Robust exteroceptive multi-unit state estimation

Purpose and goal

This project aims to enable safe, efficient, and sustainable operation of long and heavy vehicle combinations. It will develop robust state-estimation frameworks that account for model uncertainties, sensor imperfections, and heterogeneous system architectures. By integrating exteroceptive sensors (radar, lidar, cameras) with proprioceptive sensors (wheel-speed sensors and IMUs), the project seeks to improve vehicle state awareness and operational reliability.

Expected effects and result

Long vehicle combinations exhibit highly coupled and nonlinear dynamics, making accurate knowledge of the full vehicle state essential for safe and efficient operation. This project will deliver robust state-estimation methods for multi-unit vehicle combinations, improving vehicle motion control, stability, and energy efficiency. The outcomes will support the safe deployment of automated freight transport systems and contribute to more sustainable and efficient logistics.

Planned approach and implementation

This project will develop state-estimation methods with well-calibrated uncertainty quantification to ensure reliable state inference. A key focus is the systematic fusion of model-based estimation with exteroceptive sensing modalities, including radar, lidar, and cameras, to improve estimation accuracy and robustness. The developed methods will be validated through both simulation studies and real-world experimental testing.