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

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 techniquesThis 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. 73746

A study of a comfortable vehicle motion predictive control with no speed limit reference

Publisher Copyright: © 2019 IEEE.In the last decade great advances have been achieved in the development of reliable Advanced Driver Assistance Systems. The feeling of safety in automated vehicles performing trajectory tracking in urban environments has become an interesting field of study, where several strategies have been proposed. However, most of the current approaches employ speed limiters for the longitudinal control of automated vehicles to avoid discomfort due to excessive lateral accelerations in paths with high curvatures. Therefore, smoothness of the path must be evaluated previously in a planning stage before the trajectory tracking task. In this work, a comparative study is carried out with different comfortable predictive controllers based on kinematic model approaches. Moreover, the novelty of including the lateral acceleration as an additional state parameter into the tracking stage to avoid a previous speed limit calculation is evaluated. A comparison of the strategies is accomplished using a simulated test vehicle within a realistic environment developed in Dynacar. For that purpose, the control architecture for the automated driving problem is exhaustively explained and low-level control disturbances are considered and modeled to scale into a future real implementation of the vehicle motion control strategies. The performed tests demonstrate effectiveness of the proposed approach.ACKNOWLEDGMENT This project has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 783190 (PRYSTINE Project) and the National HAZITEK 2017 project HI-ADVICE: HIghway ADVanced CruIse AssistanCE. This work was developed at Tecnalia Research & Innovation facilities supporting this work.Peer reviewe