Addressing Behavior Model Inaccuracies for Safe Motion Control in Uncertain Dynamic Environments
Minjun Sung, Hunmin Kim, Naira Hovakimyan
Abstract
Uncertainties in the environment and behavior model inaccuracies compromise the state estimation of a dynamic obstacle and its trajectory predictions, introducing biases in estimation and shifts in predictive distributions. In this letter, we propose a novel algorithm <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIED-MPC</monospace>, which synergistically integrates Simultaneous State and Input Estimation (SSIE) and Distributionally Robust Model Predictive Control (DR-MPC) using model confidence evaluation. The SSIE process produces unbiased state estimates and optimal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">input gap</i> estimates to assess the confidence of the behavior model, defining the ambiguity radius for DR-MPC to handle predictive distribution shifts. The proposed method produces safe inputs with an adequate level of conservatism. Our algorithm demonstrated a reduced collision rate and computation time in autonomous driving simulations through improved state estimation.
BibTeX
@inproceedings{ral2025_addressingbehavi,
title = {Addressing Behavior Model Inaccuracies for Safe Motion Control in Uncertain Dynamic Environments},
author = {Minjun Sung and Hunmin Kim and Naira Hovakimyan},
booktitle = {RA-L 2025},
year = {2025}
}