Obstacle Avoidance using Model Predictive Control: A Detailed Analysis using Scaled Vehicles 2020-01-0109
Over the last decade, tremendous amount of research and progress has been made towards developing smart technologies for autonomous vehicles such as adaptive cruise control, lane keeping assist, lane following algorithms, decision making algorithms for lane changing, adaptive control etc. One of the fundamental objectives for the development of such technologies is to enable autonomous vehicles with the capability to avoid obstacles and maintain safety.
Automobiles are intricate systems and increasing autonomy in vehicles increases their complexity by several folds; especially since the dynamics of the vehicle needs to be considered. Model predictive control is a powerful tool that is used extensively to control the behavior of complex, dynamic systems. As a model-based approach, the fidelity of the model and selection of model-parameters plays a role in ultimate performance. In this paper, we use model predictive control to comparatively study controller performance for obstacle avoidance strategy using scaled-vehicles (1/10th scale). The assessment is conducted initially in simulation and planned to be evaluated in a hardware-in-loop framework.