Flatness-based Model Predictive Control for Autonomous Vehicle Trajectory Tracking

2019 ◽  
Author(s):  
Zejiang Wang ◽  
Jingqiang Zha ◽  
Junmin Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Autonomous Vehicle ◽  
Vehicle Trajectory

Lane-Exchanging Driving Strategy for Autonomous Vehicle via Trajectory Prediction and Model Predictive Control

2020 ◽  
Author(s):  
YIMIN CHEN ◽  
Huilong Yu ◽  
Jinwei Zhang ◽  
Donpu Cao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Potential Field ◽  
Autonomous Vehicle ◽  
Kinematic Model ◽  
Prediction Method ◽  
Gaussian Mixture ◽  
Driving Safety ◽  
Trajectory Prediction ◽  
Vehicle Trajectory

Abstract The cooperation between an autonomous vehicle and a nearby vehicle is critical to ensure driving safety in the lane-exchanging scenario. The nearby vehicle trajectory needs to be predicted, from which the autonomous vehicle is controlled to prevent possible collisions. This paper proposes a lane-exchanging driving strategy for the autonomous vehicle to cooperate with the nearby vehicle by integrating vehicle trajectory prediction and motion control. A trajectory prediction method is developed to anticipate the nearby vehicle trajectory. The Gaussian mixture model (GMM), together with the vehicle kinematic model, are synthesized to predict the nearby vehicle trajectory. A potential-field-based model predictive control (MPC) approach is utilized by the autonomous vehicle to conduct the lane-exchanging maneuver. The potential field of the nearby vehicle is considered in the controller design for collision avoidance. On-road driving data verification shows the nearby vehicle trajectory can be predicted by the proposed method. CarSim simulations validate that the autonomous vehicle can perform the lane-exchanging maneuver and avoid the nearby vehicle using the proposed driving strategy. The autonomous vehicle can thus safely perform the lane-exchanging maneuver and avoid the nearby vehicle.

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