Expected yaw rate-based trajectory tracking control with vision delay for intelligent vehicle.

Area of Science:

• Intelligent Transportation Systems
• Control Engineering
• Robotics

Background:

• Real-time preview point positioning is critical for intelligent vehicle trajectory tracking.
• Delays in road automatic identification systems (RAIS) degrade control performance and vehicle stability.

Purpose of the Study:

• To propose a compensator for RAIS delay to enhance trajectory tracking control.
• To evaluate the impact of RAIS delay on intelligent vehicle performance.

Main Methods:

• Developed a compensator combining a statistical model and adaptive Kalman predictor for preview point state estimation.
• Implemented a sliding mode controller using a 2-DOF vehicle dynamic model in MATLAB/Simulink and CarSim.
• Analyzed trajectory tracking performance under simulated delays of 20-100 ms.

Main Results:

• RAIS delay significantly reduces intelligent vehicle tracking accuracy.
• Excessive delay deteriorates vehicle stability and safety.
• The proposed statistical-adaptive Kalman predictor compensator effectively mitigates delay effects across various delay intervals.

Conclusions:

• The developed compensator is effective in improving the trajectory tracking performance of intelligent vehicles.
• Addressing RAIS delay is essential for enhancing the stability and safety of autonomous driving systems.