MPC-based control of autonomous vehicles with localized path planning for obstacle avoidance under uncertainties
Department of Mechanical Engineering-Engineering Mechanics
This paper presents a Model Predictive Control (MPC) approach for longitudinal and lateral control of autonomous vehicles with a real-time local path planning algorithm. A heuristic graph search method (A* algorithm) combined with piecewise Bezier curve generation is implemented for obstacle avoidance in autonomous driving applications. Constant time headway control is implemented for a longitudinal motion to track lead vehicles and maintain a constant time gap. MPC is used to control the steering angle and the tractive force of the autonomous vehicle. Furthermore, a new method of developing Advanced Driver Assistance Systems (ADAS) algorithms and vehicle controllers using Model-In-the-Loop (MIL) testing is explored with the use of PreScan®. With PreScan®, various traffic scenarios are modeled and the sensor data are simulated by using physics-based sensor models, which are fed to the controller for data processing and motion planning. Obstacle detection and collision avoidance are demonstrated using the presented MPC controller.
ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
MPC-based control of autonomous vehicles with localized path planning for obstacle avoidance under uncertainties.
ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.
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