不確実な動的環境における安全な運動制御のための行動モデル誤差への対処
Addressing Behavior Model Inaccuracies for Safe Motion Control in Uncertain Dynamic Environments
動的障害物の状態推定と予測の不確かさに対処するため、同時状態・入力推定と分布ロバストMPCをモデル信頼度評価で統合したSIED-MPCを提案し、自動運転シミュレーションで衝突率と計算時間を削減した。
著者: Minjun Sung, Hunmin Kim, Naira Hovakimyan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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. Addressing these challenges is crucial to safely control an autonomous system. In this paper, we propose a novel algorithm SIED-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 optimal input gap estimates to assess the confidence of the behavior model, defining the ambiguity radius for DR-MPC to handle predictive distribution shifts. This systematic confidence evaluation leads to producing safe inputs with an adequate level of conservatism. Our algorithm demonstrated a reduced collision rate in autonomous driving simulations through improved state estimation, with a 54% shorter average computation time.
