A MPC Combined Decision Making and Trajectory Planning for Autonomous Vehicle Collision Avoidance
Manel Ammour, Rodolfo Orjuela, Michel Basset
Abstract
Increasing focus is being paid to ensuring safety in autonomous driving. The current paper addresses the challenge of collision avoidance with dynamic surrounding vehicles in different driving situations. The established solution formulated utilizing Model Predictive Control (MPC) includes decision making and trajectory planning. A simplified prediction model is used, which takes into account the relative positions and velocities of the surrounding vehicles and the ego vehicle. Depending on traffic conditions, which are stated as constraints in the MPC formulation, the ego vehicle may perform lane keeping, lane shift, overtaking or braking to avoid collision with the road participants. The decision making constraints are included into the MPC in a mixed integer formulation-like manner. The safety constraints are defined using the Sigmoid function and the braking barrier to define the navigable zone of the ego vehicle. The proposed algorithm has been evaluated through simulation, with different scenarios revealing its effectiveness.