Lateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllers

Abstract

settings Open AccessArticle Lateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllers by Jose A. Matute-Peaspan 1,2,* [OrcID] , Mauricio Marcano 1,2 [OrcID] , Sergio Diaz 1 [OrcID] , Asier Zubizarreta 2 [OrcID] and Joshue Perez 1 [OrcID] 1 TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain 2 Department of Automatic Control and Systems Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain * Author to whom correspondence should be addressed. Electronics 2020, 9(10), 1674; https://doi.org/10.3390/electronics9101674 Received: 4 September 2020 / Revised: 26 September 2020 / Accepted: 8 October 2020 / Published: 13 October 2020 (This article belongs to the Special Issue Autonomous Vehicles Technology) Download PDF Browse Figures Review Reports Abstract Model-based trajectory tracking has become a widely used technique for automated driving system applications. A critical design decision is the proper selection of a vehicle model that achieves the best trade-off between real-time capability and robustness. Blending different types of vehicle models is a recent practice to increase the operating range of model-based trajectory tracking control applications. However, current approaches focus on the use of longitudinal speed as the blending parameter, with a formal procedure to tune and select its parameters still lacking. This work presents a novel approach based on lateral accelerations, along with a formal procedure and criteria to tune and select blending parameters, for its use on model-based predictive controllers for autonomous driving. An electric passenger bus traveling at different speeds over urban routes is proposed as a case study. Results demonstrate that the lateral acceleration, which is proportional to the lateral forces that differentiate kinematic and dynamic models, is a more appropriate model-switching enabler than the currently used longitudinal velocity. Moreover, the advanced procedure to define blending parameters is shown to be effective. Finally, a smooth blending method offers better tracking results versus sudden model switching ones and non-blending techniques.