Work

Addressing Behavior Model Inaccuracies for Safe Motion Control in Uncertain Dynamic Environments

Year: 2025

Type: article

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 algorithmSIED-MPC, 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 optimalinput gapestimates 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.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2025.3555877

Authors: Minjun Sung, Hunmin Kim, Naira Hovakimyan

Institutions: University of Illinois Urbana-Champaign, University of Illinois System, Mercer University

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Air Force Office of Scientific Research

Awards: Collaborative Research: SLES: Guaranteed Tubes for Safe Learning across Autonomy Architectures, G5323404648, Distributionally Robust Adaptive Control: Enabling Safe and Robust Reinforcement Learning