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This research aims to improve vehicle state estimation for better trajectory planning and control in autonomous driving.

Robust vehicle state estimation method for synergistic trajectory planning and tracking control

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This research aims to improve vehicle state estimation for better trajectory planning and control in autonomous driving.
Developed a Robust Adaptive Extended Kalman Filter using Limited Memory and Maximum Correntropy.
Utilized Finite Impulse Response structure for real-time tracking with reduced phase lag.
Implemented adaptive update architecture for noise covariance to address model parameter mismatches.
Reduced longitudinal velocity estimation error by approximately 95% compared to traditional filters.
Compressed yaw rate phase lag from 30.5° to 3.8°.
Improved data support for trajectory planning and reduced lower-level control instability.

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Randomized trial shows improved state estimation in autonomous vehicles, suggesting enhanced control stability.

Wu et al. (2026) studied this question.

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