOASES.