IFAC PapersOnLine 52-8 (2019) 289–294 ScienceDirect ScienceDirect Available online at www.sciencedirect.com 2405-8963 © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2019.08.085 © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. 1. INTRODUCTION In Automated Driving, the vehicle motion planning is an essential procedure to obtain a safe and comfortable driving experience in automated mode. Researchers have been putting efforts on this area proposing the use of smooth curves for obstacle free trajectories, and speed profiles dependent on curvature to reduce discomfort in bends and lateral maneuvers (Lattarulo et al., 2018). However, comfort should not be considered in the planning stage alone, because it assumes that the controller will follow the references perfectly. Instead, it must be consid- ered together with the control aspect of driving. Although comfort in a dynamic driving task is not formally defined (Bellem et al., 2018), it is related in practice with variables such as jerk and acceleration (Bautista, 2017), which are characteristics of the control stage. Nonetheless, not all control techniques allow to consider these vehicle states in the design process, e.g. classical controllers. In contrast, Model Predictive Control (MPC) technique allows to combine different objectives functions ⋆ This project has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 737469 (AutoDrive Project). This Joint Undertaking receives support from the European Union Horizon 2020 research and innovation programme and Germany, Austria, Spain, Italy, Latvia, Belgium, Netherlands, Sweden, Finland, Lithuania, Czech Republic, Romania, Norway. This work was developed at Tecnalia Research & Innovation facilities supporting this research. that pursue performance, safety and comfort. In (Matute et al., 2018), MPC is implemented as a speed controller with constraints of longitudinal jerk and acceleration, using a triple integrator model. However, it failed on considering the lateral actions of the vehicle, which have a high impact on passenger comfort. Other works (Mata et al., 2017; Kong et al., 2015), include a kinematic bicycle model to couple the lateral and longitudinal control of the vehicle in a single optimization problem. However, when different objectives functions compete (e.g. increase tracking performance, reach a speed set point and reduce steering rate) the optimization problem may be unfeasible, producing unexpected behaviors on the vehicle motion and compromising safety. In this sense, (Polack et al., 2017) states that keeping the lateral acceleration under a threshold of 0.5g guarantees a feasible solution when a kinematic bicycle model is used. Considering this, the novelty of the present work relies on the inclusion of the lateral jerk formula within the MPC formulation. This consideration allows a new state con- straint limiting the lateral acceleration to assure a feasible solution. This also permits the use of simple vehicle model which require less computational effort. Additionally, an adaptive speed weight equation that depends on the lat- eral acceleration is also included to improve the tracking performance of the vehicle. This work is organized as follows. In Section II, the control architecture developed is described in detail. Section III, Keywords: Intelligent Control, Path Planning, Intelligent Transportation Systems. Abstract: Nowadays, Automated Driving has a growing interest in the scientific and industrial automotive community. The vehicle motion planning is an essential procedure to obtain safe and comfortable trajectories, adapting the longitudinal speed to the road legal limits and mainly to avoid the excessive lateral accelerations along the journey. Typically, the proper speed of the vehicle is intrinsically related to the curvature of the path, requiring a previous approximation of this parameter in the planning stage. In this work, a novel procedure to follow a route trajectory and speed limits considering the lateral acceleration parameter is presented. A lateral jerk equation was developed and introduced into a kinematic bicycle model predictive control formulation. An adaptive speed weight equation that depends on the lateral acceleration is presented to improve the lateral positioning. A vehicle motion control simulation, developed in Dynacar, is validated with some real tests. The results show the capabilities of the proposed approach. An accurate vehicle motion control considers the lateral acceleration to avoid unfeasibility in optimization problem. * Tecnalia Research and Innovation, Derio, 48160 Spain (e-mail: joseangel.matute@tecnalia.com). ** University of the Basque Country, Bilbao, 48013 Spain (e-mail: author@ehu.eus) Jose A. Matute *,** Mauricio Marcano *,** Sergio Diaz * Joshue Perez * Experimental Validation of a Kinematic Bicycle Model Predictive Control with Lateral Acceleration Consideration ⋆