Lateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllers
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2020-10-13
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Multidisciplinary Digital Publishing Institute (MDPI)
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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
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Vehicle-model blending, Trajectory tracking, Model predictive control, Automated driving, Vehicle control
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journal article
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Matute-Peaspan, Jose A., Mauricio Marcano, Sergio Diaz, Asier Zubizarreta, and Joshue Perez. “Lateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllers.” Electronics 9, no. 10 (October 13, 2020): 1674