TY - JOUR
AU1 - Matute-Peaspan, Jose  A.
AU2 - Marcano, Mauricio
AU3 - Diaz, Sergio
AU4 - Zubizarreta, Asier
AU5 - Perez, Joshue
AB - 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.
TI - Lateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllers
JF - Electronics
DO - 10.3390/electronics9101674
DA - 2020-10-13
UR - https://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/lateral-acceleration-based-vehicle-models-blending-for-automated-Y0LoaVN89B
SP - 1674
VL - 9
IS - 10
DP - DeepDyve
ER -