NMPC trajectory planner for urban autonomous driving

TL;DR

This paper introduces a real-time capable NMPC trajectory planner for urban autonomous driving that incorporates nonlinear tire dynamics, obstacle avoidance, and static and moving obstacle constraints, validated through CarMaker simulations.

Contribution

It presents a novel NMPC trajectory planning method that accounts for complex tire dynamics and obstacle constraints, suitable for real-time autonomous vehicle control.

Findings

Effective obstacle avoidance in simulation

Real-time implementation feasibility demonstrated

Incorporates nonlinear tire dynamics for accuracy

Abstract

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.