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

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. This research was funded by AUTODRIVE within the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL JU) in collaboration with the European Union’s H2020 Framework Program (H2020/2014-2020) and National Authorities, under Grant No. 737469.