Development of Behavior-Based Control Systems for Autonomous Vehicle Navigation
Motion planning for autonomous vehicles in urban environments is an important and challenging task due to the vehicle dynamic constraints, involvement of stationary and moving obstacles, other vehicles and unpredictable pedestrian movements. The contribution of this work is to propose a real time motion planning scheme of autonomous vehicles. The proposed algorithm will allow autonomous vehicles to track lane centerline while avoiding collisions with stationary obstacle. A linear model predictive control (MPC) algorithm using Laguerre Functions is developed and used as a typical solution. The controller manipulated variables (outputs) are the acceleration and the front wheels steering angle. Knowledge of the surrounding environment, the state of the autonomous vehicle and other constraints of the MPC are incorporated in the prediction model of the controller to predict safe trajectory for the vehicle and to avoid collision with other obstacles. The presented algorithm will improve autonomous vehicles navigation behavior in structured urban environments.
Authors: Abdalla Al-Salah, Saleh Zein-Sabatto
Published in: World Congress on Industrial Control Systems Security (WCICSS-2020)
- Date of Conference: 8-10 December 2020
- DOI: 10.20533/WCICSS.2020.0002
- ISBN: 978-1-913572-26-6
- Conference Location: London, UK