Automated highway lane changes of long vehicle combinations: A specific comparison between driver model based control and non-linear model predictive control
Paper in proceedings, 2015

This paper compares the vehicle dynamics performances of two approaches for automated lane change manoeuvres of a long vehicle combination in simulated highway driving. One of the two approaches is a non-linear model predictive controller (NMPC), and the other is based on driver model control (DMC) theory. Both approaches utilize traffic situation predictions that include motion variable constraints and actuation requests for steering, propulsion and braking. The two automated driving approaches are compared in a simulation environment including a high-fidelity vehicle plant model and models of surrounding vehicles. Simulations show that both approaches can generate feasible lane change manoeuvres at the constant speeds of 44 and 78 km/h. In addition, lane changes were successfully conducted in combination with retardation due to leading vehicle braking from 80 to 50 km/h with a varying retardation range of 0.1-0.7 g. In general, the non-linear model predictive control shows a shorter lane change duration and lower values of the used absolute magnitude of the longitudinal and lateral accelerations. However, the specific objective function used in the NMPC leads to an unnecessary variation of longitudinal vehicle speed compared to the driver model control approach.

predictive control

steering systems

vehicle dynamics

nonlinear control systems

road vehicles

Author

Peter Nilsson

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

Leo Laine

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

Niels van Duijkeren

KU Leuven

Bengt J H Jacobson

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), September 2-4, Madrid, Spain

472-479

Areas of Advance

Transport

Subject Categories

Vehicle Engineering

DOI

10.1109/INISTA.2015.7276790

ISBN

978-146739096-5

More information

Latest update

5/29/2018