Advanced Trajectory Planning and Control for Autonomous Vehicles with Quintic Polynomials.

Area of Science:

• Robotics and Autonomous Systems
• Control Engineering
• Computational Geometry

Background:

• Intelligent vehicles require precise trajectory planning and control for safe and efficient operation.
• Ensuring smooth trajectory continuity and accurate state tracking is crucial for autonomous navigation.
• Existing methods may face challenges in handling dynamic environments and complex maneuvers.

Purpose of the Study:

• To design and implement a robust trajectory generation and control system for intelligent vehicles.
• To ensure smooth continuity of vehicle state curves (position, velocity, acceleration) during trajectory execution.
• To validate the proposed control framework through experimental testing on a physical platform.

Main Methods:

• Utilized quintic polynomials for generating smooth and continuous vehicle trajectories.
• Developed lateral and longitudinal controllers based on a two-degree-of-freedom dynamic model.
• Employed the Frenet coordinate system transformation for controller design and error modeling.
• Conducted experimental verification using a small-scale intelligent vehicle with Ackermann steering.

Main Results:

• The quintic polynomial method successfully generated smooth and continuous trajectories.
• The developed controllers effectively regulated vehicle tracking performance.
• Experimental results confirmed the controllers' efficacy under various operating conditions.
• The study demonstrated the feasibility of the integrated trajectory and control framework.

Conclusions:

• The proposed quintic polynomial-based trajectory planning and control system is effective for intelligent vehicles.
• The method ensures smooth state transitions and accurate path following.
• Experimental validation confirms the practical applicability and robustness of the approach.