NMPC trajectory planner for urban autonomous driving
This addresses the problem of safe and efficient trajectory planning for autonomous vehicles in urban environments, representing an incremental improvement with specific technical enhancements.
The paper tackled trajectory planning for urban autonomous driving by developing a Nonlinear Model Predictive Control (NMPC) algorithm that incorporates nonlinear tyre dynamics and handles zero speed conditions, with simulations showing it can be implemented in real-time on an autonomous vehicle.
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 angles calculation. In the NMPC framework, road boundaries and obstacles (both static and moving) are taken into account thanks to 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. Time analysis results provided by the simulations shown, state that the proposed algorithm can be implemented on the real-time control framework of an autonomous vehicle under the assumption of data coming from an upstream estimation block.