Real-Time NMPC for Semi-Automated Highway Driving of Long Heavy Vehicle Combinations

Abstract

This work presents a Nonlinear Model Predictive Control (NMPC) approach to realtime trajectory generation for highway driving with long truck combinations. In order to consider all relevant information for the road and the surrounding traffic, we formulate a fininite-horizon optimal control problem (OCP) that incorporates a prediction model in spatial coordinates for the vehicle and the surrounding traffic. This allows road properties (such as curvature) to appear as known variables in the prediction horizon. The objective function of the resulting constrained nonlinear least-squares problem provides a trade-off between tracking performance, driver comfort, and keeping a comfortable distance from fellow road users. The OCP is solved with a direct multiple shooting solution method implemented in a real-time iteration scheme using ACADO code generation and compared with a feedback scheme that solves the entire nonlinear program in each time step with the interior point solver IPOPT. To illustrate the efficacy and the real-time implementability of the methodology, simulation results are presented for a high way merging scenario of a long heavy vehicle combination.