Dynamic Trajectory Planning and Tracking for Autonomous Vehicle with Obstacle Avoidance Based on Model Predictive Control

• Published in: IEEE access Vol. 7; p. 1
• Main Authors: Li, Shaosong, Li, Zheng, Yu, Zhixin, Zhang, Bangcheng, Zhang, Niaona
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Autonomous navigation; Autonomous vehicle; Autonomous vehicles; Collision avoidance; Collision dynamics; Control systems design; Controllers; Cost function; Dynamic obstacles; Dynamic planning and tracking; Dynamic stability; Horizon; Lateral stability; Moving trend function; Nonlinear control; Obstacle avoidance; Planning; Predictive control; Single-level controller; Tracking; Trajectory; Trajectory planning; Vehicle dynamics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2940758

In this study, an obstacle avoidance controller based on nonlinear model predictive control is designed in autonomous vehicle navigation. The reference trajectory is predefined using a sigmoid function in accordance with road conditions. When obstacles suddenly appear on a predefined trajectory, the reference trajectory should be adjusted dynamically. For dynamic obstacles, a moving trend function is constructed to predict the obstacle position variances in the predictive horizon. Furthermore, a risk index is constructed and introduced into the cost function to realize collision avoidance by combining the relative position relationship between vehicle and obstacles in the predictive horizon. Meanwhile, lateral acceleration constraint is also considered to ensure vehicle stability. Finally, trajectory dynamic planning and tracking are integrated into a single-level model predictive controller. Simulation tests reveal that the designed controller can ensure real-time trajectory tracking and collision avoidance.