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

• Main Authors: Jose A. Matute-Peaspan, Mauricio Marcano, Sergio Diaz, Asier Zubizarreta, Joshue Perez
• Format: Article
• Language: English
• Published: MDPI AG 2020-10-01
• Series: Electronics
• Subjects: vehicle-model blending; trajectory tracking; model predictive control; automated driving; vehicle control
• Online Access: https://www.mdpi.com/2079-9292/9/10/1674

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.