The control of an autonomous host vehicle at a crossroads intersection, in the presence of uncoordinated target vehicles, and without any crossing priority regulation is considered. The problem was spilt into two sub-problems, namely the priority and the path-planning problems. These problems are solved using a hierarchical controller. The lower control level is a linear controller to control the vehicle's speed and heading, to follow the reference signals provided by a middle-level algorithm. This middle-level controller is a Model Predictive Control (MPC) computed by modelling a unicycle model that is in a Linear Parameter-Varying (LPV) state-space model form. Different features are introduced for improving the prediction capability of the LPV-MPC. Prediction data computed by the MPC are used by the higher-level state-machine supervisor algorithm to determine when the host vehicle can safely cross the junction. The hierarchical controller was tested in simulation using a set of stressing scenarios. Reported results show the effectiveness of the proposed LPV-MPC in managing complex traffic scenarios with efficient compute.
LPV-MPC Path Planning for Autonomous Vehicles in Road Junction Scenarios / Cavanini, Luca; Majecki, Pawel; Grimble, Mike J.; Ivanovic, Vladimir; Tseng, H. Eric. - (2021), pp. 386-393. [10.1109/ITSC48978.2021.9564942]
LPV-MPC Path Planning for Autonomous Vehicles in Road Junction Scenarios
Cavanini, Luca;
2021-01-01
Abstract
The control of an autonomous host vehicle at a crossroads intersection, in the presence of uncoordinated target vehicles, and without any crossing priority regulation is considered. The problem was spilt into two sub-problems, namely the priority and the path-planning problems. These problems are solved using a hierarchical controller. The lower control level is a linear controller to control the vehicle's speed and heading, to follow the reference signals provided by a middle-level algorithm. This middle-level controller is a Model Predictive Control (MPC) computed by modelling a unicycle model that is in a Linear Parameter-Varying (LPV) state-space model form. Different features are introduced for improving the prediction capability of the LPV-MPC. Prediction data computed by the MPC are used by the higher-level state-machine supervisor algorithm to determine when the host vehicle can safely cross the junction. The hierarchical controller was tested in simulation using a set of stressing scenarios. Reported results show the effectiveness of the proposed LPV-MPC in managing complex traffic scenarios with efficient compute.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.