Search

International audience; Autonomous driving is achieved by controlling the coupled nonlinear longitudinal and lateral vehicle dynamics.Longitudinal control greatly affects lateral dynamics and must preserve lateral stability conditions, while lateral controllers must take into account actuator limits and ride comfort. This work deals with the coordinated longitudinal and lateral control for autonomous driving. An improved particle swarm optimized PID (PSO-PID) is proposed to handle the task of speed tracking based on nonlinear longitudinal dynamics. An enhanced linear parameter varying model predictive controller (LPV-MPC) is also designed to control lateral dynamics, the latter is formulated with an adaptive LPV model in which the tire cornering stiffness coefficients are estimated by a recursive estimator. The proposed LPV-MPC is enhanced with an improved cost function to provide better performance and stability. Matlab/Carsim cosimulations are carried out to validate the proposed controllers.

Autonomous vehicles are complex systems that operate in dynamic environments, where automated driving seeks to control the coupled longitudinal and lateral vehicle dynamics to follow a certain behaviour. Model predictive control is one of the most promising tools for this type of application due to its optimal performance and ability to handle input and output constraints. This paper addresses autonomous driving by introducing an adaptive linear parameter varying model predictive controller (LPV-MPC), whose prediction model is adapted online by a neural network. Moreover, the controller’s cost function is optimized by an improved Genetic Algorithm. The proposed controller is evaluated on a challenging track subject to variable wind disturbances.

International audience; The purpose of this paper is to develop an automated tuning procedure for autonomous vehicle lateral control. A low effort and high level method of automated Model Predictive Control tuning based on Bayesian Optimization is proposed. Except from reducing the workload and making the process less tedious, the method yields optimal gains in a sense defined by a user. The solution is implemented and verified in simulation on a driving scenario. The vehicle is able to perform lane keeping maneuvers under varying vehicle velocity.