Fast Trajectory Planning for Automated Vehicles using Gradient-based Nonlinear Model Predictive Control

TL;DR

This paper introduces a fast, real-time capable nonlinear model predictive control algorithm for trajectory planning in automated vehicles, enabling quick adaptation to dynamic environments.

Contribution

It presents a novel, efficient optimization algorithm for nonlinear vehicle models and a concurrent operation scheme, achieving submillisecond computation times.

Findings

Operates in submillisecond range on standard PC

Handles complex, dynamic driving scenarios

Improves real-time trajectory planning capabilities

Abstract

Motion trajectory planning is one crucial aspect for automated vehicles, as it governs the own future behavior in a dynamically changing environment. A good utilization of a vehicle's characteristics requires the consideration of the nonlinear system dynamics within the optimization problem to be solved. In particular, real-time feasibility is essential for automated driving, in order to account for the fast changing surrounding, e.g. for moving objects. The key contributions of this paper are the presentation of a fast optimization algorithm for trajectory planning including the nonlinear system model. Further, a new concurrent operation scheme for two optimization algorithms is derived and investigated. The proposed algorithm operates in the submillisecond range on a standard PC. As an exemplary scenario, the task of driving along a challenging reference course is demonstrated.