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Trajectory Following using Nonlinear Model Predictive Control and 3D Point-Cloud-based Localization for Autonomous Driving

Ajish Babu; Kerim Yener Yurtdas; Christian Ernst Siegfried Koch; Mehmed Yüksel
In: 2019 European Conference on Mobile Robots (ECMR). European Conference on Mobile Robots (ECMR-2019), September 4-6, Prag, Czech Republic, IEEE, 9/2019.


In autonomous driving, the trajectory follower is one of the critical controllers which should be capable of handling different driving scenarios. Most of the existing controllers are limited to a particular driving scenario and for a specific vehicle model. In this work, the trajectory follower is formulated as a nonlinear model predictive control problem and solved using the multiple-shooting trajectory optimization method, Gauss-Newton Multiple Shooting. This solver has already been used for other control applications and provides the flexibility to use different nonlinear models. The controller is tested using a retrofitted autonomous driving platform, along with the 3D point-cloud-based mapping and localization algorithms. The nonlinear model being used is a classical kinematic bicycle model. Due to the high nonlinearity between the vehicle inputs, throttle and brake, and the acceleration, the longitudinal speed control uses an additional piece-wise linear mapping. The results from the initial tests, while following a predefined trajectory on a Go-Kart test-track, are evaluated and presented here.